From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.

PubMed

Truccolo, Wilson

2016-11-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. Published by Elsevier Ltd.

Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia*

PubMed Central

Kim, Sung-Phil; Simeral, John D; Hochberg, Leigh R; Donoghue, John P; Black, Michael J

2010-01-01

Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. PMID:19015583

Neuronal ensemble control of prosthetic devices by a human with tetraplegia

NASA Astrophysics Data System (ADS)

Hochberg, Leigh R.; Serruya, Mijail D.; Friehs, Gerhard M.; Mukand, Jon A.; Saleh, Maryam; Caplan, Abraham H.; Branner, Almut; Chen, David; Penn, Richard D.; Donoghue, John P.

2006-07-01

Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a `neural cursor' with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis.

Decoding complete reach and grasp actions from local primary motor cortex populations.

PubMed

Vargas-Irwin, Carlos E; Shakhnarovich, Gregory; Yadollahpour, Payman; Mislow, John M K; Black, Michael J; Donoghue, John P

2010-07-21

How the activity of populations of cortical neurons generates coordinated multijoint actions of the arm, wrist, and hand is poorly understood. This study combined multielectrode recording techniques with full arm motion capture to relate neural activity in primary motor cortex (M1) of macaques (Macaca mulatta) to arm, wrist, and hand postures during movement. We find that the firing rate of individual M1 neurons is typically modulated by the kinematics of multiple joints and that small, local ensembles of M1 neurons contain sufficient information to reconstruct 25 measured joint angles (representing an estimated 10 functionally independent degrees of freedom). Beyond showing that the spiking patterns of local M1 ensembles represent a rich set of naturalistic movements involving the entire upper limb, the results also suggest that achieving high-dimensional reach and grasp actions with neuroprosthetic devices may be possible using small intracortical arrays like those already being tested in human pilot clinical trials.

Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials.

PubMed

Bansal, Arjun K; Truccolo, Wilson; Vargas-Irwin, Carlos E; Donoghue, John P

2012-03-01

Neural activity in motor cortex during reach and grasp movements shows modulations in a broad range of signals from single-neuron spiking activity (SA) to various frequency bands in broadband local field potentials (LFPs). In particular, spatiotemporal patterns in multiband LFPs are thought to reflect dendritic integration of local and interareal synaptic inputs, attentional and preparatory processes, and multiunit activity (MUA) related to movement representation in the local motor area. Nevertheless, the relationship between multiband LFPs and SA, and their relationship to movement parameters and their relative value as brain-computer interface (BCI) control signals, remain poorly understood. Also, although this broad range of signals may provide complementary information channels in primary (MI) and ventral premotor (PMv) areas, areal differences in information have not been systematically examined. Here, for the first time, the amount of information in SA and multiband LFPs was compared for MI and PMv by recording from dual 96-multielectrode arrays while monkeys made naturalistic reach and grasp actions. Information was assessed as decoding accuracy for 3D arm end point and grip aperture kinematics based on SA or LFPs in MI and PMv, or combinations of signal types across areas. In contrast with previous studies with ≤16 simultaneous electrodes, here ensembles of >16 units (on average) carried more information than multiband, multichannel LFPs. Furthermore, reach and grasp information added by various LFP frequency bands was not independent from that in SA ensembles but rather typically less than and primarily contained within the latter. Notably, MI and PMv did not show a particular bias toward reach or grasp for this task or for a broad range of signal types. For BCIs, our results indicate that neuronal ensemble spiking is the preferred signal for decoding, while LFPs and combined signals from PMv and MI can add robustness to BCI control.

Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials

PubMed Central

Truccolo, Wilson; Vargas-Irwin, Carlos E.; Donoghue, John P.

2012-01-01

Neural activity in motor cortex during reach and grasp movements shows modulations in a broad range of signals from single-neuron spiking activity (SA) to various frequency bands in broadband local field potentials (LFPs). In particular, spatiotemporal patterns in multiband LFPs are thought to reflect dendritic integration of local and interareal synaptic inputs, attentional and preparatory processes, and multiunit activity (MUA) related to movement representation in the local motor area. Nevertheless, the relationship between multiband LFPs and SA, and their relationship to movement parameters and their relative value as brain-computer interface (BCI) control signals, remain poorly understood. Also, although this broad range of signals may provide complementary information channels in primary (MI) and ventral premotor (PMv) areas, areal differences in information have not been systematically examined. Here, for the first time, the amount of information in SA and multiband LFPs was compared for MI and PMv by recording from dual 96-multielectrode arrays while monkeys made naturalistic reach and grasp actions. Information was assessed as decoding accuracy for 3D arm end point and grip aperture kinematics based on SA or LFPs in MI and PMv, or combinations of signal types across areas. In contrast with previous studies with ≤16 simultaneous electrodes, here ensembles of >16 units (on average) carried more information than multiband, multichannel LFPs. Furthermore, reach and grasp information added by various LFP frequency bands was not independent from that in SA ensembles but rather typically less than and primarily contained within the latter. Notably, MI and PMv did not show a particular bias toward reach or grasp for this task or for a broad range of signal types. For BCIs, our results indicate that neuronal ensemble spiking is the preferred signal for decoding, while LFPs and combined signals from PMv and MI can add robustness to BCI control. PMID:22157115

Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

NASA Astrophysics Data System (ADS)

Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Black, Michael J.

2008-12-01

Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. Disclosure. JPD is the Chief Scientific Officer and a director of Cyberkinetics Neurotechnology Systems (CYKN); he holds stock and receives compensation. JDS has been a consultant for CYKN. LRH receives clinical trial support from CYKN.

Decoding bipedal locomotion from the rat sensorimotor cortex.

PubMed

Rigosa, J; Panarese, A; Dominici, N; Friedli, L; van den Brand, R; Carpaneto, J; DiGiovanna, J; Courtine, G; Micera, S

2015-10-01

Decoding forelimb movements from the firing activity of cortical neurons has been interfaced with robotic and prosthetic systems to replace lost upper limb functions in humans. Despite the potential of this approach to improve locomotion and facilitate gait rehabilitation, decoding lower limb movement from the motor cortex has received comparatively little attention. Here, we performed experiments to identify the type and amount of information that can be decoded from neuronal ensemble activity in the hindlimb area of the rat motor cortex during bipedal locomotor tasks. Rats were trained to stand, step on a treadmill, walk overground and climb staircases in a bipedal posture. To impose this gait, the rats were secured in a robotic interface that provided support against the direction of gravity and in the mediolateral direction, but behaved transparently in the forward direction. After completion of training, rats were chronically implanted with a micro-wire array spanning the left hindlimb motor cortex to record single and multi-unit activity, and bipolar electrodes into 10 muscles of the right hindlimb to monitor electromyographic signals. Whole-body kinematics, muscle activity, and neural signals were simultaneously recorded during execution of the trained tasks over multiple days of testing. Hindlimb kinematics, muscle activity, gait phases, and locomotor tasks were decoded using offline classification algorithms. We found that the stance and swing phases of gait and the locomotor tasks were detected with accuracies as robust as 90% in all rats. Decoded hindlimb kinematics and muscle activity exhibited a larger variability across rats and tasks. Our study shows that the rodent motor cortex contains useful information for lower limb neuroprosthetic development. However, brain-machine interfaces estimating gait phases or locomotor behaviors, instead of continuous variables such as limb joint positions or speeds, are likely to provide more robust control strategies for the design of such neuroprostheses.

Offline decoding of end-point forces using neural ensembles: application to a brain-machine interface.

PubMed

Gupta, Rahul; Ashe, James

2009-06-01

Brain-machine interfaces (BMIs) hold a lot of promise for restoring some level of motor function to patients with neuronal disease or injury. Current BMI approaches fall into two broad categories--those that decode discrete properties of limb movement (such as movement direction and movement intent) and those that decode continuous variables (such as position and velocity). However, to enable the prosthetic devices to be useful for common everyday tasks, precise control of the forces applied by the end-point of the prosthesis (e.g., the hand) is also essential. Here, we used linear regression and Kalman filter methods to show that neural activity recorded from the motor cortex of the monkey during movements in a force field can be used to decode the end-point forces applied by the subject successfully and with high fidelity. Furthermore, the models exhibit some generalization to novel task conditions. We also demonstrate how the simultaneous prediction of kinematics and kinetics can be easily achieved using the same framework, without any degradation in decoding quality. Our results represent a useful extension of the current BMI technology, making dynamic control of a prosthetic device a distinct possibility in the near future.

Deciphering neuronal population codes for acute thermal pain

NASA Astrophysics Data System (ADS)

Chen, Zhe; Zhang, Qiaosheng; Phuong Sieu Tong, Ai; Manders, Toby R.; Wang, Jing

2017-06-01

Objective. Pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage. Current pain research mostly focuses on molecular and synaptic changes at the spinal and peripheral levels. However, a complete understanding of pain mechanisms requires the physiological study of the neocortex. Our goal is to apply a neural decoding approach to read out the onset of acute thermal pain signals, which can be used for brain-machine interface. Approach. We used micro wire arrays to record ensemble neuronal activities from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) in freely behaving rats. We further investigated neural codes for acute thermal pain at both single-cell and population levels. To detect the onset of acute thermal pain signals, we developed a novel latent state-space framework to decipher the sorted or unsorted S1 and ACC ensemble spike activities, which reveal information about the onset of pain signals. Main results. The state space analysis allows us to uncover a latent state process that drives the observed ensemble spike activity, and to further detect the ‘neuronal threshold’ for acute thermal pain on a single-trial basis. Our method achieved good detection performance in sensitivity and specificity. In addition, our results suggested that an optimal strategy for detecting the onset of acute thermal pain signals may be based on combined evidence from S1 and ACC population codes. Significance. Our study is the first to detect the onset of acute pain signals based on neuronal ensemble spike activity. It is important from a mechanistic viewpoint as it relates to the significance of S1 and ACC activities in the regulation of the acute pain onset.

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

PubMed Central

2017-01-01

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson's disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about 0.729 ± 0.16 for decoding movement from the resting state and about 0.671 ± 0.14 for decoding left and right visually cued movements. PMID:29201041

Mapping visual stimuli to perceptual decisions via sparse decoding of mesoscopic neural activity.

PubMed

Sajda, Paul

2010-01-01

In this talk I will describe our work investigating sparse decoding of neural activity, given a realistic mapping of the visual scene to neuronal spike trains generated by a model of primary visual cortex (V1). We use a linear decoder which imposes sparsity via an L1 norm. The decoder can be viewed as a decoding neuron (linear summation followed by a sigmoidal nonlinearity) in which there are relatively few non-zero synaptic weights. We find: (1) the best decoding performance is for a representation that is sparse in both space and time, (2) decoding of a temporal code results in better performance than a rate code and is also a better fit to the psychophysical data, (3) the number of neurons required for decoding increases monotonically as signal-to-noise in the stimulus decreases, with as little as 1% of the neurons required for decoding at the highest signal-to-noise levels, and (4) sparse decoding results in a more accurate decoding of the stimulus and is a better fit to psychophysical performance than a distributed decoding, for example one imposed by an L2 norm. We conclude that sparse coding is well-justified from a decoding perspective in that it results in a minimum number of neurons and maximum accuracy when sparse representations can be decoded from the neural dynamics.

Decoding bipedal locomotion from the rat sensorimotor cortex

NASA Astrophysics Data System (ADS)

Rigosa, J.; Panarese, A.; Dominici, N.; Friedli, L.; van den Brand, R.; Carpaneto, J.; DiGiovanna, J.; Courtine, G.; Micera, S.

2015-10-01

Objective. Decoding forelimb movements from the firing activity of cortical neurons has been interfaced with robotic and prosthetic systems to replace lost upper limb functions in humans. Despite the potential of this approach to improve locomotion and facilitate gait rehabilitation, decoding lower limb movement from the motor cortex has received comparatively little attention. Here, we performed experiments to identify the type and amount of information that can be decoded from neuronal ensemble activity in the hindlimb area of the rat motor cortex during bipedal locomotor tasks. Approach. Rats were trained to stand, step on a treadmill, walk overground and climb staircases in a bipedal posture. To impose this gait, the rats were secured in a robotic interface that provided support against the direction of gravity and in the mediolateral direction, but behaved transparently in the forward direction. After completion of training, rats were chronically implanted with a micro-wire array spanning the left hindlimb motor cortex to record single and multi-unit activity, and bipolar electrodes into 10 muscles of the right hindlimb to monitor electromyographic signals. Whole-body kinematics, muscle activity, and neural signals were simultaneously recorded during execution of the trained tasks over multiple days of testing. Hindlimb kinematics, muscle activity, gait phases, and locomotor tasks were decoded using offline classification algorithms. Main results. We found that the stance and swing phases of gait and the locomotor tasks were detected with accuracies as robust as 90% in all rats. Decoded hindlimb kinematics and muscle activity exhibited a larger variability across rats and tasks. Significance. Our study shows that the rodent motor cortex contains useful information for lower limb neuroprosthetic development. However, brain-machine interfaces estimating gait phases or locomotor behaviors, instead of continuous variables such as limb joint positions or speeds, are likely to provide more robust control strategies for the design of such neuroprostheses.

The Limits of Coding with Joint Constraints on Detected and Undetected Error Rates

NASA Technical Reports Server (NTRS)

Dolinar, Sam; Andrews, Kenneth; Pollara, Fabrizio; Divsalar, Dariush

2008-01-01

We develop a remarkably tight upper bound on the performance of a parameterized family of bounded angle maximum-likelihood (BA-ML) incomplete decoders. The new bound for this class of incomplete decoders is calculated from the code's weight enumerator, and is an extension of Poltyrev-type bounds developed for complete ML decoders. This bound can also be applied to bound the average performance of random code ensembles in terms of an ensemble average weight enumerator. We also formulate conditions defining a parameterized family of optimal incomplete decoders, defined to minimize both the total codeword error probability and the undetected error probability for any fixed capability of the decoder to detect errors. We illustrate the gap between optimal and BA-ML incomplete decoding via simulation of a small code.

On the error probability of general tree and trellis codes with applications to sequential decoding

NASA Technical Reports Server (NTRS)

Johannesson, R.

1973-01-01

An upper bound on the average error probability for maximum-likelihood decoding of the ensemble of random binary tree codes is derived and shown to be independent of the length of the tree. An upper bound on the average error probability for maximum-likelihood decoding of the ensemble of random L-branch binary trellis codes of rate R = 1/n is derived which separates the effects of the tail length T and the memory length M of the code. It is shown that the bound is independent of the length L of the information sequence. This implication is investigated by computer simulations of sequential decoding utilizing the stack algorithm. These simulations confirm the implication and further suggest an empirical formula for the true undetected decoding error probability with sequential decoding.

Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights

PubMed Central

Nicola, Wilten; Tripp, Bryan; Scott, Matthew

2016-01-01

A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks. PMID:26973503

Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights.

PubMed

Nicola, Wilten; Tripp, Bryan; Scott, Matthew

2016-01-01

A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks.

Extracting Behaviorally Relevant Traits from Natural Stimuli: Benefits of Combinatorial Representations at the Accessory Olfactory Bulb

PubMed Central

Kahan, Anat; Ben-Shaul, Yoram

2016-01-01

For many animals, chemosensation is essential for guiding social behavior. However, because multiple factors can modulate levels of individual chemical cues, deriving information about other individuals via natural chemical stimuli involves considerable challenges. How social information is extracted despite these sources of variability is poorly understood. The vomeronasal system provides an excellent opportunity to study this topic due to its role in detecting socially relevant traits. Here, we focus on two such traits: a female mouse’s strain and reproductive state. In particular, we measure stimulus-induced neuronal activity in the accessory olfactory bulb (AOB) in response to various dilutions of urine, vaginal secretions, and saliva, from estrus and non-estrus female mice from two different strains. We first show that all tested secretions provide information about a female’s receptivity and genotype. Next, we investigate how these traits can be decoded from neuronal activity despite multiple sources of variability. We show that individual neurons are limited in their capacity to allow trait classification across multiple sources of variability. However, simple linear classifiers sampling neuronal activity from small neuronal ensembles can provide a substantial improvement over that attained with individual units. Furthermore, we show that some traits are more efficiently detected than others, and that particular secretions may be optimized for conveying information about specific traits. Across all tested stimulus sources, discrimination between strains is more accurate than discrimination of receptivity, and detection of receptivity is more accurate with vaginal secretions than with urine. Our findings highlight the challenges of chemosensory processing of natural stimuli, and suggest that downstream readout stages decode multiple behaviorally relevant traits by sampling information from distinct but overlapping populations of AOB neurons. PMID:26938460

Extracting Behaviorally Relevant Traits from Natural Stimuli: Benefits of Combinatorial Representations at the Accessory Olfactory Bulb.

PubMed

Kahan, Anat; Ben-Shaul, Yoram

2016-03-01

For many animals, chemosensation is essential for guiding social behavior. However, because multiple factors can modulate levels of individual chemical cues, deriving information about other individuals via natural chemical stimuli involves considerable challenges. How social information is extracted despite these sources of variability is poorly understood. The vomeronasal system provides an excellent opportunity to study this topic due to its role in detecting socially relevant traits. Here, we focus on two such traits: a female mouse's strain and reproductive state. In particular, we measure stimulus-induced neuronal activity in the accessory olfactory bulb (AOB) in response to various dilutions of urine, vaginal secretions, and saliva, from estrus and non-estrus female mice from two different strains. We first show that all tested secretions provide information about a female's receptivity and genotype. Next, we investigate how these traits can be decoded from neuronal activity despite multiple sources of variability. We show that individual neurons are limited in their capacity to allow trait classification across multiple sources of variability. However, simple linear classifiers sampling neuronal activity from small neuronal ensembles can provide a substantial improvement over that attained with individual units. Furthermore, we show that some traits are more efficiently detected than others, and that particular secretions may be optimized for conveying information about specific traits. Across all tested stimulus sources, discrimination between strains is more accurate than discrimination of receptivity, and detection of receptivity is more accurate with vaginal secretions than with urine. Our findings highlight the challenges of chemosensory processing of natural stimuli, and suggest that downstream readout stages decode multiple behaviorally relevant traits by sampling information from distinct but overlapping populations of AOB neurons.

Decoding sound level in the marmoset primary auditory cortex.

PubMed

Sun, Wensheng; Marongelli, Ellisha N; Watkins, Paul V; Barbour, Dennis L

2017-10-01

Neurons that respond favorably to a particular sound level have been observed throughout the central auditory system, becoming steadily more common at higher processing areas. One theory about the role of these level-tuned or nonmonotonic neurons is the level-invariant encoding of sounds. To investigate this theory, we simulated various subpopulations of neurons by drawing from real primary auditory cortex (A1) neuron responses and surveyed their performance in forming different sound level representations. Pure nonmonotonic subpopulations did not provide the best level-invariant decoding; instead, mixtures of monotonic and nonmonotonic neurons provided the most accurate decoding. For level-fidelity decoding, the inclusion of nonmonotonic neurons slightly improved or did not change decoding accuracy until they constituted a high proportion. These results indicate that nonmonotonic neurons fill an encoding role complementary to, rather than alternate to, monotonic neurons. NEW & NOTEWORTHY Neurons with nonmonotonic rate-level functions are unique to the central auditory system. These level-tuned neurons have been proposed to account for invariant sound perception across sound levels. Through systematic simulations based on real neuron responses, this study shows that neuron populations perform sound encoding optimally when containing both monotonic and nonmonotonic neurons. The results indicate that instead of working independently, nonmonotonic neurons complement the function of monotonic neurons in different sound-encoding contexts. Copyright © 2017 the American Physiological Society.

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

PubMed Central

Presacco, Alessandro; Goodman, Ronald; Forrester, Larry

2011-01-01

Chronic recordings from ensembles of cortical neurons in primary motor and somatosensory areas in rhesus macaques provide accurate information about bipedal locomotion (Fitzsimmons NA, Lebedev MA, Peikon ID, Nicolelis MA. Front Integr Neurosci 3: 3, 2009). Here we show that the linear and angular kinematics of the ankle, knee, and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activities (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular and linear kinematics of the left and right hip, knee, and ankle joints and EEG were recorded, and neural decoders were designed and optimized with cross-validation procedures. Of note, the optimal set of electrodes of these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function. PMID:21768121

Decoding ensemble activity from neurophysiological recordings in the temporal cortex.

PubMed

Kreiman, Gabriel

2011-01-01

We study subjects with pharmacologically intractable epilepsy who undergo semi-chronic implantation of electrodes for clinical purposes. We record physiological activity from tens to more than one hundred electrodes implanted in different parts of neocortex. These recordings provide higher spatial and temporal resolution than non-invasive measures of human brain activity. Here we discuss our efforts to develop hardware and algorithms to interact with the human brain by decoding ensemble activity in single trials. We focus our discussion on decoding visual information during a variety of visual object recognition tasks but the same technologies and algorithms can also be directly applied to other cognitive phenomena.

Neuron selection based on deflection coefficient maximization for the neural decoding of dexterous finger movements.

PubMed

Kim, Yong-Hee; Thakor, Nitish V; Schieber, Marc H; Kim, Hyoung-Nam

2015-05-01

Future generations of brain-machine interface (BMI) will require more dexterous motion control such as hand and finger movements. Since a population of neurons in the primary motor cortex (M1) area is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an intended finger movement. In a BMI system, decoding discrete finger movements from a large number of input neurons does not guarantee a higher decoding accuracy in spite of the increase in computational burden. Hence, we hypothesize that selecting neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively measure the importance of each neuron based on Bayes risk minimization and deflection coefficient maximization in a statistical decision problem. Since motor cortical neurons are active with movements of several different fingers, the proposed method is more suitable for a discrete decoding of flexion-extension finger movements than the previous methods for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and also in the case of including six combined two-finger movements. While our data acquisition and analysis was done off-line and post processing, our results point to the significance of highly coding neurons in improving BMI performance.

Neuron Selection Based on Deflection Coefficient Maximization for the Neural Decoding of Dexterous Finger Movements

PubMed Central

Kim, Yong-Hee; Thakor, Nitish V.; Schieber, Marc H.; Kim, Hyoung-Nam

2015-01-01

Future generations of brain-machine interface (BMI) will require more dexterous motion control such as hand and finger movements. Since a population of neurons in the primary motor cortex (M1) area is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an intended finger movement. In a BMI system, decoding discrete finger movements from a large number of input neurons does not guarantee a higher decoding accuracy in spite of the increase in computational burden. Hence, we hypothesize that selecting neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively measure the importance of each neuron based on Bayes risk minimization and deflection coefficient maximization in a statistical decision problem. Since motor cortical neurons are active with movements of several different fingers, the proposed method is more suitable for a discrete decoding of flexion-extension finger movements than the previous methods for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and also in the case of including six combined two-finger movements. While our data acquisition and analysis was done off-line and post processing, our results point to the significance of highly coding neurons in improving BMI performance. PMID:25347884

Cortical Correlates of Fitts’ Law

PubMed Central

Ifft, Peter J.; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

2011-01-01

Fitts’ law describes the fundamental trade-off between movement accuracy and speed: it states that the duration of reaching movements is a function of target size (TS) and distance. While Fitts’ law has been extensively studied in ergonomics and has guided the design of human–computer interfaces, there have been few studies on its neuronal correlates. To elucidate sensorimotor cortical activity underlying Fitts’ law, we implanted two monkeys with multielectrode arrays in the primary motor (M1) and primary somatosensory (S1) cortices. The monkeys performed reaches with a joystick-controlled cursor toward targets of different size. The reaction time (RT), movement time, and movement velocity changed with TS, and M1 and S1 activity reflected these changes. Moreover, modifications of cortical activity could not be explained by changes of movement parameters alone, but required TS as an additional parameter. Neuronal representation of TS was especially prominent during the early RT period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was correlated with movement velocity. Neural decoders were applied to simultaneously decode TS and motor parameters from cortical modulations. We suggest that sensorimotor cortex activity reflects the characteristics of both the movement and the target. Classifiers that extract these parameters from cortical ensembles could improve neuroprosthetic control. PMID:22275888

Contribution of correlated noise and selective decoding to choice probability measurements in extrastriate visual cortex.

PubMed

Gu, Yong; Angelaki, Dora E; DeAngelis, Gregory C

2014-07-01

Trial by trial covariations between neural activity and perceptual decisions (quantified by choice Probability, CP) have been used to probe the contribution of sensory neurons to perceptual decisions. CPs are thought to be determined by both selective decoding of neural activity and by the structure of correlated noise among neurons, but the respective roles of these factors in creating CPs have been controversial. We used biologically-constrained simulations to explore this issue, taking advantage of a peculiar pattern of CPs exhibited by multisensory neurons in area MSTd that represent self-motion. Although models that relied on correlated noise or selective decoding could both account for the peculiar pattern of CPs, predictions of the selective decoding model were substantially more consistent with various features of the neural and behavioral data. While correlated noise is essential to observe CPs, our findings suggest that selective decoding of neuronal signals also plays important roles.

The Sensory Neurons of Touch

PubMed Central

Abraira, Victoria E.; Ginty, David D.

2013-01-01

The somatosensory system decodes a wide range of tactile stimuli and thus endows us with a remarkable capacity for object recognition, texture discrimination, sensory-motor feedback and social exchange. The first step leading to perception of innocuous touch is activation of cutaneous sensory neurons called low-threshold mechanoreceptors (LTMRs). Here, we review the properties and functions of LTMRs, emphasizing the unique tuning properties of LTMR subtypes and the organizational logic of their peripheral and central axonal projections. We discuss the spinal cord neurophysiological representation of complex mechanical forces acting upon the skin and current views of how tactile information is processed and conveyed from the spinal cord to the brain. An integrative model in which ensembles of impulses arising from physiologically distinct LTMRs are integrated and processed in somatotopically aligned mechanosensory columns of the spinal cord dorsal horn underlies the nervous system’s enormous capacity for perceiving the richness of the tactile world. PMID:23972592

Temporal Response Properties of Accessory Olfactory Bulb Neurons: Limitations and Opportunities for Decoding.

PubMed

Yoles-Frenkel, Michal; Kahan, Anat; Ben-Shaul, Yoram

2018-05-23

The vomeronasal system (VNS) is a major vertebrate chemosensory system that functions in parallel to the main olfactory system (MOS). Despite many similarities, the two systems dramatically differ in the temporal domain. While MOS responses are governed by breathing and follow a subsecond temporal scale, VNS responses are uncoupled from breathing and evolve over seconds. This suggests that the contribution of response dynamics to stimulus information will differ between these systems. While temporal dynamics in the MOS are widely investigated, similar analyses in the accessory olfactory bulb (AOB) are lacking. Here, we have addressed this issue using controlled stimulus delivery to the vomeronasal organ of male and female mice. We first analyzed the temporal properties of AOB projection neurons and demonstrated that neurons display prolonged, variable, and neuron-specific characteristics. We then analyzed various decoding schemes using AOB population responses. We showed that compared with the simplest scheme (i.e., integration of spike counts over the entire response period), the division of this period into smaller temporal bins actually yields poorer decoding accuracy. However, optimal classification accuracy can be achieved well before the end of the response period by integrating spike counts within temporally defined windows. Since VNS stimulus uptake is variable, we analyzed decoding using limited information about stimulus uptake time, and showed that with enough neurons, such time-invariant decoding is feasible. Finally, we conducted simulations that demonstrated that, unlike the main olfactory bulb, the temporal features of AOB neurons disfavor decoding with high temporal accuracy, and, rather, support decoding without precise knowledge of stimulus uptake time. SIGNIFICANCE STATEMENT A key goal in sensory system research is to identify which metrics of neuronal activity are relevant for decoding stimulus features. Here, we describe the first systematic analysis of temporal coding in the vomeronasal system (VNS), a chemosensory system devoted to socially relevant cues. Compared with the main olfactory system, timescales of VNS function are inherently slower and variable. Using various analyses of real and simulated data, we show that the consideration of response times relative to stimulus uptake can aid the decoding of stimulus information from neuronal activity. However, response properties of accessory olfactory bulb neurons favor decoding schemes that do not rely on the precise timing of stimulus uptake. Such schemes are consistent with the variable nature of VNS stimulus uptake. Copyright © 2018 the authors 0270-6474/18/384957-20$15.00/0.

Bayesian population decoding of spiking neurons.

PubMed

Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

2009-01-01

The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

Imprinting and recalling cortical ensembles.

PubMed

Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S; Yuste, Rafael

2016-08-12

Neuronal ensembles are coactive groups of neurons that may represent building blocks of cortical circuits. These ensembles could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from ensembles in the visual cortex of awake mice builds neuronal ensembles that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted ensembles can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. Copyright © 2016, American Association for the Advancement of Science.

Contribution of correlated noise and selective decoding to choice probability measurements in extrastriate visual cortex

PubMed Central

Gu, Yong; Angelaki, Dora E; DeAngelis, Gregory C

2014-01-01

Trial by trial covariations between neural activity and perceptual decisions (quantified by choice Probability, CP) have been used to probe the contribution of sensory neurons to perceptual decisions. CPs are thought to be determined by both selective decoding of neural activity and by the structure of correlated noise among neurons, but the respective roles of these factors in creating CPs have been controversial. We used biologically-constrained simulations to explore this issue, taking advantage of a peculiar pattern of CPs exhibited by multisensory neurons in area MSTd that represent self-motion. Although models that relied on correlated noise or selective decoding could both account for the peculiar pattern of CPs, predictions of the selective decoding model were substantially more consistent with various features of the neural and behavioral data. While correlated noise is essential to observe CPs, our findings suggest that selective decoding of neuronal signals also plays important roles. DOI: http://dx.doi.org/10.7554/eLife.02670.001 PMID:24986734

Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

NASA Astrophysics Data System (ADS)

Rajangam, Sankaranarayani; Tseng, Po-He; Yin, Allen; Lehew, Gary; Schwarz, David; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

2016-03-01

Several groups have developed brain-machine-interfaces (BMIs) that allow primates to use cortical activity to control artificial limbs. Yet, it remains unknown whether cortical ensembles could represent the kinematics of whole-body navigation and be used to operate a BMI that moves a wheelchair continuously in space. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair, using their cortical activity as the main control signal. Two monkeys were chronically implanted with multichannel microelectrode arrays that allowed wireless recordings from ensembles of premotor and sensorimotor cortical neurons. Initially, while monkeys remained seated in the robotic wheelchair, passive navigation was employed to train a linear decoder to extract 2D wheelchair kinematics from cortical activity. Next, monkeys employed the wireless BMI to translate their cortical activity into the robotic wheelchair’s translational and rotational velocities. Over time, monkeys improved their ability to navigate the wheelchair toward the location of a grape reward. The navigation was enacted by populations of cortical neurons tuned to whole-body displacement. During practice with the apparatus, we also noticed the presence of a cortical representation of the distance to reward location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future.

Local-learning-based neuron selection for grasping gesture prediction in motor brain machine interfaces

NASA Astrophysics Data System (ADS)

Xu, Kai; Wang, Yiwen; Wang, Yueming; Wang, Fang; Hao, Yaoyao; Zhang, Shaomin; Zhang, Qiaosheng; Chen, Weidong; Zheng, Xiaoxiang

2013-04-01

Objective. The high-dimensional neural recordings bring computational challenges to movement decoding in motor brain machine interfaces (mBMI), especially for portable applications. However, not all recorded neural activities relate to the execution of a certain movement task. This paper proposes to use a local-learning-based method to perform neuron selection for the gesture prediction in a reaching and grasping task. Approach. Nonlinear neural activities are decomposed into a set of linear ones in a weighted feature space. A margin is defined to measure the distance between inter-class and intra-class neural patterns. The weights, reflecting the importance of neurons, are obtained by minimizing a margin-based exponential error function. To find the most dominant neurons in the task, 1-norm regularization is introduced to the objective function for sparse weights, where near-zero weights indicate irrelevant neurons. Main results. The signals of only 10 neurons out of 70 selected by the proposed method could achieve over 95% of the full recording's decoding accuracy of gesture predictions, no matter which different decoding methods are used (support vector machine and K-nearest neighbor). The temporal activities of the selected neurons show visually distinguishable patterns associated with various hand states. Compared with other algorithms, the proposed method can better eliminate the irrelevant neurons with near-zero weights and provides the important neuron subset with the best decoding performance in statistics. The weights of important neurons converge usually within 10-20 iterations. In addition, we study the temporal and spatial variation of neuron importance along a period of one and a half months in the same task. A high decoding performance can be maintained by updating the neuron subset. Significance. The proposed algorithm effectively ascertains the neuronal importance without assuming any coding model and provides a high performance with different decoding models. It shows better robustness of identifying the important neurons with noisy signals presented. The low demand of computational resources which, reflected by the fast convergence, indicates the feasibility of the method applied in portable BMI systems. The ascertainment of the important neurons helps to inspect neural patterns visually associated with the movement task. The elimination of irrelevant neurons greatly reduces the computational burden of mBMI systems and maintains the performance with better robustness.

Ensemble cryo-EM elucidates the mechanism of translation fidelity

PubMed Central

Loveland, Anna B.; Demo, Gabriel; Grigorieff, Nikolaus; Korostelev, Andrei A.

2017-01-01

SUMMARY Faithful gene translation depends on accurate decoding, whose structural mechanism remains a matter of debate. Ribosomes decode mRNA codons by selecting cognate aminoacyl-tRNAs delivered by EF-Tu. We present high-resolution structural ensembles of ribosomes with cognate or near-cognate aminoacyl-tRNAs delivered by EF-Tu. Both cognate and near-cognate tRNA anticodons explore the A site of an open 30S subunit, while inactive EF-Tu is separated from the 50S subunit. A transient conformation of decoding-center nucleotide G530 stabilizes the cognate codon-anticodon helix, initiating step-wise “latching” of the decoding center. The resulting 30S domain closure docks EF-Tu at the sarcin-ricin loop of the 50S subunit, activating EF-Tu for GTP hydrolysis and ensuing aminoacyl-tRNA accommodation. By contrast, near-cognate complexes fail to induce the G530 latch, thus favoring open 30S pre-accommodation intermediates with inactive EF-Tu. This work unveils long-sought structural differences between the pre-accommodation of cognate and near-cognate tRNA that elucidate the mechanism of accurate decoding. PMID:28538735

Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.

PubMed

Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Fan, Gong; Wang, Yiwen; Zheng, Xiaoxiang

2014-01-01

Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

Coding of odors by temporal binding within a model network of the locust antennal lobe.

PubMed

Patel, Mainak J; Rangan, Aaditya V; Cai, David

2013-01-01

The locust olfactory system interfaces with the external world through antennal receptor neurons (ORNs), which represent odors in a distributed, combinatorial manner. ORN axons bundle together to form the antennal nerve, which relays sensory information centrally to the antennal lobe (AL). Within the AL, an odor generates a dynamically evolving ensemble of active cells, leading to a stimulus-specific temporal progression of neuronal spiking. This experimental observation has led to the hypothesis that an odor is encoded within the AL by a dynamically evolving trajectory of projection neuron (PN) activity that can be decoded piecewise to ascertain odor identity. In order to study information coding within the locust AL, we developed a scaled-down model of the locust AL using Hodgkin-Huxley-type neurons and biologically realistic connectivity parameters and current components. Using our model, we examined correlations in the precise timing of spikes across multiple neurons, and our results suggest an alternative to the dynamic trajectory hypothesis. We propose that the dynamical interplay of fast and slow inhibition within the locust AL induces temporally stable correlations in the spiking activity of an odor-dependent neural subset, giving rise to a temporal binding code that allows rapid stimulus detection by downstream elements.

Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.

PubMed

Naud, Richard; Gerstner, Wulfram

2012-01-01

The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractoriness and adaptation. As a modeling framework, we use the spike response model, also known as the generalized linear neuron model. Because of refractoriness, the effect of the most recent spike on the spiking probability a few milliseconds later is very strong. The influence of the last spike needs therefore to be described with high precision, while the rest of the neuronal spiking history merely introduces an average self-inhibition or adaptation that depends on the expected number of past spikes but not on the exact spike timings. Based on these insights, we derive a 'quasi-renewal equation' which is shown to yield an excellent description of the firing rate of adapting neurons. We explore the domain of validity of the quasi-renewal equation and compare it with other rate equations for populations of spiking neurons. The problem of decoding the stimulus from the population response (or PSTH) is addressed analogously. We find that for small levels of activity and weak adaptation, a simple accumulator of the past activity is sufficient to decode the original input, but when refractory effects become large decoding becomes a non-linear function of the past activity. The results presented here can be applied to the mean-field analysis of coupled neuron networks, but also to arbitrary point processes with negative self-interaction.

New technologies for examining the role of neuronal ensembles in drug addiction and fear.

PubMed

Cruz, Fabio C; Koya, Eisuke; Guez-Barber, Danielle H; Bossert, Jennifer M; Lupica, Carl R; Shaham, Yavin; Hope, Bruce T

2013-11-01

Correlational data suggest that learned associations are encoded within neuronal ensembles. However, it has been difficult to prove that neuronal ensembles mediate learned behaviours because traditional pharmacological and lesion methods, and even newer cell type-specific methods, affect both activated and non-activated neurons. In addition, previous studies on synaptic and molecular alterations induced by learning did not distinguish between behaviourally activated and non-activated neurons. Here, we describe three new approaches--Daun02 inactivation, FACS sorting of activated neurons and Fos-GFP transgenic rats--that have been used to selectively target and study activated neuronal ensembles in models of conditioned drug effects and relapse. We also describe two new tools--Fos-tTA transgenic mice and inactivation of CREB-overexpressing neurons--that have been used to study the role of neuronal ensembles in conditioned fear.

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram

PubMed Central

Naud, Richard; Gerstner, Wulfram

2012-01-01

The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractoriness and adaptation. As a modeling framework, we use the spike response model, also known as the generalized linear neuron model. Because of refractoriness, the effect of the most recent spike on the spiking probability a few milliseconds later is very strong. The influence of the last spike needs therefore to be described with high precision, while the rest of the neuronal spiking history merely introduces an average self-inhibition or adaptation that depends on the expected number of past spikes but not on the exact spike timings. Based on these insights, we derive a ‘quasi-renewal equation’ which is shown to yield an excellent description of the firing rate of adapting neurons. We explore the domain of validity of the quasi-renewal equation and compare it with other rate equations for populations of spiking neurons. The problem of decoding the stimulus from the population response (or PSTH) is addressed analogously. We find that for small levels of activity and weak adaptation, a simple accumulator of the past activity is sufficient to decode the original input, but when refractory effects become large decoding becomes a non-linear function of the past activity. The results presented here can be applied to the mean-field analysis of coupled neuron networks, but also to arbitrary point processes with negative self-interaction. PMID:23055914

Changes in Appetitive Associative Strength Modulates Nucleus Accumbens, But Not Orbitofrontal Cortex Neuronal Ensemble Excitability.

PubMed

Ziminski, Joseph J; Hessler, Sabine; Margetts-Smith, Gabriella; Sieburg, Meike C; Crombag, Hans S; Koya, Eisuke

2017-03-22

Cues that predict the availability of food rewards influence motivational states and elicit food-seeking behaviors. If a cue no longer predicts food availability, then animals may adapt accordingly by inhibiting food-seeking responses. Sparsely activated sets of neurons, coined "neuronal ensembles," have been shown to encode the strength of reward-cue associations. Although alterations in intrinsic excitability have been shown to underlie many learning and memory processes, little is known about these properties specifically on cue-activated neuronal ensembles. We examined the activation patterns of cue-activated orbitofrontal cortex (OFC) and nucleus accumbens (NAc) shell ensembles using wild-type and Fos-GFP mice, which express green fluorescent protein (GFP) in activated neurons, after appetitive conditioning with sucrose and extinction learning. We also investigated the neuronal excitability of recently activated, GFP+ neurons in these brain areas using whole-cell electrophysiology in brain slices. Exposure to a sucrose cue elicited activation of neurons in both the NAc shell and OFC. In the NAc shell, but not the OFC, these activated GFP+ neurons were more excitable than surrounding GFP- neurons. After extinction, the number of neurons activated in both areas was reduced and activated ensembles in neither area exhibited altered excitability. These data suggest that learning-induced alterations in the intrinsic excitability of neuronal ensembles is regulated dynamically across different brain areas. Furthermore, we show that changes in associative strength modulate the excitability profile of activated ensembles in the NAc shell. SIGNIFICANCE STATEMENT Sparsely distributed sets of neurons called "neuronal ensembles" encode learned associations about food and cues predictive of its availability. Widespread changes in neuronal excitability have been observed in limbic brain areas after associative learning, but little is known about the excitability changes that occur specifically on neuronal ensembles that encode appetitive associations. Here, we reveal that sucrose cue exposure recruited a more excitable ensemble in the nucleus accumbens, but not orbitofrontal cortex, compared with their surrounding neurons. This excitability difference was not observed when the cue's salience was diminished after extinction learning. These novel data provide evidence that the intrinsic excitability of appetitive memory-encoding ensembles is regulated differentially across brain areas and adapts dynamically to changes in associative strength. Copyright © 2017 the authors 0270-6474/17/373160-11$15.00/0.

Imprinting and Recalling Cortical Ensembles

PubMed Central

Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S.; Yuste, Rafael

2017-01-01

Neuronal ensembles are coactive groups of neurons that may represent emergent building blocks of neural circuits. They could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations in visual cortex of awake mice generates artificially induced ensembles which recur spontaneously after being imprinted and do not disrupt preexistent ones. Moreover, imprinted ensembles can be recalled by single cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. PMID:27516599

Spiking Neural Network Decoder for Brain-Machine Interfaces.

PubMed

Dethier, Julie; Gilja, Vikash; Nuyujukian, Paul; Elassaad, Shauki A; Shenoy, Krishna V; Boahen, Kwabena

2011-01-01

We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-filter neural prosthetic decode algorithm developed to predict the arm's velocity on to the SNN using the Neural Engineering Framework and simulated it using Nengo , a freely available software package. A 20,000-neuron network matched the standard decoder's prediction to within 0.03% (normalized by maximum arm velocity). A 1,600-neuron version of this network was within 0.27%, and run in real-time on a 3GHz PC. These results demonstrate that a SNN can implement a statistical signal processing algorithm widely used as the decoder in high-performance neural prostheses (Kalman filter), and achieve similar results with just a few thousand neurons. Hardware SNN implementations-neuromorphic chips-may offer power savings, essential for realizing fully-implantable cortically controlled prostheses.

Dissociation of Self-Motion and Object Motion by Linear Population Decoding That Approximates Marginalization

PubMed Central

Sasaki, Ryo; Angelaki, Dora E.

2017-01-01

We use visual image motion to judge the movement of objects, as well as our own movements through the environment. Generally, image motion components caused by object motion and self-motion are confounded in the retinal image. Thus, to estimate heading, the brain would ideally marginalize out the effects of object motion (or vice versa), but little is known about how this is accomplished neurally. Behavioral studies suggest that vestibular signals play a role in dissociating object motion and self-motion, and recent computational work suggests that a linear decoder can approximate marginalization by taking advantage of diverse multisensory representations. By measuring responses of MSTd neurons in two male rhesus monkeys and by applying a recently-developed method to approximate marginalization by linear population decoding, we tested the hypothesis that vestibular signals help to dissociate self-motion and object motion. We show that vestibular signals stabilize tuning for heading in neurons with congruent visual and vestibular heading preferences, whereas they stabilize tuning for object motion in neurons with discrepant preferences. Thus, vestibular signals enhance the separability of joint tuning for object motion and self-motion. We further show that a linear decoder, designed to approximate marginalization, allows the population to represent either self-motion or object motion with good accuracy. Decoder weights are broadly consistent with a readout strategy, suggested by recent computational work, in which responses are decoded according to the vestibular preferences of multisensory neurons. These results demonstrate, at both single neuron and population levels, that vestibular signals help to dissociate self-motion and object motion. SIGNIFICANCE STATEMENT The brain often needs to estimate one property of a changing environment while ignoring others. This can be difficult because multiple properties of the environment may be confounded in sensory signals. The brain can solve this problem by marginalizing over irrelevant properties to estimate the property-of-interest. We explore this problem in the context of self-motion and object motion, which are inherently confounded in the retinal image. We examine how diversity in a population of multisensory neurons may be exploited to decode self-motion and object motion from the population activity of neurons in macaque area MSTd. PMID:29030435

New technologies for examining neuronal ensembles in drug addiction and fear

PubMed Central

Cruz, Fabio C.; Koya, Eisuke; Guez-Barber, Danielle H.; Bossert, Jennifer M.; Lupica, Carl R.; Shaham, Yavin; Hope, Bruce T.

2015-01-01

Correlational data suggest that learned associations are encoded within neuronal ensembles. However, it has been difficult to prove that neuronal ensembles mediate learned behaviours because traditional pharmacological and lesion methods, and even newer cell type-specific methods, affect both activated and non-activated neurons. Additionally, previous studies on synaptic and molecular alterations induced by learning did not distinguish between behaviourally activated and non-activated neurons. Here, we describe three new approaches—Daun02 inactivation, FACS sorting of activated neurons and c-fos-GFP transgenic rats — that have been used to selectively target and study activated neuronal ensembles in models of conditioned drug effects and relapse. We also describe two new tools — c-fos-tTA mice and inactivation of CREB-overexpressing neurons — that have been used to study the role of neuronal ensembles in conditioned fear. PMID:24088811

Bidirectional Modulation of Intrinsic Excitability in Rat Prelimbic Cortex Neuronal Ensembles and Non-Ensembles after Operant Learning.

PubMed

Whitaker, Leslie R; Warren, Brandon L; Venniro, Marco; Harte, Tyler C; McPherson, Kylie B; Beidel, Jennifer; Bossert, Jennifer M; Shaham, Yavin; Bonci, Antonello; Hope, Bruce T

2017-09-06

Learned associations between environmental stimuli and rewards drive goal-directed learning and motivated behavior. These memories are thought to be encoded by alterations within specific patterns of sparsely distributed neurons called neuronal ensembles that are activated selectively by reward-predictive stimuli. Here, we use the Fos promoter to identify strongly activated neuronal ensembles in rat prelimbic cortex (PLC) and assess altered intrinsic excitability after 10 d of operant food self-administration training (1 h/d). First, we used the Daun02 inactivation procedure in male FosLacZ-transgenic rats to ablate selectively Fos-expressing PLC neurons that were active during operant food self-administration. Selective ablation of these neurons decreased food seeking. We then used male FosGFP-transgenic rats to assess selective alterations of intrinsic excitability in Fos-expressing neuronal ensembles (FosGFP + ) that were activated during food self-administration and compared these with alterations in less activated non-ensemble neurons (FosGFP - ). Using whole-cell recordings of layer V pyramidal neurons in an ex vivo brain slice preparation, we found that operant self-administration increased excitability of FosGFP + neurons and decreased excitability of FosGFP - neurons. Increased excitability of FosGFP + neurons was driven by increased steady-state input resistance. Decreased excitability of FosGFP - neurons was driven by increased contribution of small-conductance calcium-activated potassium (SK) channels. Injections of the specific SK channel antagonist apamin into PLC increased Fos expression but had no effect on food seeking. Overall, operant learning increased intrinsic excitability of PLC Fos-expressing neuronal ensembles that play a role in food seeking but decreased intrinsic excitability of Fos - non-ensembles. SIGNIFICANCE STATEMENT Prefrontal cortex activity plays a critical role in operant learning, but the underlying cellular mechanisms are unknown. Using the chemogenetic Daun02 inactivation procedure, we found that a small number of strongly activated Fos-expressing neuronal ensembles in rat PLC play an important role in learned operant food seeking. Using GFP expression to identify Fos-expressing layer V pyramidal neurons in prelimbic cortex (PLC) of FosGFP-transgenic rats, we found that operant food self-administration led to increased intrinsic excitability in the behaviorally relevant Fos-expressing neuronal ensembles, but decreased intrinsic excitability in Fos - neurons using distinct cellular mechanisms. Copyright © 2017 the authors 0270-6474/17/378845-12$15.00/0.

Decoding thalamic afferent input using microcircuit spiking activity

PubMed Central

Sederberg, Audrey J.; Palmer, Stephanie E.

2015-01-01

A behavioral response appropriate to a sensory stimulus depends on the collective activity of thousands of interconnected neurons. The majority of cortical connections arise from neighboring neurons, and thus understanding the cortical code requires characterizing information representation at the scale of the cortical microcircuit. Using two-photon calcium imaging, we densely sampled the thalamically evoked response of hundreds of neurons spanning multiple layers and columns in thalamocortical slices of mouse somatosensory cortex. We then used a biologically plausible decoder to characterize the representation of two distinct thalamic inputs, at the level of the microcircuit, to reveal those aspects of the activity pattern that are likely relevant to downstream neurons. Our data suggest a sparse code, distributed across lamina, in which a small population of cells carries stimulus-relevant information. Furthermore, we find that, within this subset of neurons, decoder performance improves when noise correlations are taken into account. PMID:25695647

Decoding thalamic afferent input using microcircuit spiking activity.

PubMed

Sederberg, Audrey J; Palmer, Stephanie E; MacLean, Jason N

2015-04-01

A behavioral response appropriate to a sensory stimulus depends on the collective activity of thousands of interconnected neurons. The majority of cortical connections arise from neighboring neurons, and thus understanding the cortical code requires characterizing information representation at the scale of the cortical microcircuit. Using two-photon calcium imaging, we densely sampled the thalamically evoked response of hundreds of neurons spanning multiple layers and columns in thalamocortical slices of mouse somatosensory cortex. We then used a biologically plausible decoder to characterize the representation of two distinct thalamic inputs, at the level of the microcircuit, to reveal those aspects of the activity pattern that are likely relevant to downstream neurons. Our data suggest a sparse code, distributed across lamina, in which a small population of cells carries stimulus-relevant information. Furthermore, we find that, within this subset of neurons, decoder performance improves when noise correlations are taken into account. Copyright © 2015 the American Physiological Society.

Neural Representation of Spatial Topology in the Rodent Hippocampus

PubMed Central

Chen, Zhe; Gomperts, Stephen N.; Yamamoto, Jun; Wilson, Matthew A.

2014-01-01

Pyramidal cells in the rodent hippocampus often exhibit clear spatial tuning in navigation. Although it has been long suggested that pyramidal cell activity may underlie a topological code rather than a topographic code, it remains unclear whether an abstract spatial topology can be encoded in the ensemble spiking activity of hippocampal place cells. Using a statistical approach developed previously, we investigate this question and related issues in greater details. We recorded ensembles of hippocampal neurons as rodents freely foraged in one and two-dimensional spatial environments, and we used a “decode-to-uncover” strategy to examine the temporally structured patterns embedded in the ensemble spiking activity in the absence of observed spatial correlates during periods of rodent navigation or awake immobility. Specifically, the spatial environment was represented by a finite discrete state space. Trajectories across spatial locations (“states”) were associated with consistent hippocampal ensemble spiking patterns, which were characterized by a state transition matrix. From this state transition matrix, we inferred a topology graph that defined the connectivity in the state space. In both one and two-dimensional environments, the extracted behavior patterns from the rodent hippocampal population codes were compared against randomly shuffled spike data. In contrast to a topographic code, our results support the efficiency of topological coding in the presence of sparse sample size and fuzzy space mapping. This computational approach allows us to quantify the variability of ensemble spiking activity, to examine hippocampal population codes during off-line states, and to quantify the topological complexity of the environment. PMID:24102128

Distinct Fos-Expressing Neuronal Ensembles in the Ventromedial Prefrontal Cortex Mediate Food Reward and Extinction Memories

PubMed Central

Warren, Brandon L.; Mendoza, Michael P.; Cruz, Fabio C.; Leao, Rodrigo M.; Caprioli, Daniele; Rubio, F. Javier; Whitaker, Leslie R.; McPherson, Kylie B.; Bossert, Jennifer M.; Shaham, Yavin

2016-01-01

In operant learning, initial reward-associated memories are thought to be distinct from subsequent extinction-associated memories. Memories formed during operant learning are thought to be stored in “neuronal ensembles.” Thus, we hypothesize that different neuronal ensembles encode reward- and extinction-associated memories. Here, we examined prefrontal cortex neuronal ensembles involved in the recall of reward and extinction memories of food self-administration. We first trained rats to lever press for palatable food pellets for 7 d (1 h/d) and then exposed them to 0, 2, or 7 daily extinction sessions in which lever presses were not reinforced. Twenty-four hours after the last training or extinction session, we exposed the rats to either a short 15 min extinction test session or left them in their homecage (a control condition). We found maximal Fos (a neuronal activity marker) immunoreactivity in the ventral medial prefrontal cortex of rats that previously received 2 extinction sessions, suggesting that neuronal ensembles in this area encode extinction memories. We then used the Daun02 inactivation procedure to selectively disrupt ventral medial prefrontal cortex neuronal ensembles that were activated during the 15 min extinction session following 0 (no extinction) or 2 prior extinction sessions to determine the effects of inactivating the putative food reward and extinction ensembles, respectively, on subsequent nonreinforced food seeking 2 d later. Inactivation of the food reward ensembles decreased food seeking, whereas inactivation of the extinction ensembles increased food seeking. Our results indicate that distinct neuronal ensembles encoding operant reward and extinction memories intermingle within the same cortical area. SIGNIFICANCE STATEMENT A current popular hypothesis is that neuronal ensembles in different prefrontal cortex areas control reward-associated versus extinction-associated memories: the dorsal medial prefrontal cortex (mPFC) promotes reward seeking, whereas the ventral mPFC inhibits reward seeking. In this paper, we use the Daun02 chemogenetic inactivation procedure to demonstrate that Fos-expressing neuronal ensembles mediating both food reward and extinction memories intermingle within the same ventral mPFC area. PMID:27335401

Distinct Fos-Expressing Neuronal Ensembles in the Ventromedial Prefrontal Cortex Mediate Food Reward and Extinction Memories.

PubMed

Warren, Brandon L; Mendoza, Michael P; Cruz, Fabio C; Leao, Rodrigo M; Caprioli, Daniele; Rubio, F Javier; Whitaker, Leslie R; McPherson, Kylie B; Bossert, Jennifer M; Shaham, Yavin; Hope, Bruce T

2016-06-22

In operant learning, initial reward-associated memories are thought to be distinct from subsequent extinction-associated memories. Memories formed during operant learning are thought to be stored in "neuronal ensembles." Thus, we hypothesize that different neuronal ensembles encode reward- and extinction-associated memories. Here, we examined prefrontal cortex neuronal ensembles involved in the recall of reward and extinction memories of food self-administration. We first trained rats to lever press for palatable food pellets for 7 d (1 h/d) and then exposed them to 0, 2, or 7 daily extinction sessions in which lever presses were not reinforced. Twenty-four hours after the last training or extinction session, we exposed the rats to either a short 15 min extinction test session or left them in their homecage (a control condition). We found maximal Fos (a neuronal activity marker) immunoreactivity in the ventral medial prefrontal cortex of rats that previously received 2 extinction sessions, suggesting that neuronal ensembles in this area encode extinction memories. We then used the Daun02 inactivation procedure to selectively disrupt ventral medial prefrontal cortex neuronal ensembles that were activated during the 15 min extinction session following 0 (no extinction) or 2 prior extinction sessions to determine the effects of inactivating the putative food reward and extinction ensembles, respectively, on subsequent nonreinforced food seeking 2 d later. Inactivation of the food reward ensembles decreased food seeking, whereas inactivation of the extinction ensembles increased food seeking. Our results indicate that distinct neuronal ensembles encoding operant reward and extinction memories intermingle within the same cortical area. A current popular hypothesis is that neuronal ensembles in different prefrontal cortex areas control reward-associated versus extinction-associated memories: the dorsal medial prefrontal cortex (mPFC) promotes reward seeking, whereas the ventral mPFC inhibits reward seeking. In this paper, we use the Daun02 chemogenetic inactivation procedure to demonstrate that Fos-expressing neuronal ensembles mediating both food reward and extinction memories intermingle within the same ventral mPFC area. Copyright © 2016 the authors 0270-6474/16/366691-13$15.00/0.

Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys

PubMed Central

Ifft, Peter J.; Shokur, Solaiman; Li, Zheng; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

2014-01-01

Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to severely paralyzed patients. However, previous BMIs enabled only single arm functionality, and control of bimanual movements was a major challenge. Here, we developed and tested a bimanual BMI that enabled rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374–497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a 5th order unscented Kalman filter (UKF). The UKF is well-suited for BMI decoding because it accounts for both characteristics of reaching movements and their representation by cortical neurons. The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals’ performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. PMID:24197735

Dissociation of Self-Motion and Object Motion by Linear Population Decoding That Approximates Marginalization.

PubMed

Sasaki, Ryo; Angelaki, Dora E; DeAngelis, Gregory C

2017-11-15

We use visual image motion to judge the movement of objects, as well as our own movements through the environment. Generally, image motion components caused by object motion and self-motion are confounded in the retinal image. Thus, to estimate heading, the brain would ideally marginalize out the effects of object motion (or vice versa), but little is known about how this is accomplished neurally. Behavioral studies suggest that vestibular signals play a role in dissociating object motion and self-motion, and recent computational work suggests that a linear decoder can approximate marginalization by taking advantage of diverse multisensory representations. By measuring responses of MSTd neurons in two male rhesus monkeys and by applying a recently-developed method to approximate marginalization by linear population decoding, we tested the hypothesis that vestibular signals help to dissociate self-motion and object motion. We show that vestibular signals stabilize tuning for heading in neurons with congruent visual and vestibular heading preferences, whereas they stabilize tuning for object motion in neurons with discrepant preferences. Thus, vestibular signals enhance the separability of joint tuning for object motion and self-motion. We further show that a linear decoder, designed to approximate marginalization, allows the population to represent either self-motion or object motion with good accuracy. Decoder weights are broadly consistent with a readout strategy, suggested by recent computational work, in which responses are decoded according to the vestibular preferences of multisensory neurons. These results demonstrate, at both single neuron and population levels, that vestibular signals help to dissociate self-motion and object motion. SIGNIFICANCE STATEMENT The brain often needs to estimate one property of a changing environment while ignoring others. This can be difficult because multiple properties of the environment may be confounded in sensory signals. The brain can solve this problem by marginalizing over irrelevant properties to estimate the property-of-interest. We explore this problem in the context of self-motion and object motion, which are inherently confounded in the retinal image. We examine how diversity in a population of multisensory neurons may be exploited to decode self-motion and object motion from the population activity of neurons in macaque area MSTd. Copyright © 2017 the authors 0270-6474/17/3711204-16$15.00/0.

Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons.

PubMed

Oddo, Calogero M; Mazzoni, Alberto; Spanne, Anton; Enander, Jonas M D; Mogensen, Hannes; Bengtsson, Fredrik; Camboni, Domenico; Micera, Silvestro; Jörntell, Henrik

2017-04-04

Investigations of the mechanisms of touch perception and decoding has been hampered by difficulties in achieving invariant patterns of skin sensor activation. To obtain reproducible spatiotemporal patterns of activation of sensory afferents, we used an artificial fingertip equipped with an array of neuromorphic sensors. The artificial fingertip was used to transduce real-world haptic stimuli into spatiotemporal patterns of spikes. These spike patterns were delivered to the skin afferents of the second digit of rats via an array of stimulation electrodes. Combined with low-noise intra- and extracellular recordings from neocortical neurons in vivo, this approach provided a previously inaccessible high resolution analysis of the representation of tactile information in the neocortical neuronal circuitry. The results indicate high information content in individual neurons and reveal multiple novel neuronal tactile coding features such as heterogeneous and complementary spatiotemporal input selectivity also between neighboring neurons. Such neuronal heterogeneity and complementariness can potentially support a very high decoding capacity in a limited population of neurons. Our results also indicate a potential neuroprosthetic approach to communicate with the brain at a very high resolution and provide a potential novel solution for evaluating the degree or state of neurological disease in animal models.

Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons

PubMed Central

Oddo, Calogero M.; Mazzoni, Alberto; Spanne, Anton; Enander, Jonas M. D.; Mogensen, Hannes; Bengtsson, Fredrik; Camboni, Domenico; Micera, Silvestro; Jörntell, Henrik

2017-01-01

Investigations of the mechanisms of touch perception and decoding has been hampered by difficulties in achieving invariant patterns of skin sensor activation. To obtain reproducible spatiotemporal patterns of activation of sensory afferents, we used an artificial fingertip equipped with an array of neuromorphic sensors. The artificial fingertip was used to transduce real-world haptic stimuli into spatiotemporal patterns of spikes. These spike patterns were delivered to the skin afferents of the second digit of rats via an array of stimulation electrodes. Combined with low-noise intra- and extracellular recordings from neocortical neurons in vivo, this approach provided a previously inaccessible high resolution analysis of the representation of tactile information in the neocortical neuronal circuitry. The results indicate high information content in individual neurons and reveal multiple novel neuronal tactile coding features such as heterogeneous and complementary spatiotemporal input selectivity also between neighboring neurons. Such neuronal heterogeneity and complementariness can potentially support a very high decoding capacity in a limited population of neurons. Our results also indicate a potential neuroprosthetic approach to communicate with the brain at a very high resolution and provide a potential novel solution for evaluating the degree or state of neurological disease in animal models. PMID:28374841

Performance breakdown in optimal stimulus decoding

NASA Astrophysics Data System (ADS)

Kostal, Lubomir; Lansky, Petr; Pilarski, Stevan

2015-06-01

Objective. One of the primary goals of neuroscience is to understand how neurons encode and process information about their environment. The problem is often approached indirectly by examining the degree to which the neuronal response reflects the stimulus feature of interest. Approach. In this context, the methods of signal estimation and detection theory provide the theoretical limits on the decoding accuracy with which the stimulus can be identified. The Cramér-Rao lower bound on the decoding precision is widely used, since it can be evaluated easily once the mathematical model of the stimulus-response relationship is determined. However, little is known about the behavior of different decoding schemes with respect to the bound if the neuronal population size is limited. Main results. We show that under broad conditions the optimal decoding displays a threshold-like shift in performance in dependence on the population size. The onset of the threshold determines a critical range where a small increment in size, signal-to-noise ratio or observation time yields a dramatic gain in the decoding precision. Significance. We demonstrate the existence of such threshold regions in early auditory and olfactory information coding. We discuss the origin of the threshold effect and its impact on the design of effective coding approaches in terms of relevant population size.

Performance breakdown in optimal stimulus decoding.

PubMed

Lubomir Kostal; Lansky, Petr; Pilarski, Stevan

2015-06-01

One of the primary goals of neuroscience is to understand how neurons encode and process information about their environment. The problem is often approached indirectly by examining the degree to which the neuronal response reflects the stimulus feature of interest. In this context, the methods of signal estimation and detection theory provide the theoretical limits on the decoding accuracy with which the stimulus can be identified. The Cramér-Rao lower bound on the decoding precision is widely used, since it can be evaluated easily once the mathematical model of the stimulus-response relationship is determined. However, little is known about the behavior of different decoding schemes with respect to the bound if the neuronal population size is limited. We show that under broad conditions the optimal decoding displays a threshold-like shift in performance in dependence on the population size. The onset of the threshold determines a critical range where a small increment in size, signal-to-noise ratio or observation time yields a dramatic gain in the decoding precision. We demonstrate the existence of such threshold regions in early auditory and olfactory information coding. We discuss the origin of the threshold effect and its impact on the design of effective coding approaches in terms of relevant population size.

Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface

NASA Astrophysics Data System (ADS)

Sachs, Nicholas A.; Ruiz-Torres, Ricardo; Perreault, Eric J.; Miller, Lee E.

2016-02-01

Objective. It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. Approach. We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. Main results. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor’s proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. Significance. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the individual movement and posture decoders.

Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface.

PubMed

Sachs, Nicholas A; Ruiz-Torres, Ricardo; Perreault, Eric J; Miller, Lee E

2016-02-01

It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor's proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the individual movement and posture decoders.

To sort or not to sort: the impact of spike-sorting on neural decoding performance.

PubMed

Todorova, Sonia; Sadtler, Patrick; Batista, Aaron; Chase, Steven; Ventura, Valérie

2014-10-01

Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity. We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step. Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior. Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive methods used here. Even basic spike-sorting adds value to the low-threshold waveform-crossing methods often employed in BCI decoding.

To sort or not to sort: the impact of spike-sorting on neural decoding performance

NASA Astrophysics Data System (ADS)

Todorova, Sonia; Sadtler, Patrick; Batista, Aaron; Chase, Steven; Ventura, Valérie

2014-10-01

Objective. Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity. Approach. We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step. Main results. Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior. Significance. Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive methods used here. Even basic spike-sorting adds value to the low-threshold waveform-crossing methods often employed in BCI decoding.

Neural population encoding and decoding of sound source location across sound level in the rabbit inferior colliculus

PubMed Central

Delgutte, Bertrand

2015-01-01

At lower levels of sensory processing, the representation of a stimulus feature in the response of a neural population can vary in complex ways across different stimulus intensities, potentially changing the amount of feature-relevant information in the response. How higher-level neural circuits could implement feature decoding computations that compensate for these intensity-dependent variations remains unclear. Here we focused on neurons in the inferior colliculus (IC) of unanesthetized rabbits, whose firing rates are sensitive to both the azimuthal position of a sound source and its sound level. We found that the azimuth tuning curves of an IC neuron at different sound levels tend to be linear transformations of each other. These transformations could either increase or decrease the mutual information between source azimuth and spike count with increasing level for individual neurons, yet population azimuthal information remained constant across the absolute sound levels tested (35, 50, and 65 dB SPL), as inferred from the performance of a maximum-likelihood neural population decoder. We harnessed evidence of level-dependent linear transformations to reduce the number of free parameters in the creation of an accurate cross-level population decoder of azimuth. Interestingly, this decoder predicts monotonic azimuth tuning curves, broadly sensitive to contralateral azimuths, in neurons at higher levels in the auditory pathway. PMID:26490292

Recruitment of a Neuronal Ensemble in the Central Nucleus of the Amygdala Is Required for Alcohol Dependence.

PubMed

de Guglielmo, Giordano; Crawford, Elena; Kim, Sarah; Vendruscolo, Leandro F; Hope, Bruce T; Brennan, Molly; Cole, Maury; Koob, George F; George, Olivier

2016-09-07

Abstinence from alcohol is associated with the recruitment of neurons in the central nucleus of the amygdala (CeA) in nondependent rats that binge drink alcohol and in alcohol-dependent rats. However, whether the recruitment of this neuronal ensemble in the CeA is causally related to excessive alcohol drinking or if it represents a consequence of excessive drinking remains unknown. We tested the hypothesis that the recruitment of a neuronal ensemble in the CeA during abstinence is required for excessive alcohol drinking in nondependent rats that binge drink alcohol and in alcohol-dependent rats. We found that inactivation of the CeA neuronal ensemble during abstinence significantly decreased alcohol drinking in both groups. In nondependent rats, the decrease in alcohol intake was transient and returned to normal the day after the injection. In dependent rats, inactivation of the neuronal ensemble with Daun02 produced a long-term decrease in alcohol drinking. Moreover, we observed a significant reduction of somatic withdrawal signs in dependent animals that were injected with Daun02 in the CeA. These results indicate that the recruitment of a neuronal ensemble in the CeA during abstinence from alcohol is causally related to excessive alcohol drinking in alcohol-dependent rats, whereas a similar neuronal ensemble only partially contributed to alcohol-binge-like drinking in nondependent rats. These results identify a critical neurobiological mechanism that may be required for the transition to alcohol dependence, suggesting that focusing on the neuronal ensemble in the CeA may lead to a better understanding of the etiology of alcohol use disorders and improve medication development. Alcohol dependence recruits neurons in the central nucleus of the amygdala (CeA). Here, we found that inactivation of a specific dependence-induced neuronal ensemble in the CeA reversed excessive alcohol drinking and somatic signs of alcohol dependence in rats. These results identify a critical neurobiological mechanism that is required for alcohol dependence, suggesting that targeting dependence neuronal ensembles may lead to a better understanding of the etiology of alcohol use disorders, with implications for diagnosis, prevention, and treatment. Copyright © 2016 the authors 0270-6474/16/369446-08$15.00/0.

Genetic Feedback Regulation of Frontal Cortical Neuronal Ensembles Through Activity-Dependent Arc Expression and Dopaminergic Input.

PubMed

Mastwal, Surjeet; Cao, Vania; Wang, Kuan Hong

2016-01-01

Mental functions involve coordinated activities of specific neuronal ensembles that are embedded in complex brain circuits. Aberrant neuronal ensemble dynamics is thought to form the neurobiological basis of mental disorders. A major challenge in mental health research is to identify these cellular ensembles and determine what molecular mechanisms constrain their emergence and consolidation during development and learning. Here, we provide a perspective based on recent studies that use activity-dependent gene Arc/Arg3.1 as a cellular marker to identify neuronal ensembles and a molecular probe to modulate circuit functions. These studies have demonstrated that the transcription of Arc is activated in selective groups of frontal cortical neurons in response to specific behavioral tasks. Arc expression regulates the persistent firing of individual neurons and predicts the consolidation of neuronal ensembles during repeated learning. Therefore, the Arc pathway represents a prototypical example of activity-dependent genetic feedback regulation of neuronal ensembles. The activation of this pathway in the frontal cortex starts during early postnatal development and requires dopaminergic (DA) input. Conversely, genetic disruption of Arc leads to a hypoactive mesofrontal dopamine circuit and its related cognitive deficit. This mutual interaction suggests an auto-regulatory mechanism to amplify the impact of neuromodulators and activity-regulated genes during postnatal development. Such a mechanism may contribute to the association of mutations in dopamine and Arc pathways with neurodevelopmental psychiatric disorders. As the mesofrontal dopamine circuit shows extensive activity-dependent developmental plasticity, activity-guided modulation of DA projections or Arc ensembles during development may help to repair circuit deficits related to neuropsychiatric disorders.

Specific CA3 neurons decode neural information of dentate granule cells evoked by paired-pulse stimulation in co-cultured networks.

PubMed

Poli, Daniele; DeMarse, Thomas B; Wheeler, Bruce C; Brewer, Gregory J

2017-07-01

CA3 and dentate gyrus (DG) neurons are cultured in two-chamber devices on multi-electrode arrays (MEAs) and connected via micro-tunnels. In order to evoke time-locked activity, paired-pulse stimulation is applied to 22 different sites and repeated 25 times in each well in 5 MEA co-cultures and results compared to CA3-CA3 and DG-DG networks homologous controls. In these hippocampal sub-regions, we focus on the mechanisms underpinning a network's ability to decode the identity of site specific stimulation from analysis of evoked network responses using a support vector machine classifier. Our results indicate that a pool of CA3 neurons is able to reliably decode the identity of DG stimulation site information.

Parameter as a Switch Between Dynamical States of a Network in Population Decoding.

PubMed

Yu, Jiali; Mao, Hua; Yi, Zhang

2017-04-01

Population coding is a method to represent stimuli using the collective activities of a number of neurons. Nevertheless, it is difficult to extract information from these population codes with the noise inherent in neuronal responses. Moreover, it is a challenge to identify the right parameter of the decoding model, which plays a key role for convergence. To address the problem, a population decoding model is proposed for parameter selection. Our method successfully identified the key conditions for a nonzero continuous attractor. Both the theoretical analysis and the application studies demonstrate the correctness and effectiveness of this strategy.

Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation

PubMed Central

Młynarski, Wiktor

2014-01-01

To date a number of studies have shown that receptive field shapes of early sensory neurons can be reproduced by optimizing coding efficiency of natural stimulus ensembles. A still unresolved question is whether the efficient coding hypothesis explains formation of neurons which explicitly represent environmental features of different functional importance. This paper proposes that the spatial selectivity of higher auditory neurons emerges as a direct consequence of learning efficient codes for natural binaural sounds. Firstly, it is demonstrated that a linear efficient coding transform—Independent Component Analysis (ICA) trained on spectrograms of naturalistic simulated binaural sounds extracts spatial information present in the signal. A simple hierarchical ICA extension allowing for decoding of sound position is proposed. Furthermore, it is shown that units revealing spatial selectivity can be learned from a binaural recording of a natural auditory scene. In both cases a relatively small subpopulation of learned spectrogram features suffices to perform accurate sound localization. Representation of the auditory space is therefore learned in a purely unsupervised way by maximizing the coding efficiency and without any task-specific constraints. This results imply that efficient coding is a useful strategy for learning structures which allow for making behaviorally vital inferences about the environment. PMID:24639644

Designing a deep brain stimulator to suppress pathological neuronal synchrony.

PubMed

Montaseri, Ghazal; Yazdanpanah, Mohammad Javad; Bahrami, Fariba

2015-03-01

Some of neuropathologies are believed to be related to abnormal synchronization of neurons. In the line of therapy, designing effective deep brain stimulators to suppress the pathological synchrony among neuronal ensembles is a challenge of high clinical relevance. The stimulation should be able to disrupt the synchrony in the presence of latencies due to imperfect knowledge about parameters of a neuronal ensemble and stimulation impacts on the ensemble. We propose an adaptive desynchronizing deep brain stimulator capable of dealing with these uncertainties. We analyze the collective behavior of the stimulated neuronal ensemble and show that, using the designed stimulator, the resulting asynchronous state is stable. Simulation results reveal the efficiency of the proposed technique. Copyright © 2014 Elsevier Ltd. All rights reserved.

Nonspatial Sequence Coding in CA1 Neurons

PubMed Central

Allen, Timothy A.; Salz, Daniel M.; McKenzie, Sam

2016-01-01

The hippocampus is critical to the memory for sequences of events, a defining feature of episodic memory. However, the fundamental neuronal mechanisms underlying this capacity remain elusive. While considerable research indicates hippocampal neurons can represent sequences of locations, direct evidence of coding for the memory of sequential relationships among nonspatial events remains lacking. To address this important issue, we recorded neural activity in CA1 as rats performed a hippocampus-dependent sequence-memory task. Briefly, the task involves the presentation of repeated sequences of odors at a single port and requires rats to identify each item as “in sequence” or “out of sequence”. We report that, while the animals' location and behavior remained constant, hippocampal activity differed depending on the temporal context of items—in this case, whether they were presented in or out of sequence. Some neurons showed this effect across items or sequence positions (general sequence cells), while others exhibited selectivity for specific conjunctions of item and sequence position information (conjunctive sequence cells) or for specific probe types (probe-specific sequence cells). We also found that the temporal context of individual trials could be accurately decoded from the activity of neuronal ensembles, that sequence coding at the single-cell and ensemble level was linked to sequence memory performance, and that slow-gamma oscillations (20–40 Hz) were more strongly modulated by temporal context and performance than theta oscillations (4–12 Hz). These findings provide compelling evidence that sequence coding extends beyond the domain of spatial trajectories and is thus a fundamental function of the hippocampus. SIGNIFICANCE STATEMENT The ability to remember the order of life events depends on the hippocampus, but the underlying neural mechanisms remain poorly understood. Here we addressed this issue by recording neural activity in hippocampal region CA1 while rats performed a nonspatial sequence memory task. We found that hippocampal neurons code for the temporal context of items (whether odors were presented in the correct or incorrect sequential position) and that this activity is linked with memory performance. The discovery of this novel form of temporal coding in hippocampal neurons advances our fundamental understanding of the neurobiology of episodic memory and will serve as a foundation for our cross-species, multitechnique approach aimed at elucidating the neural mechanisms underlying memory impairments in aging and dementia. PMID:26843637

Characterizing ISI and sub-threshold membrane potential distributions: Ensemble of IF neurons with random squared-noise intensity.

PubMed

Kumar, Sanjeev; Karmeshu

2018-04-01

A theoretical investigation is presented that characterizes the emerging sub-threshold membrane potential and inter-spike interval (ISI) distributions of an ensemble of IF neurons that group together and fire together. The squared-noise intensity σ 2 of the ensemble of neurons is treated as a random variable to account for the electrophysiological variations across population of nearly identical neurons. Employing superstatistical framework, both ISI distribution and sub-threshold membrane potential distribution of neuronal ensemble are obtained in terms of generalized K-distribution. The resulting distributions exhibit asymptotic behavior akin to stretched exponential family. Extensive simulations of the underlying SDE with random σ 2 are carried out. The results are found to be in excellent agreement with the analytical results. The analysis has been extended to cover the case corresponding to independent random fluctuations in drift in addition to random squared-noise intensity. The novelty of the proposed analytical investigation for the ensemble of IF neurons is that it yields closed form expressions of probability distributions in terms of generalized K-distribution. Based on a record of spiking activity of thousands of neurons, the findings of the proposed model are validated. The squared-noise intensity σ 2 of identified neurons from the data is found to follow gamma distribution. The proposed generalized K-distribution is found to be in excellent agreement with that of empirically obtained ISI distribution of neuronal ensemble. Copyright © 2018 Elsevier B.V. All rights reserved.

A computationally efficient method for incorporating spike waveform information into decoding algorithms.

PubMed

Ventura, Valérie; Todorova, Sonia

2015-05-01

Spike-based brain-computer interfaces (BCIs) have the potential to restore motor ability to people with paralysis and amputation, and have shown impressive performance in the lab. To transition BCI devices from the lab to the clinic, decoding must proceed automatically and in real time, which prohibits the use of algorithms that are computationally intensive or require manual tweaking. A common choice is to avoid spike sorting and treat the signal on each electrode as if it came from a single neuron, which is fast, easy, and therefore desirable for clinical use. But this approach ignores the kinematic information provided by individual neurons recorded on the same electrode. The contribution of this letter is a linear decoding model that extracts kinematic information from individual neurons without spike-sorting the electrode signals. The method relies on modeling sample averages of waveform features as functions of kinematics, which is automatic and requires minimal data storage and computation. In offline reconstruction of arm trajectories of a nonhuman primate performing reaching tasks, the proposed method performs as well as decoders based on expertly manually and automatically sorted spikes.

Inhibition of propofol on single neuron and neuronal ensemble activity in prefrontal cortex of rats during working memory task.

PubMed

Xu, Xinyu; Tian, Yu; Wang, Guolin; Tian, Xin

2014-08-15

Working memory (WM) refers to the temporary storage and manipulation of information necessary for performance of complex cognitive tasks. There is a growing interest in whether and how propofol anesthesia inhibits WM function. The aim of this study is to investigate the possible inhibition mechanism of propofol anesthesia from the view of single neuron and neuronal ensemble activities. Adult SD rats were randomly divided into two groups: propofol group (0.9 mg kg(-1)min(-1), 2h via a tail vein catheter) and control group. All the rats were tested for working memory performances in a Y-maze-rewarded alternation task (a task of delayed non-matched-to-sample) at 24, 48, 72 h after propofol anesthesia, and the behavior results of WM tasks were recorded at the same time. Spatio-temporal trains of action potentials were obtained from the original signals. Single neuron activity was characterized by peri-event time histograms analysis and neuron ensemble activities were characterized by Granger causality to describe the interactions within the neuron ensemble. The results show that: comparing with the control group, the percentage of neurons excited and related to WM was significantly decreased (p<0.01 in 24h, p<0.05 in 48 h); the interactions within neuron ensemble were significantly weakened (p<0.01 in 24h, p<0.05 in 48 h), whereas no significant difference in 72 h (p>0.05), which were consistent with the behavior results. These findings could lead to improved understanding of the mechanism of anesthesia inhibition on WM functions from the view of single neuron activity and neuron ensemble interactions. Copyright © 2014 Elsevier B.V. All rights reserved.

A systematic approach to selecting task relevant neurons.

PubMed

Kahn, Kevin; Saxena, Shreya; Eskandar, Emad; Thakor, Nitish; Schieber, Marc; Gale, John T; Averbeck, Bruno; Eden, Uri; Sarma, Sridevi V

2015-04-30

Since task related neurons cannot be specifically targeted during surgery, a critical decision to make is to select which neurons are task-related when performing data analysis. Including neurons unrelated to the task degrade decoding accuracy and confound neurophysiological results. Traditionally, task-related neurons are selected as those with significant changes in firing rate when a stimulus is applied. However, this assumes that neurons' encoding of stimuli are dominated by their firing rate with little regard to temporal dynamics. This paper proposes a systematic approach for neuron selection, which uses a likelihood ratio test to capture the contribution of stimulus to spiking activity while taking into account task-irrelevant intrinsic dynamics that affect firing rates. This approach is denoted as the model deterioration excluding stimulus (MDES) test. MDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples. The MDES rankings in the simulation match closely with ideal rankings, while firing rate rankings are skewed by task-irrelevant parameters. For decoding, 95% accuracy is achieved using the top 8 MDES-ranked neurons, while the top 12 firing-rate ranked neurons are needed. In the neurophysiological examples, MDES matches published results when firing rates do encode salient stimulus information, and uncovers oscillatory modulations in task-related neurons that are not captured when neurons are selected using firing rates. These case studies illustrate the importance of accounting for intrinsic dynamics when selecting task-related neurons and following the MDES approach accomplishes that. MDES selects neurons that encode task-related information irrespective of these intrinsic dynamics which can bias firing rate based selection. Copyright © 2015 Elsevier B.V. All rights reserved.

Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices

PubMed Central

Vargas-Irwin, Carlos E.; Truccolo, Wilson; Donoghue, John P.

2011-01-01

A prominent feature of motor cortex field potentials during movement is a distinctive low-frequency local field potential (lf-LFP) (<4 Hz), referred to as the movement event-related potential (mEP). The lf-LFP appears to be a global signal related to regional synaptic input, but its relationship to nearby output signaled by single unit spiking activity (SUA) or to movement remains to be established. Previous studies comparing information in primary motor cortex (MI) lf-LFPs and SUA in the context of planar reaching tasks concluded that lf-LFPs have more information than spikes about movement. However, the relative performance of these signals was based on a small number of simultaneously recorded channels and units, or for data averaged across sessions, which could miss information of larger-scale spiking populations. Here, we simultaneously recorded LFPs and SUA from two 96-microelectrode arrays implanted in two major motor cortical areas, MI and ventral premotor (PMv), while monkeys freely reached for and grasped objects swinging in front of them. We compared arm end point and grip aperture kinematics′ decoding accuracy for lf-LFP and SUA ensembles. The results show that lf-LFPs provide enough information to reconstruct kinematics in both areas with little difference in decoding performance between MI and PMv. Individual lf-LFP channels often provided more accurate decoding of single kinematic variables than any one single unit. However, the decoding performance of the best single unit among the large population usually exceeded that of the best single lf-LFP channel. Furthermore, ensembles of SUA outperformed the pool of lf-LFP channels, in disagreement with the previously reported superiority of lf-LFP decoding. Decoding results suggest that information in lf-LFPs recorded from intracortical arrays may allow the reconstruction of reach and grasp for real-time neuroprosthetic applications, thus potentially supplementing the ability to decode these same features from spiking populations. PMID:21273313

Comparison of Classifiers for Decoding Sensory and Cognitive Information from Prefrontal Neuronal Populations

PubMed Central

Astrand, Elaine; Enel, Pierre; Ibos, Guilhem; Dominey, Peter Ford; Baraduc, Pierre; Ben Hamed, Suliann

2014-01-01

Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders. PMID:24466019

Distinct spatiotemporal activity in principal neurons of the mouse olfactory bulb in anesthetized and awake states

PubMed Central

Blauvelt, David G.; Sato, Tomokazu F.; Wienisch, Martin; Murthy, Venkatesh N.

2013-01-01

The acquisition of olfactory information and its early processing in mammals are modulated by brain states through sniffing behavior and neural feedback. We imaged the spatiotemporal pattern of odor-evoked activity in a population of output neurons (mitral/tufted cells, MTCs) in the olfactory bulb (OB) of head-restrained mice expressing a genetically-encoded calcium indicator. The temporal dynamics of MTC population activity were relatively simple in anesthetized animals, but were highly variable in awake animals. However, the apparently irregular activity in awake animals could be predicted well using sniff timing measured externally, or inferred through fluctuations in the global responses of MTC population even without explicit knowledge of sniff times. The overall spatial pattern of activity was conserved across states, but odor responses had a diffuse spatial component in anesthetized mice that was less prominent during wakefulness. Multi-photon microscopy indicated that MTC lateral dendrites were the likely source of spatially disperse responses in the anesthetized animal. Our data demonstrate that the temporal and spatial dynamics of MTCs can be significantly modulated by behavioral state, and that the ensemble activity of MTCs can provide information about sniff timing to downstream circuits to help decode odor responses. PMID:23543674

Desynchronization in an ensemble of globally coupled chaotic bursting neuronal oscillators by dynamic delayed feedback control

NASA Astrophysics Data System (ADS)

Che, Yanqiu; Yang, Tingting; Li, Ruixue; Li, Huiyan; Han, Chunxiao; Wang, Jiang; Wei, Xile

2015-09-01

In this paper, we propose a dynamic delayed feedback control approach or desynchronization of chaotic-bursting synchronous activities in an ensemble of globally coupled neuronal oscillators. We demonstrate that the difference signal between an ensemble's mean field and its time delayed state, filtered and fed back to the ensemble, can suppress the self-synchronization in the ensemble. These individual units are decoupled and stabilized at the desired desynchronized states while the stimulation signal reduces to the noise level. The effectiveness of the method is illustrated by examples of two different populations of globally coupled chaotic-bursting neurons. The proposed method has potential for mild, effective and demand-controlled therapy of neurological diseases characterized by pathological synchronization.

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.

PubMed

Kamrunnahar, M; Schiff, S J

2011-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%-90% for the hand movements and 70%-90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.

Large-scale recording of neuronal ensembles.

PubMed

Buzsáki, György

2004-05-01

How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.

Modulation Depth Estimation and Variable Selection in State-Space Models for Neural Interfaces

PubMed Central

Hochberg, Leigh R.; Donoghue, John P.; Brown, Emery N.

2015-01-01

Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems. PMID:25265627

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout

PubMed Central

Clery, Stephane; Cumming, Bruce G.

2017-01-01

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal “noise” correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. SIGNIFICANCE STATEMENT Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain. PMID:28100751

A brain-machine interface enables bimanual arm movements in monkeys.

PubMed

Ifft, Peter J; Shokur, Solaiman; Li, Zheng; Lebedev, Mikhail A; Nicolelis, Miguel A L

2013-11-06

Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to paralyzed patients. So far, BMIs have enabled only one arm to be moved at a time. Control of bimanual arm movements remains a major challenge. We have developed and tested a bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374 to 497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a fifth-order unscented Kalman filter (UKF). The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals' performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. These findings should help in the design of more sophisticated BMIs capable of enabling bimanual motor control in human patients.

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

PubMed Central

Logiaco, Laureline; Quilodran, René; Procyk, Emmanuel; Arleo, Angelo

2015-01-01

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators. PMID:26266537

Protograph based LDPC codes with minimum distance linearly growing with block size

NASA Technical Reports Server (NTRS)

Divsalar, Dariush; Jones, Christopher; Dolinar, Sam; Thorpe, Jeremy

2005-01-01

We propose several LDPC code constructions that simultaneously achieve good threshold and error floor performance. Minimum distance is shown to grow linearly with block size (similar to regular codes of variable degree at least 3) by considering ensemble average weight enumerators. Our constructions are based on projected graph, or protograph, structures that support high-speed decoder implementations. As with irregular ensembles, our constructions are sensitive to the proportion of degree-2 variable nodes. A code with too few such nodes tends to have an iterative decoding threshold that is far from the capacity threshold. A code with too many such nodes tends to not exhibit a minimum distance that grows linearly in block length. In this paper we also show that precoding can be used to lower the threshold of regular LDPC codes. The decoding thresholds of the proposed codes, which have linearly increasing minimum distance in block size, outperform that of regular LDPC codes. Furthermore, a family of low to high rate codes, with thresholds that adhere closely to their respective channel capacity thresholds, is presented. Simulation results for a few example codes show that the proposed codes have low error floors as well as good threshold SNFt performance.

Noise effects on robust synchronization of a small pacemaker neuronal ensemble via nonlinear controller: electronic circuit design.

PubMed

Megam Ngouonkadi, Elie Bertrand; Fotsin, Hilaire Bertrand; Kabong Nono, Martial; Louodop Fotso, Patrick Herve

2016-10-01

In this paper, we report on the synchronization of a pacemaker neuronal ensemble constituted of an AB neuron electrically coupled to two PD neurons. By the virtue of this electrical coupling, they can fire synchronous bursts of action potential. An external master neuron is used to induce to the whole system the desired dynamics, via a nonlinear controller. Such controller is obtained by a combination of sliding mode and feedback control. The proposed controller is able to offset uncertainties in the synchronized systems. We show how noise affects the synchronization of the pacemaker neuronal ensemble, and briefly discuss its potential benefits in our synchronization scheme. An extended Hindmarsh-Rose neuronal model is used to represent a single cell dynamic of the network. Numerical simulations and Pspice implementation of the synchronization scheme are presented. We found that, the proposed controller reduces the stochastic resonance of the network when its gain increases.

Signal acquisition and analysis for cortical control of neuroprosthetics.

PubMed

Tillery, Stephen I Helms; Taylor, Dawn M

2004-12-01

Work in cortically controlled neuroprosthetic systems has concentrated on decoding natural behaviors from neural activity, with the idea that if the behavior could be fully decoded it could be duplicated using an artificial system. Initial estimates from this approach suggested that a high-fidelity signal comprised of many hundreds of neurons would be required to control a neuroprosthetic system successfully. However, recent studies are showing hints that these systems can be controlled effectively using only a few tens of neurons. Attempting to decode the pre-existing relationship between neural activity and natural behavior is not nearly as important as choosing a decoding scheme that can be more readily deployed and trained to generate the desired actions of the artificial system. These artificial systems need not resemble or behave similarly to any natural biological system. Effective matching of discrete and continuous neural command signals to appropriately configured device functions will enable effective control of both natural and abstract artificial systems using compatible thought processes.

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface

PubMed Central

Kamrunnahar, M.; Schiff, S. J.

2017-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%–90% for the hand movements and 70%–90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models. PMID:22255799

Subcortical neuronal ensembles: an analysis of motor task association, tremor, oscillations, and synchrony in human patients.

PubMed

Hanson, Timothy L; Fuller, Andrew M; Lebedev, Mikhail A; Turner, Dennis A; Nicolelis, Miguel A L

2012-06-20

Deep brain stimulation (DBS) has expanded as an effective treatment for motor disorders, providing a valuable opportunity for intraoperative recording of the spiking activity of subcortical neurons. The properties of these neurons and their potential utility in neuroprosthetic applications are not completely understood. During DBS surgeries in 25 human patients with either essential tremor or Parkinson's disease, we acutely recorded the single-unit activity of 274 ventral intermediate/ventral oralis posterior motor thalamus (Vim/Vop) neurons and 123 subthalamic nucleus (STN) neurons. These subcortical neuronal ensembles (up to 23 neurons sampled simultaneously) were recorded while the patients performed a target-tracking motor task using a cursor controlled by a haptic glove. We observed that modulations in firing rate of a substantial number of neurons in both Vim/Vop and STN represented target onset, movement onset/direction, and hand tremor. Neurons in both areas exhibited rhythmic oscillations and pairwise synchrony. Notably, all tremor-associated neurons exhibited synchrony within the ensemble. The data further indicate that oscillatory (likely pathological) neurons and behaviorally tuned neurons are not distinct but rather form overlapping sets. Whereas previous studies have reported a linear relationship between power spectra of neuronal oscillations and hand tremor, we report a nonlinear relationship suggestive of complex encoding schemes. Even in the presence of this pathological activity, linear models were able to extract motor parameters from ensemble discharges. Based on these findings, we propose that chronic multielectrode recordings from Vim/Vop and STN could prove useful for further studying, monitoring, and even treating motor disorders.

Multiplicative mixing of object identity and image attributes in single inferior temporal neurons.

PubMed

Ratan Murty, N Apurva; Arun, S P

2018-04-03

Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively. Copyright © 2018 the Author(s). Published by PNAS.

A shared neural ensemble links distinct contextual memories encoded close in time

NASA Astrophysics Data System (ADS)

Cai, Denise J.; Aharoni, Daniel; Shuman, Tristan; Shobe, Justin; Biane, Jeremy; Song, Weilin; Wei, Brandon; Veshkini, Michael; La-Vu, Mimi; Lou, Jerry; Flores, Sergio E.; Kim, Isaac; Sano, Yoshitake; Zhou, Miou; Baumgaertel, Karsten; Lavi, Ayal; Kamata, Masakazu; Tuszynski, Mark; Mayford, Mark; Golshani, Peyman; Silva, Alcino J.

2016-06-01

Recent studies suggest that a shared neural ensemble may link distinct memories encoded close in time. According to the memory allocation hypothesis, learning triggers a temporary increase in neuronal excitability that biases the representation of a subsequent memory to the neuronal ensemble encoding the first memory, such that recall of one memory increases the likelihood of recalling the other memory. Here we show in mice that the overlap between the hippocampal CA1 ensembles activated by two distinct contexts acquired within a day is higher than when they are separated by a week. Several findings indicate that this overlap of neuronal ensembles links two contextual memories. First, fear paired with one context is transferred to a neutral context when the two contexts are acquired within a day but not across a week. Second, the first memory strengthens the second memory within a day but not across a week. Older mice, known to have lower CA1 excitability, do not show the overlap between ensembles, the transfer of fear between contexts, or the strengthening of the second memory. Finally, in aged mice, increasing cellular excitability and activating a common ensemble of CA1 neurons during two distinct context exposures rescued the deficit in linking memories. Taken together, these findings demonstrate that contextual memories encoded close in time are linked by directing storage into overlapping ensembles. Alteration of these processes by ageing could affect the temporal structure of memories, thus impairing efficient recall of related information.

Decoding emotional valence from electroencephalographic rhythmic activity.

PubMed

Celikkanat, Hande; Moriya, Hiroki; Ogawa, Takeshi; Kauppi, Jukka-Pekka; Kawanabe, Motoaki; Hyvarinen, Aapo

2017-07-01

We attempt to decode emotional valence from electroencephalographic rhythmic activity in a naturalistic setting. We employ a data-driven method developed in a previous study, Spectral Linear Discriminant Analysis, to discover the relationships between the classification task and independent neuronal sources, optimally utilizing multiple frequency bands. A detailed investigation of the classifier provides insight into the neuronal sources related with emotional valence, and the individual differences of the subjects in processing emotions. Our findings show: (1) sources whose locations are similar across subjects are consistently involved in emotional responses, with the involvement of parietal sources being especially significant, and (2) even though the locations of the involved neuronal sources are consistent, subjects can display highly varying degrees of valence-related EEG activity in the sources.

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout.

PubMed

Clery, Stephane; Cumming, Bruce G; Nienborg, Hendrikje

2017-01-18

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal "noise" correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain. Copyright © 2017 the authors 0270-6474/17/370715-11$15.00/0.

Flexible categorization of relative stimulus strength by the optic tectum

PubMed Central

Mysore, Shreesh P.; Knudsen, Eric I.

2011-01-01

Categorization is the process by which the brain segregates continuously variable stimuli into discrete groups. We report that patterns of neural population activity in the owl optic tectum (OT) categorize stimuli based on their relative strengths into “strongest” versus “other”. The category boundary shifts adaptively to track changes in the absolute strength of the strongest stimulus. This population-wide categorization is mediated by the responses of a small subset of neurons. Our data constitute the first direct demonstration of an explicit categorization of stimuli by a neural network based on relative stimulus strength or salience. The finding of categorization by the population code relaxes constraints on the properties of downstream decoders that might read out the location of the strongest stimulus. These results indicate that the ensemble neural code in the OT could mediate bottom-up stimulus selection for gaze and attention, a form of stimulus categorization in which the category boundary often shifts within hundreds of milliseconds. PMID:21613487

Associative-memory representations emerge as shared spatial patterns of theta activity spanning the primate temporal cortex

PubMed Central

Nakahara, Kiyoshi; Adachi, Ken; Kawasaki, Keisuke; Matsuo, Takeshi; Sawahata, Hirohito; Majima, Kei; Takeda, Masaki; Sugiyama, Sayaka; Nakata, Ryota; Iijima, Atsuhiko; Tanigawa, Hisashi; Suzuki, Takafumi; Kamitani, Yukiyasu; Hasegawa, Isao

2016-01-01

Highly localized neuronal spikes in primate temporal cortex can encode associative memory; however, whether memory formation involves area-wide reorganization of ensemble activity, which often accompanies rhythmicity, or just local microcircuit-level plasticity, remains elusive. Using high-density electrocorticography, we capture local-field potentials spanning the monkey temporal lobes, and show that the visual pair-association (PA) memory is encoded in spatial patterns of theta activity in areas TE, 36, and, partially, in the parahippocampal cortex, but not in the entorhinal cortex. The theta patterns elicited by learned paired associates are distinct between pairs, but similar within pairs. This pattern similarity, emerging through novel PA learning, allows a machine-learning decoder trained on theta patterns elicited by a particular visual item to correctly predict the identity of those elicited by its paired associate. Our results suggest that the formation and sharing of widespread cortical theta patterns via learning-induced reorganization are involved in the mechanisms of associative memory representation. PMID:27282247

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.

PubMed

Ranganayaki, V; Deepa, S N

2016-01-01

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

PubMed Central

Ranganayaki, V.; Deepa, S. N.

2016-01-01

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature. PMID:27034973

Synaptic plasticity associated with a memory engram in the basolateral amygdala.

PubMed

Nonaka, Ayako; Toyoda, Takeshi; Miura, Yuki; Hitora-Imamura, Natsuko; Naka, Masamitsu; Eguchi, Megumi; Yamaguchi, Shun; Ikegaya, Yuji; Matsuki, Norio; Nomura, Hiroshi

2014-07-09

Synaptic plasticity is a cellular mechanism putatively underlying learning and memory. However, it is unclear whether learning induces synaptic modification globally or only in a subset of neurons in associated brain regions. In this study, we genetically identified neurons activated during contextual fear learning and separately recorded synaptic efficacy from recruited and nonrecruited neurons in the mouse basolateral amygdala (BLA). We found that the fear learning induces presynaptic potentiation, which was reflected by an increase in the miniature EPSC frequency and by a decrease in the paired-pulse ratio. Changes occurred only in the cortical synapses targeting the BLA neurons that were recruited into the fear memory trace. Furthermore, we found that fear learning reorganizes the neuronal ensemble responsive to the conditioning context in conjunction with the synaptic plasticity. In particular, the neuronal activity during learning was associated with the neuronal recruitment into the context-responsive ensemble. These findings suggest that synaptic plasticity in a subset of BLA neurons contributes to fear memory expression through ensemble reorganization. Copyright © 2014 the authors 0270-6474/14/349305-05$15.00/0.

Cortical Decoding of Individual Finger and Wrist Kinematics for an Upper-Limb Neuroprosthesis

PubMed Central

Aggarwal, Vikram; Tenore, Francesco; Acharya, Soumyadipta; Schieber, Marc H.; Thakor, Nitish V.

2010-01-01

Previous research has shown that neuronal activity can be used to continuously decode the kinematics of gross movements involving arm and hand trajectory. However, decoding the kinematics of fine motor movements, such as the manipulation of individual fingers, has not been demonstrated. In this study, single unit activities were recorded from task-related neurons in M1 of two trained rhesus monkey as they performed individuated movements of the fingers and wrist. The primates’ hand was placed in a manipulandum, and strain gauges at the tips of each finger were used to track the digit’s position. Both linear and non-linear filters were designed to simultaneously predict kinematics of each digit and the wrist, and their performance compared using mean squared error and correlation coefficients. All models had high decoding accuracy, but the feedforward ANN (R=0.76–0.86, MSE=0.04–0.05) and Kalman filter (R=0.68–0.86, MSE=0.04–0.07) performed better than a simple linear regression filter (0.58–0.81, 0.05–0.07). These results suggest that individual finger and wrist kinematics can be decoded with high accuracy, and be used to control a multi-fingered prosthetic hand in real-time. PMID:19964645

A robust activity marking system for exploring active neuronal ensembles

PubMed Central

Sørensen, Andreas T; Cooper, Yonatan A; Baratta, Michael V; Weng, Feng-Ju; Zhang, Yuxiang; Ramamoorthi, Kartik; Fropf, Robin; LaVerriere, Emily; Xue, Jian; Young, Andrew; Schneider, Colleen; Gøtzsche, Casper René; Hemberg, Martin; Yin, Jerry CP; Maier, Steven F; Lin, Yingxi

2016-01-01

Understanding how the brain captures transient experience and converts it into long lasting changes in neural circuits requires the identification and investigation of the specific ensembles of neurons that are responsible for the encoding of each experience. We have developed a Robust Activity Marking (RAM) system that allows for the identification and interrogation of ensembles of neurons. The RAM system provides unprecedented high sensitivity and selectivity through the use of an optimized synthetic activity-regulated promoter that is strongly induced by neuronal activity and a modified Tet-Off system that achieves improved temporal control. Due to its compact design, RAM can be packaged into a single adeno-associated virus (AAV), providing great versatility and ease of use, including application to mice, rats, flies, and potentially many other species. Cre-dependent RAM, CRAM, allows for the study of active ensembles of a specific cell type and anatomical connectivity, further expanding the RAM system’s versatility. DOI: http://dx.doi.org/10.7554/eLife.13918.001 PMID:27661450

Investigating common coding of observed and executed actions in the monkey brain using cross-modal multi-variate fMRI classification.

PubMed

Fiave, Prosper Agbesi; Sharma, Saloni; Jastorff, Jan; Nelissen, Koen

2018-05-19

Mirror neurons are generally described as a neural substrate hosting shared representations of actions, by simulating or 'mirroring' the actions of others onto the observer's own motor system. Since single neuron recordings are rarely feasible in humans, it has been argued that cross-modal multi-variate pattern analysis (MVPA) of non-invasive fMRI data is a suitable technique to investigate common coding of observed and executed actions, allowing researchers to infer the presence of mirror neurons in the human brain. In an effort to close the gap between monkey electrophysiology and human fMRI data with respect to the mirror neuron system, here we tested this proposal for the first time in the monkey. Rhesus monkeys either performed reach-and-grasp or reach-and-touch motor acts with their right hand in the dark or observed videos of human actors performing similar motor acts. Unimodal decoding showed that both executed or observed motor acts could be decoded from numerous brain regions. Specific portions of rostral parietal, premotor and motor cortices, previously shown to house mirror neurons, in addition to somatosensory regions, yielded significant asymmetric action-specific cross-modal decoding. These results validate the use of cross-modal multi-variate fMRI analyses to probe the representations of own and others' actions in the primate brain and support the proposed mapping of others' actions onto the observer's own motor cortices. Copyright © 2018 Elsevier Inc. All rights reserved.

Cortical Specializations Underlying Fast Computations

PubMed Central

Volgushev, Maxim

2016-01-01

The time course of behaviorally relevant environmental events sets temporal constraints on neuronal processing. How does the mammalian brain make use of the increasingly complex networks of the neocortex, while making decisions and executing behavioral reactions within a reasonable time? The key parameter determining the speed of computations in neuronal networks is a time interval that neuronal ensembles need to process changes at their input and communicate results of this processing to downstream neurons. Theoretical analysis identified basic requirements for fast processing: use of neuronal populations for encoding, background activity, and fast onset dynamics of action potentials in neurons. Experimental evidence shows that populations of neocortical neurons fulfil these requirements. Indeed, they can change firing rate in response to input perturbations very quickly, within 1 to 3 ms, and encode high-frequency components of the input by phase-locking their spiking to frequencies up to 300 to 1000 Hz. This implies that time unit of computations by cortical ensembles is only few, 1 to 3 ms, which is considerably faster than the membrane time constant of individual neurons. The ability of cortical neuronal ensembles to communicate on a millisecond time scale allows for complex, multiple-step processing and precise coordination of neuronal activity in parallel processing streams, while keeping the speed of behavioral reactions within environmentally set temporal constraints. PMID:25689988

Chaos-induced modulation of reliability boosts output firing rate in downstream cortical areas.

PubMed

Tiesinga, P H E

2004-03-01

The reproducibility of neural spike train responses to an identical stimulus across different presentations (trials) has been studied extensively. Reliability, the degree of reproducibility of spike trains, was found to depend in part on the amplitude and frequency content of the stimulus [J. Hunter and J. Milton, J. Neurophysiol. 90, 387 (2003)]. The responses across different trials can sometimes be interpreted as the response of an ensemble of similar neurons to a single stimulus presentation. How does the reliability of the activity of neural ensembles affect information transmission between different cortical areas? We studied a model neural system consisting of two ensembles of neurons with Hodgkin-Huxley-type channels. The first ensemble was driven by an injected sinusoidal current that oscillated in the gamma-frequency range (40 Hz) and its output spike trains in turn drove the second ensemble by fast excitatory synaptic potentials with short term depression. We determined the relationship between the reliability of the first ensemble and the response of the second ensemble. In our paradigm the neurons in the first ensemble were initially in a chaotic state with unreliable and imprecise spike trains. The neurons became entrained to the oscillation and responded reliably when the stimulus power was increased by less than 10%. The firing rate of the first ensemble increased by 30%, whereas that of the second ensemble could increase by an order of magnitude. We also determined the response of the second ensemble when its input spike trains, which had non-Poisson statistics, were replaced by an equivalent ensemble of Poisson spike trains. The resulting output spike trains were significantly different from the original response, as assessed by the metric introduced by Victor and Purpura [J. Neurophysiol. 76, 1310 (1996)]. These results are a proof of principle that weak temporal modulations in the power of gamma-frequency oscillations in a given cortical area can strongly affect firing rate responses downstream by way of reliability in spite of rather modest changes in firing rate in the originating area.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

PubMed

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

PubMed Central

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive. PMID:28018162

Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems

PubMed Central

Malik, Wasim Q.; Truccolo, Wilson; Brown, Emery N.; Hochberg, Leigh R.

2011-01-01

The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5 ± 0.5 s (mean ± s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25 ± 3 single units by a factor of 7.0 ± 0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems. PMID:21078582

Computational properties of networks of synchronous groups of spiking neurons.

PubMed

Dayhoff, Judith E

2007-09-01

We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.

Regional Differences in Striatal Neuronal Ensemble Excitability Following Cocaine and Extinction Memory Retrieval in Fos-GFP Mice.

PubMed

Ziminski, Joseph J; Sieburg, Meike C; Margetts-Smith, Gabriella; Crombag, Hans S; Koya, Eisuke

2018-03-01

Learned associations between drugs of abuse and the drug administration environment have an important role in addiction. In rodents, exposure to a drug-associated environment elicits conditioned psychomotor activation, which may be weakened following extinction (EXT) learning. Although widespread drug-induced changes in neuronal excitability have been observed, little is known about specific changes within neuronal ensembles activated during the recall of drug-environment associations. Using a cocaine-conditioned locomotion (CL) procedure, the present study assessed the excitability of neuronal ensembles in the nucleus accumbens core and shell (NAc core and NAc shell ), and dorsal striatum (DS) following cocaine conditioning and EXT in Fos-GFP mice that express green fluorescent protein (GFP) in activated neurons (GFP+). During conditioning, mice received repeated cocaine injections (20 mg/kg) paired with a locomotor activity chamber (Paired) or home cage (Unpaired). Seven to 13 days later, both groups were re-exposed to the activity chamber under drug-free conditions and Paired, but not Unpaired, mice exhibited CL. In a separate group of mice, CL was extinguished by repeatedly exposing mice to the activity chamber under drug-free conditions. Following the expression and EXT of CL, GFP+ neurons in the NAc core (but not NAc shell and DS) displayed greater firing capacity compared to surrounding GFP- neurons. This difference in excitability was due to a generalized decrease in GFP- excitability following CL and a selective increase in GFP+ excitability following its EXT. These results suggest a role for both widespread and ensemble-specific changes in neuronal excitability following recall of drug-environment associations.

Neural decoding with kernel-based metric learning.

PubMed

Brockmeier, Austin J; Choi, John S; Kriminger, Evan G; Francis, Joseph T; Principe, Jose C

2014-06-01

In studies of the nervous system, the choice of metric for the neural responses is a pivotal assumption. For instance, a well-suited distance metric enables us to gauge the similarity of neural responses to various stimuli and assess the variability of responses to a repeated stimulus-exploratory steps in understanding how the stimuli are encoded neurally. Here we introduce an approach where the metric is tuned for a particular neural decoding task. Neural spike train metrics have been used to quantify the information content carried by the timing of action potentials. While a number of metrics for individual neurons exist, a method to optimally combine single-neuron metrics into multineuron, or population-based, metrics is lacking. We pose the problem of optimizing multineuron metrics and other metrics using centered alignment, a kernel-based dependence measure. The approach is demonstrated on invasively recorded neural data consisting of both spike trains and local field potentials. The experimental paradigm consists of decoding the location of tactile stimulation on the forepaws of anesthetized rats. We show that the optimized metrics highlight the distinguishing dimensions of the neural response, significantly increase the decoding accuracy, and improve nonlinear dimensionality reduction methods for exploratory neural analysis.

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

PubMed Central

Hochberg, Leigh R.; Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y.; Simeral, John D.; Vogel, Joern; Haddadin, Sami; Liu, Jie; Cash, Sydney S.; van der Smagt, Patrick; Donoghue, John P.

2012-01-01

Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS)1–5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices6–8. Able-bodied monkeys have used an NIS to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after CNS injury, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals. PMID:22596161

Spatial Correlations in Natural Scenes Modulate Response Reliability in Mouse Visual Cortex

PubMed Central

Rikhye, Rajeev V.

2015-01-01

Intrinsic neuronal variability significantly limits information encoding in the primary visual cortex (V1). Certain stimuli can suppress this intertrial variability to increase the reliability of neuronal responses. In particular, responses to natural scenes, which have broadband spatiotemporal statistics, are more reliable than responses to stimuli such as gratings. However, very little is known about which stimulus statistics modulate reliable coding and how this occurs at the neural ensemble level. Here, we sought to elucidate the role that spatial correlations in natural scenes play in reliable coding. We developed a novel noise-masking method to systematically alter spatial correlations in natural movies, without altering their edge structure. Using high-speed two-photon calcium imaging in vivo, we found that responses in mouse V1 were much less reliable at both the single neuron and population level when spatial correlations were removed from the image. This change in reliability was due to a reorganization of between-neuron correlations. Strongly correlated neurons formed ensembles that reliably and accurately encoded visual stimuli, whereas reducing spatial correlations reduced the activation of these ensembles, leading to an unreliable code. Together with an ensemble-specific normalization model, these results suggest that the coordinated activation of specific subsets of neurons underlies the reliable coding of natural scenes. SIGNIFICANCE STATEMENT The natural environment is rich with information. To process this information with high fidelity, V1 neurons have to be robust to noise and, consequentially, must generate responses that are reliable from trial to trial. While several studies have hinted that both stimulus attributes and population coding may reduce noise, the details remain unclear. Specifically, what features of natural scenes are important and how do they modulate reliability? This study is the first to investigate the role of spatial correlations, which are a fundamental attribute of natural scenes, in shaping stimulus coding by V1 neurons. Our results provide new insights into how stimulus spatial correlations reorganize the correlated activation of specific ensembles of neurons to ensure accurate information processing in V1. PMID:26511254

Temporal Context in Speech Processing and Attentional Stream Selection: A Behavioral and Neural perspective

PubMed Central

Zion Golumbic, Elana M.; Poeppel, David; Schroeder, Charles E.

2012-01-01

The human capacity for processing speech is remarkable, especially given that information in speech unfolds over multiple time scales concurrently. Similarly notable is our ability to filter out of extraneous sounds and focus our attention on one conversation, epitomized by the ‘Cocktail Party’ effect. Yet, the neural mechanisms underlying on-line speech decoding and attentional stream selection are not well understood. We review findings from behavioral and neurophysiological investigations that underscore the importance of the temporal structure of speech for achieving these perceptual feats. We discuss the hypothesis that entrainment of ambient neuronal oscillations to speech’s temporal structure, across multiple time-scales, serves to facilitate its decoding and underlies the selection of an attended speech stream over other competing input. In this regard, speech decoding and attentional stream selection are examples of ‘active sensing’, emphasizing an interaction between proactive and predictive top-down modulation of neuronal dynamics and bottom-up sensory input. PMID:22285024

Decoding synchronized oscillations within the brain: phase-delayed inhibition provides a robust mechanism for creating a sharp synchrony filter.

PubMed

Patel, Mainak; Joshi, Badal

2013-10-07

The widespread presence of synchronized neuronal oscillations within the brain suggests that a mechanism must exist that is capable of decoding such activity. Two realistic designs for such a decoder include: (1) a read-out neuron with a high spike threshold, or (2) a phase-delayed inhibition network motif. Despite requiring a more elaborate network architecture, phase-delayed inhibition has been observed in multiple systems, suggesting that it may provide inherent advantages over simply imposing a high spike threshold. In this work, we use a computational and mathematical approach to investigate the efficacy of the phase-delayed inhibition motif in detecting synchronized oscillations. We show that phase-delayed inhibition is capable of creating a synchrony detector with sharp synchrony filtering properties that depend critically on the time course of inputs. Additionally, we show that phase-delayed inhibition creates a synchrony filter that is far more robust than that created by a high spike threshold. Copyright © 2013 Elsevier Ltd. All rights reserved.

Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements

PubMed Central

Ma, Xuan; Ma, Chaolin; Huang, Jian; Zhang, Peng; Xu, Jiang; He, Jiping

2017-01-01

Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed algorithms. Our findings provide new insights for extending current BMI design concepts and techniques on upper limbs to lower limb circumstances. Brain controlled exoskeleton, prostheses or neuromuscular electrical stimulators for lower limbs are expected to be developed, which enables the subject to manipulate complex biomechatronic devices with mind in more harmonized manner. PMID:28223914

A simple approach to ignoring irrelevant variables by population decoding based on multisensory neurons

PubMed Central

Kim, HyungGoo R.; Pitkow, Xaq; Angelaki, Dora E.

2016-01-01

Sensory input reflects events that occur in the environment, but multiple events may be confounded in sensory signals. For example, under many natural viewing conditions, retinal image motion reflects some combination of self-motion and movement of objects in the world. To estimate one stimulus event and ignore others, the brain can perform marginalization operations, but the neural bases of these operations are poorly understood. Using computational modeling, we examine how multisensory signals may be processed to estimate the direction of self-motion (i.e., heading) and to marginalize out effects of object motion. Multisensory neurons represent heading based on both visual and vestibular inputs and come in two basic types: “congruent” and “opposite” cells. Congruent cells have matched heading tuning for visual and vestibular cues and have been linked to perceptual benefits of cue integration during heading discrimination. Opposite cells have mismatched visual and vestibular heading preferences and are ill-suited for cue integration. We show that decoding a mixed population of congruent and opposite cells substantially reduces errors in heading estimation caused by object motion. In addition, we present a general formulation of an optimal linear decoding scheme that approximates marginalization and can be implemented biologically by simple reinforcement learning mechanisms. We also show that neural response correlations induced by task-irrelevant variables may greatly exceed intrinsic noise correlations. Overall, our findings suggest a general computational strategy by which neurons with mismatched tuning for two different sensory cues may be decoded to perform marginalization operations that dissociate possible causes of sensory inputs. PMID:27334948

Adaptive neuron-to-EMG decoder training for FES neuroprostheses

NASA Astrophysics Data System (ADS)

Ethier, Christian; Acuna, Daniel; Solla, Sara A.; Miller, Lee E.

2016-08-01

Objective. We have previously demonstrated a brain-machine interface neuroprosthetic system that provided continuous control of functional electrical stimulation (FES) and restoration of grasp in a primate model of spinal cord injury (SCI). Predicting intended EMG directly from cortical recordings provides a flexible high-dimensional control signal for FES. However, no peripheral signal such as force or EMG is available for training EMG decoders in paralyzed individuals. Approach. Here we present a method for training an EMG decoder in the absence of muscle activity recordings; the decoder relies on mapping behaviorally relevant cortical activity to the inferred EMG activity underlying an intended action. Monkeys were trained at a 2D isometric wrist force task to control a computer cursor by applying force in the flexion, extension, ulnar, and radial directions and execute a center-out task. We used a generic muscle force-to-endpoint force model based on muscle pulling directions to relate each target force to an optimal EMG pattern that attained the target force while minimizing overall muscle activity. We trained EMG decoders during the target hold periods using a gradient descent algorithm that compared EMG predictions to optimal EMG patterns. Main results. We tested this method both offline and online. We quantified both the accuracy of offline force predictions and the ability of a monkey to use these real-time force predictions for closed-loop cursor control. We compared both offline and online results to those obtained with several other direct force decoders, including an optimal decoder computed from concurrently measured neural and force signals. Significance. This novel approach to training an adaptive EMG decoder could make a brain-control FES neuroprosthesis an effective tool to restore the hand function of paralyzed individuals. Clinical implementation would make use of individualized EMG-to-force models. Broad generalization could be achieved by including data from multiple grasping tasks in the training of the neuron-to-EMG decoder. Our approach would make it possible for persons with SCI to grasp objects with their own hands, using near-normal motor intent.

Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex

NASA Astrophysics Data System (ADS)

Remedios, Ryan; Kennedy, Ann; Zelikowsky, Moriel; Grewe, Benjamin F.; Schnitzer, Mark J.; Anderson, David J.

2017-10-01

All animals possess a repertoire of innate (or instinctive) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown. Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours, are shaped by social experience. Oestrogen receptor 1-expressing (Esr1+) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents. We used microendoscopy to image Esr1+ neuronal activity in the VMHvl of male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male versus female conspecifics. However, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific neuronal ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not to mount or attack conspecifics, ensemble divergence did not occur. However, 30 minutes of sexual experience with a female was sufficient to promote the separation of male and female ensembles and to induce an attack response 24 h later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviours. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a ‘hard-wired’ system.

Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex.

PubMed

Remedios, Ryan; Kennedy, Ann; Zelikowsky, Moriel; Grewe, Benjamin F; Schnitzer, Mark J; Anderson, David J

2017-10-18

All animals possess a repertoire of innate (or instinctive) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown. Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours, are shaped by social experience. Oestrogen receptor 1-expressing (Esr1 + ) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents. We used microendoscopy to image Esr1 + neuronal activity in the VMHvl of male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male versus female conspecifics. However, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific neuronal ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not to mount or attack conspecifics, ensemble divergence did not occur. However, 30 minutes of sexual experience with a female was sufficient to promote the separation of male and female ensembles and to induce an attack response 24 h later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviours. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a 'hard-wired' system.

RNA sequencing from neural ensembles activated during fear conditioning in the mouse temporal association cortex

PubMed Central

Cho, Jin-Hyung; Huang, Ben S.; Gray, Jesse M.

2016-01-01

The stable formation of remote fear memories is thought to require neuronal gene induction in cortical ensembles that are activated during learning. However, the set of genes expressed specifically in these activated ensembles is not known; knowledge of such transcriptional profiles may offer insights into the molecular program underlying stable memory formation. Here we use RNA-Seq to identify genes whose expression is enriched in activated cortical ensembles labeled during associative fear learning. We first establish that mouse temporal association cortex (TeA) is required for remote recall of auditory fear memories. We then perform RNA-Seq in TeA neurons that are labeled by the activity reporter Arc-dVenus during learning. We identify 944 genes with enriched expression in Arc-dVenus+ neurons. These genes include markers of L2/3, L5b, and L6 excitatory neurons but not glial or inhibitory markers, confirming Arc-dVenus to be an excitatory neuron-specific but non-layer-specific activity reporter. Cross comparisons to other transcriptional profiles show that 125 of the enriched genes are also activity-regulated in vitro or induced by visual stimulus in the visual cortex, suggesting that they may be induced generally in the cortex in an experience-dependent fashion. Prominent among the enriched genes are those encoding potassium channels that down-regulate neuronal activity, suggesting the possibility that part of the molecular program induced by fear conditioning may initiate homeostatic plasticity. PMID:27557751

Arc expression identifies the lateral amygdala fear memory trace

PubMed Central

Gouty-Colomer, L A; Hosseini, B; Marcelo, I M; Schreiber, J; Slump, D E; Yamaguchi, S; Houweling, A R; Jaarsma, D; Elgersma, Y; Kushner, S A

2016-01-01

Memories are encoded within sparsely distributed neuronal ensembles. However, the defining cellular properties of neurons within a memory trace remain incompletely understood. Using a fluorescence-based Arc reporter, we were able to visually identify the distinct subset of lateral amygdala (LA) neurons activated during auditory fear conditioning. We found that Arc-expressing neurons have enhanced intrinsic excitability and are preferentially recruited into newly encoded memory traces. Furthermore, synaptic potentiation of thalamic inputs to the LA during fear conditioning is learning-specific, postsynaptically mediated and highly localized to Arc-expressing neurons. Taken together, our findings validate the immediate-early gene Arc as a molecular marker for the LA neuronal ensemble recruited during fear learning. Moreover, these results establish a model of fear memory formation in which intrinsic excitability determines neuronal selection, whereas learning-related encoding is governed by synaptic plasticity. PMID:25802982

Population coding and decoding in a neural field: a computational study.

PubMed

Wu, Si; Amari, Shun-Ichi; Nakahara, Hiroyuki

2002-05-01

This study uses a neural field model to investigate computational aspects of population coding and decoding when the stimulus is a single variable. A general prototype model for the encoding process is proposed, in which neural responses are correlated, with strength specified by a gaussian function of their difference in preferred stimuli. Based on the model, we study the effect of correlation on the Fisher information, compare the performances of three decoding methods that differ in the amount of encoding information being used, and investigate the implementation of the three methods by using a recurrent network. This study not only rediscovers main results in existing literatures in a unified way, but also reveals important new features, especially when the neural correlation is strong. As the neural correlation of firing becomes larger, the Fisher information decreases drastically. We confirm that as the width of correlation increases, the Fisher information saturates and no longer increases in proportion to the number of neurons. However, we prove that as the width increases further--wider than (sqrt)2 times the effective width of the turning function--the Fisher information increases again, and it increases without limit in proportion to the number of neurons. Furthermore, we clarify the asymptotic efficiency of the maximum likelihood inference (MLI) type of decoding methods for correlated neural signals. It shows that when the correlation covers a nonlocal range of population (excepting the uniform correlation and when the noise is extremely small), the MLI type of method, whose decoding error satisfies the Cauchy-type distribution, is not asymptotically efficient. This implies that the variance is no longer adequate to measure decoding accuracy.

Incorporating spike-rate adaptation into a rate code in mathematical and biological neurons

PubMed Central

Ralston, Bridget N.; Flagg, Lucas Q.; Faggin, Eric

2016-01-01

For a slowly varying stimulus, the simplest relationship between a neuron's input and output is a rate code, in which the spike rate is a unique function of the stimulus at that instant. In the case of spike-rate adaptation, there is no unique relationship between input and output, because the spike rate at any time depends both on the instantaneous stimulus and on prior spiking (the “history”). To improve the decoding of spike trains produced by neurons that show spike-rate adaptation, we developed a simple scheme that incorporates “history” into a rate code. We utilized this rate-history code successfully to decode spike trains produced by 1) mathematical models of a neuron in which the mechanism for adaptation (IAHP) is specified, and 2) the gastropyloric receptor (GPR2), a stretch-sensitive neuron in the stomatogastric nervous system of the crab Cancer borealis, that exhibits long-lasting adaptation of unknown origin. Moreover, when we modified the spike rate either mathematically in a model system or by applying neuromodulatory agents to the experimental system, we found that changes in the rate-history code could be related to the biophysical mechanisms responsible for altering the spiking. PMID:26888106

A Spiking Neural Network System for Robust Sequence Recognition.

PubMed

Yu, Qiang; Yan, Rui; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2016-03-01

This paper proposes a biologically plausible network architecture with spiking neurons for sequence recognition. This architecture is a unified and consistent system with functional parts of sensory encoding, learning, and decoding. This is the first systematic model attempting to reveal the neural mechanisms considering both the upstream and the downstream neurons together. The whole system is a consistent temporal framework, where the precise timing of spikes is employed for information processing and cognitive computing. Experimental results show that the system is competent to perform the sequence recognition, being robust to noisy sensory inputs and invariant to changes in the intervals between input stimuli within a certain range. The classification ability of the temporal learning rule used in the system is investigated through two benchmark tasks that outperform the other two widely used learning rules for classification. The results also demonstrate the computational power of spiking neurons over perceptrons for processing spatiotemporal patterns. In summary, the system provides a general way with spiking neurons to encode external stimuli into spatiotemporal spikes, to learn the encoded spike patterns with temporal learning rules, and to decode the sequence order with downstream neurons. The system structure would be beneficial for developments in both hardware and software.

In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function.

PubMed

Sartori, Massimo; Yavuz, Utku Ş; Farina, Dario

2017-10-18

Human motor function emerges from the interaction between the neuromuscular and the musculoskeletal systems. Despite the knowledge of the mechanisms underlying neural and mechanical functions, there is no relevant understanding of the neuro-mechanical interplay in the neuro-musculo-skeletal system. This currently represents the major challenge to the understanding of human movement. We address this challenge by proposing a paradigm for investigating spinal motor neuron contribution to skeletal joint mechanical function in the intact human in vivo. We employ multi-muscle spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-motor neuron discharges across five lumbosacral segments in the human spinal cord. We use complete α-motor neuron discharge series to drive forward subject-specific models of the musculoskeletal system in open-loop with no corrective feedback. We perform validation tests where mechanical moments are estimated with no knowledge of reference data over unseen conditions. This enables accurate blinded estimation of ankle function purely from motor neuron information. Remarkably, this enables observing causal associations between spinal motor neuron activity and joint moment control. We provide a new class of neural data-driven musculoskeletal modeling formulations for bridging between movement neural and mechanical levels in vivo with implications for understanding motor physiology, pathology, and recovery.

fMRI orientation decoding in V1 does not require global maps or globally coherent orientation stimuli.

PubMed

Alink, Arjen; Krugliak, Alexandra; Walther, Alexander; Kriegeskorte, Nikolaus

2013-01-01

The orientation of a large grating can be decoded from V1 functional magnetic resonance imaging (fMRI) data, even at low resolution (3-mm isotropic voxels). This finding has suggested that columnar-level neuronal information might be accessible to fMRI at 3T. However, orientation decodability might alternatively arise from global orientation-preference maps. Such global maps across V1 could result from bottom-up processing, if the preferences of V1 neurons were biased toward particular orientations (e.g., radial from fixation, or cardinal, i.e., vertical or horizontal). Global maps could also arise from local recurrent or top-down processing, reflecting pre-attentive perceptual grouping, attention spreading, or predictive coding of global form. Here we investigate whether fMRI orientation decoding with 2-mm voxels requires (a) globally coherent orientation stimuli and/or (b) global-scale patterns of V1 activity. We used opposite-orientation gratings (balanced about the cardinal orientations) and spirals (balanced about the radial orientation), along with novel patch-swapped variants of these stimuli. The two stimuli of a patch-swapped pair have opposite orientations everywhere (like their globally coherent parent stimuli). However, the two stimuli appear globally similar, a patchwork of opposite orientations. We find that all stimulus pairs are robustly decodable, demonstrating that fMRI orientation decoding does not require globally coherent orientation stimuli. Furthermore, decoding remained robust after spatial high-pass filtering for all stimuli, showing that fine-grained components of the fMRI patterns reflect visual orientations. Consistent with previous studies, we found evidence for global radial and vertical preference maps in V1. However, these were weak or absent for patch-swapped stimuli, suggesting that global preference maps depend on globally coherent orientations and might arise through recurrent or top-down processes related to the perception of global form.

Neuronal Ensemble Synchrony during Human Focal Seizures

PubMed Central

Ahmed, Omar J.; Harrison, Matthew T.; Eskandar, Emad N.; Cosgrove, G. Rees; Madsen, Joseph R.; Blum, Andrew S.; Potter, N. Stevenson; Hochberg, Leigh R.; Cash, Sydney S.

2014-01-01

Seizures are classically characterized as the expression of hypersynchronous neural activity, yet the true degree of synchrony in neuronal spiking (action potentials) during human seizures remains a fundamental question. We quantified the temporal precision of spike synchrony in ensembles of neocortical neurons during seizures in people with pharmacologically intractable epilepsy. Two seizure types were analyzed: those characterized by sustained gamma (∼40–60 Hz) local field potential (LFP) oscillations or by spike-wave complexes (SWCs; ∼3 Hz). Fine (<10 ms) temporal synchrony was rarely present during gamma-band seizures, where neuronal spiking remained highly irregular and asynchronous. In SWC seizures, phase locking of neuronal spiking to the SWC spike phase induced synchrony at a coarse 50–100 ms level. In addition, transient fine synchrony occurred primarily during the initial ∼20 ms period of the SWC spike phase and varied across subjects and seizures. Sporadic coherence events between neuronal population spike counts and LFPs were observed during SWC seizures in high (∼80 Hz) gamma-band and during high-frequency oscillations (∼130 Hz). Maximum entropy models of the joint neuronal spiking probability, constrained only on single neurons' nonstationary coarse spiking rates and local network activation, explained most of the fine synchrony in both seizure types. Our findings indicate that fine neuronal ensemble synchrony occurs mostly during SWC, not gamma-band, seizures, and primarily during the initial phase of SWC spikes. Furthermore, these fine synchrony events result mostly from transient increases in overall neuronal network spiking rates, rather than changes in precise spiking correlations between specific pairs of neurons. PMID:25057195

LDPC Codes with Minimum Distance Proportional to Block Size

NASA Technical Reports Server (NTRS)

Divsalar, Dariush; Jones, Christopher; Dolinar, Samuel; Thorpe, Jeremy

2009-01-01

Low-density parity-check (LDPC) codes characterized by minimum Hamming distances proportional to block sizes have been demonstrated. Like the codes mentioned in the immediately preceding article, the present codes are error-correcting codes suitable for use in a variety of wireless data-communication systems that include noisy channels. The previously mentioned codes have low decoding thresholds and reasonably low error floors. However, the minimum Hamming distances of those codes do not grow linearly with code-block sizes. Codes that have this minimum-distance property exhibit very low error floors. Examples of such codes include regular LDPC codes with variable degrees of at least 3. Unfortunately, the decoding thresholds of regular LDPC codes are high. Hence, there is a need for LDPC codes characterized by both low decoding thresholds and, in order to obtain acceptably low error floors, minimum Hamming distances that are proportional to code-block sizes. The present codes were developed to satisfy this need. The minimum Hamming distances of the present codes have been shown, through consideration of ensemble-average weight enumerators, to be proportional to code block sizes. As in the cases of irregular ensembles, the properties of these codes are sensitive to the proportion of degree-2 variable nodes. A code having too few such nodes tends to have an iterative decoding threshold that is far from the capacity threshold. A code having too many such nodes tends not to exhibit a minimum distance that is proportional to block size. Results of computational simulations have shown that the decoding thresholds of codes of the present type are lower than those of regular LDPC codes. Included in the simulations were a few examples from a family of codes characterized by rates ranging from low to high and by thresholds that adhere closely to their respective channel capacity thresholds; the simulation results from these examples showed that the codes in question have low error floors as well as low decoding thresholds. As an example, the illustration shows the protograph (which represents the blueprint for overall construction) of one proposed code family for code rates greater than or equal to 1.2. Any size LDPC code can be obtained by copying the protograph structure N times, then permuting the edges. The illustration also provides Field Programmable Gate Array (FPGA) hardware performance simulations for this code family. In addition, the illustration provides minimum signal-to-noise ratios (Eb/No) in decibels (decoding thresholds) to achieve zero error rates as the code block size goes to infinity for various code rates. In comparison with the codes mentioned in the preceding article, these codes have slightly higher decoding thresholds.

Role of orbitofrontal cortex neuronal ensembles in the expression of incubation of heroin craving

PubMed Central

Fanous, Sanya; Goldart, Evan M.; Theberge, Florence R.M.; Bossert, Jennifer M.; Shaham, Yavin; Hope, Bruce T.

2012-01-01

In humans, exposure to cues previously associated with heroin use often provokes relapse after prolonged withdrawal periods. In rats, cue-induced heroin-seeking progressively increases after withdrawal (incubation of heroin craving). Here, we examined the role of orbitofrontal cortex (OFC) neuronal ensembles in the enhanced response to heroin cues after prolonged withdrawal or the expression of incubation of heroin craving. We trained rats to self-administer heroin (6-h/d for 10 d) and assessed cue-induced heroin-seeking in extinction tests after 1 or 14 withdrawal days. Cue-induced heroin-seeking increased from 1 day to 14 days and was accompanied by increased Fos expression in ~12% of OFC neurons. Non-selective inactivation of OFC neurons with the GABA agonists baclofen+muscimol decreased cue-induced heroin-seeking on withdrawal day 14 but not day 1. We then used the Daun02 inactivation procedure to assess a causal role of the minority of selectively activated Fos-expressing OFC neurons (that presumably form cue-encoding neuronal ensembles) in cue-induced heroin-seeking after 14 withdrawal days. We trained cfos-lacZ transgenic rats to self-administer heroin and 11 days later re-exposed them to heroin-associated cues or novel cues for 15 min (induction day) followed by OFC Daun02 or vehicle injections 90 min later; we then tested the rats in extinction tests 3 days later. Daun02 selectively decreased cue-induced heroin-seeking in rats previously re-exposed to the heroin-associated cues on induction day, but not in rats previously exposed to novel cues. Results suggest that heroin-cue-activated OFC neuronal ensembles contribute to the expression of incubation of heroin craving. PMID:22915104

Redundant information encoding in primary motor cortex during natural and prosthetic motor control.

PubMed

So, Kelvin; Ganguly, Karunesh; Jimenez, Jessica; Gastpar, Michael C; Carmena, Jose M

2012-06-01

Redundant encoding of information facilitates reliable distributed information processing. To explore this hypothesis in the motor system, we applied concepts from information theory to quantify the redundancy of movement-related information encoded in the macaque primary motor cortex (M1) during natural and neuroprosthetic control. Two macaque monkeys were trained to perform a delay center-out reaching task controlling a computer cursor under natural arm movement (manual control, 'MC'), and using a brain-machine interface (BMI) via volitional control of neural ensemble activity (brain control, 'BC'). During MC, we found neurons in contralateral M1 to contain higher and more redundant information about target direction than ipsilateral M1 neurons, consistent with the laterality of movement control. During BC, we found that the M1 neurons directly incorporated into the BMI ('direct' neurons) contained the highest and most redundant target information compared to neurons that were not incorporated into the BMI ('indirect' neurons). This effect was even more significant when comparing to M1 neurons of the opposite hemisphere. Interestingly, when we retrained the BMI to use ipsilateral M1 activity, we found that these neurons were more redundant and contained higher information than contralateral M1 neurons, even though ensembles from this hemisphere were previously less redundant during natural arm movement. These results indicate that ensembles most associated to movement contain highest redundancy and information encoding, which suggests a role for redundancy in proficient natural and prosthetic motor control.

Increased Sparsity of Hippocampal CA1 Neuronal Ensembles in a Mouse Model of Down Syndrome Assayed by Arc Expression

PubMed Central

Smith-Hicks, Constance L.; Cai, Peiling; Savonenko, Alena V.; Reeves, Roger H.; Worley, Paul F.

2017-01-01

Down syndrome (DS) is the leading chromosomal cause of intellectual disability, yet the neural substrates of learning and memory deficits remain poorly understood. Here, we interrogate neural networks linked to learning and memory in a well-characterized model of DS, the Ts65Dn mouse. We report that Ts65Dn mice exhibit exploratory behavior that is not different from littermate wild-type (WT) controls yet behavioral activation of Arc mRNA transcription in pyramidal neurons of the CA1 region of the hippocampus is altered in Ts65Dn mice. In WT mice, a 5 min period of exploration of a novel environment resulted in Arc mRNA transcription in 39% of CA1 neurons. By contrast, the same period of exploration resulted in only ~20% of CA1 neurons transcribing Arc mRNA in Ts65Dn mice indicating increased sparsity of the behaviorally induced ensemble. Like WT mice the CA1 pyramidal neurons of Ts65Dn mice reactivated Arc transcription during a second exposure to the same environment 20 min after the first experience, but the size of the reactivated ensemble was only ~60% of that in WT mice. After repeated daily exposures there was a further decline in the size of the reactivated ensemble in Ts65Dn and a disruption of reactivation. Together these data demonstrate reduction in the size of the behaviorally induced network that expresses Arc in Ts65Dn mice and disruption of the long-term stability of the ensemble. We propose that these deficits in network formation and stability contribute to cognitive symptoms in DS. PMID:28217086

Social Behaviour Shapes Hypothalamic Neural Ensemble Representations Of Conspecific Sex

PubMed Central

Remedios, Ryan; Kennedy, Ann; Zelikowsky, Moriel; Grewe, Benjamin F.; Schnitzer, Mark J.; Anderson, David J.

2017-01-01

Summary All animals possess a repertoire of innate (or instinctive1,2) behaviors, which can be performed without training. Whether such behaviors are mediated by anatomically distinct and/or genetically specified neural pathways remains a matter of debate3-5. Here we report that hypothalamic neural ensemble representations underlying innate social behaviors are shaped by social experience. Estrogen receptor 1-expressing (Esr1+) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents6-8. We used microendoscopy9 to image VMHvl Esr1+ neuronal activity in male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male vs. female conspecifics. But surprisingly, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not mount or attack conspecifics, ensemble divergence did not occur. However, 30 min of sexual experience with a female was sufficient to promote both male vs. female ensemble separation and attack, measured 24 hr later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviors. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a “hard-wired” system. PMID:29052632

Detecting bladder fullness through the ensemble activity patterns of the spinal cord unit population in a somatovisceral convergence environment.

PubMed

Park, Jae Hong; Kim, Chang-Eop; Shin, Jaewoo; Im, Changkyun; Koh, Chin Su; Seo, In Seok; Kim, Sang Jeong; Shin, Hyung-Cheul

2013-10-01

Chronic monitoring of the state of the bladder can be used to notify patients with urinary dysfunction when the bladder should be voided. Given that many spinal neurons respond both to somatic and visceral inputs, it is necessary to extract bladder information selectively from the spinal cord. Here, we hypothesize that sensory information with distinct modalities should be represented by the distinct ensemble activity patterns within the neuronal population and, therefore, analyzing the activity patterns of the neuronal population could distinguish bladder fullness from somatic stimuli. We simultaneously recorded 26-27 single unit activities in response to bladder distension or tactile stimuli in the dorsal spinal cord of each Sprague-Dawley rat. In order to discriminate between bladder fullness and tactile stimulus inputs, we analyzed the ensemble activity patterns of the entire neuronal population. A support vector machine (SVM) was employed as a classifier, and discrimination performance was measured by k-fold cross-validation tests. Most of the units responding to bladder fullness also responded to the tactile stimuli (88.9-100%). The SVM classifier precisely distinguished the bladder fullness from the somatic input (100%), indicating that the ensemble activity patterns of the unit population in the spinal cord are distinct enough to identify the current input modality. Moreover, our ensemble activity pattern-based classifier showed high robustness against random losses of signals. This study is the first to demonstrate that the two main issues of electroneurographic monitoring of bladder fullness, low signals and selectiveness, can be solved by an ensemble activity pattern-based approach, improving the feasibility of chronic monitoring of bladder fullness by neural recording.

Catching the engram: strategies to examine the memory trace.

PubMed

Sakaguchi, Masanori; Hayashi, Yasunori

2012-09-21

Memories are stored within neuronal ensembles in the brain. Modern genetic techniques can be used to not only visualize specific neuronal ensembles that encode memories (e.g., fear, craving) but also to selectively manipulate those neurons. These techniques are now being expanded for the study of various types of memory. In this review, we will summarize the genetic methods used to visualize and manipulate neurons involved in the representation of memory engrams. The methods will help clarify how memory is encoded, stored and processed in the brain. Furthermore, these approaches may contribute to our understanding of the pathological mechanisms associated with human memory disorders and, ultimately, may aid the development of therapeutic strategies to ameliorate these diseases.

Network-induced chaos in integrate-and-fire neuronal ensembles.

PubMed

Zhou, Douglas; Rangan, Aaditya V; Sun, Yi; Cai, David

2009-09-01

It has been shown that a single standard linear integrate-and-fire (IF) neuron under a general time-dependent stimulus cannot possess chaotic dynamics despite the firing-reset discontinuity. Here we address the issue of whether conductance-based, pulsed-coupled network interactions can induce chaos in an IF neuronal ensemble. Using numerical methods, we demonstrate that all-to-all, homogeneously pulse-coupled IF neuronal networks can indeed give rise to chaotic dynamics under an external periodic current drive. We also provide a precise characterization of the largest Lyapunov exponent for these high dimensional nonsmooth dynamical systems. In addition, we present a stable and accurate numerical algorithm for evaluating the largest Lyapunov exponent, which can overcome difficulties encountered by traditional methods for these nonsmooth dynamical systems with degeneracy induced by, e.g., refractoriness of neurons.

Multineuron spike train analysis with R-convolution linear combination kernel.

PubMed

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Synaptic dynamics contribute to long-term single neuron response fluctuations.

PubMed

Reinartz, Sebastian; Biro, Istvan; Gal, Asaf; Giugliano, Michele; Marom, Shimon

2014-01-01

Firing rate variability at the single neuron level is characterized by long-memory processes and complex statistics over a wide range of time scales (from milliseconds up to several hours). Here, we focus on the contribution of non-stationary efficacy of the ensemble of synapses-activated in response to a given stimulus-on single neuron response variability. We present and validate a method tailored for controlled and specific long-term activation of a single cortical neuron in vitro via synaptic or antidromic stimulation, enabling a clear separation between two determinants of neuronal response variability: membrane excitability dynamics vs. synaptic dynamics. Applying this method we show that, within the range of physiological activation frequencies, the synaptic ensemble of a given neuron is a key contributor to the neuronal response variability, long-memory processes and complex statistics observed over extended time scales. Synaptic transmission dynamics impact on response variability in stimulation rates that are substantially lower compared to stimulation rates that drive excitability resources to fluctuate. Implications to network embedded neurons are discussed.

High Stimulus-Related Information in Barrel Cortex Inhibitory Interneurons

PubMed Central

Reyes-Puerta, Vicente; Kim, Suam; Sun, Jyh-Jang; Imbrosci, Barbara; Kilb, Werner; Luhmann, Heiko J.

2015-01-01

The manner in which populations of inhibitory (INH) and excitatory (EXC) neocortical neurons collectively encode stimulus-related information is a fundamental, yet still unresolved question. Here we address this question by simultaneously recording with large-scale multi-electrode arrays (of up to 128 channels) the activity of cell ensembles (of up to 74 neurons) distributed along all layers of 3–4 neighboring cortical columns in the anesthetized adult rat somatosensory barrel cortex in vivo. Using two different whisker stimulus modalities (location and frequency) we show that individual INH neurons – classified as such according to their distinct extracellular spike waveforms – discriminate better between restricted sets of stimuli (≤6 stimulus classes) than EXC neurons in granular and infra-granular layers. We also demonstrate that ensembles of INH cells jointly provide as much information about such stimuli as comparable ensembles containing the ~20% most informative EXC neurons, however presenting less information redundancy – a result which was consistent when applying both theoretical information measurements and linear discriminant analysis classifiers. These results suggest that a consortium of INH neurons dominates the information conveyed to the neocortical network, thereby efficiently processing incoming sensory activity. This conclusion extends our view on the role of the inhibitory system to orchestrate cortical activity. PMID:26098109

Investigating local and long-range neuronal network dynamics by simultaneous optogenetics, reverse microdialysis and silicon probe recordings in vivo

PubMed Central

Taylor, Hannah; Schmiedt, Joscha T.; Çarçak, Nihan; Onat, Filiz; Di Giovanni, Giuseppe; Lambert, Régis; Leresche, Nathalie; Crunelli, Vincenzo; David, Francois

2014-01-01

Background The advent of optogenetics has given neuroscientists the opportunity to excite or inhibit neuronal population activity with high temporal resolution and cellular selectivity. Thus, when combined with recordings of neuronal ensemble activity in freely moving animals optogenetics can provide an unprecedented snapshot of the contribution of neuronal assemblies to (patho)physiological conditions in vivo. Still, the combination of optogenetic and silicone probe (or tetrode) recordings does not allow investigation of the role played by voltage- and transmitter-gated channels of the opsin-transfected neurons and/or other adjacent neurons in controlling neuronal activity. New method and results We demonstrate that optogenetics and silicone probe recordings can be combined with intracerebral reverse microdialysis for the long-term delivery of neuroactive drugs around the optic fiber and silicone probe. In particular, we show the effect of antagonists of T-type Ca2+ channels, hyperpolarization-activated cyclic nucleotide-gated channels and metabotropic glutamate receptors on silicone probe-recorded activity of the local opsin-transfected neurons in the ventrobasal thalamus, and demonstrate the changes that the block of these thalamic channels/receptors brings about in the network dynamics of distant somatotopic cortical neuronal ensembles. Comparison with existing methods This is the first demonstration of successfully combining optogenetics and neuronal ensemble recordings with reverse microdialysis. This combination of techniques overcomes some of the disadvantages that are associated with the use of intracerebral injection of a drug-containing solution at the site of laser activation. Conclusions The combination of reverse microdialysis, silicone probe recordings and optogenetics can unravel the short and long-term effects of specific transmitter- and voltage-gated channels on laser-modulated firing at the site of optogenetic stimulation and the actions that these manipulations exert on distant neuronal populations. PMID:25004203

Investigating local and long-range neuronal network dynamics by simultaneous optogenetics, reverse microdialysis and silicon probe recordings in vivo.

PubMed

Taylor, Hannah; Schmiedt, Joscha T; Carçak, Nihan; Onat, Filiz; Di Giovanni, Giuseppe; Lambert, Régis; Leresche, Nathalie; Crunelli, Vincenzo; David, Francois

2014-09-30

The advent of optogenetics has given neuroscientists the opportunity to excite or inhibit neuronal population activity with high temporal resolution and cellular selectivity. Thus, when combined with recordings of neuronal ensemble activity in freely moving animals optogenetics can provide an unprecedented snapshot of the contribution of neuronal assemblies to (patho)physiological conditions in vivo. Still, the combination of optogenetic and silicone probe (or tetrode) recordings does not allow investigation of the role played by voltage- and transmitter-gated channels of the opsin-transfected neurons and/or other adjacent neurons in controlling neuronal activity. We demonstrate that optogenetics and silicone probe recordings can be combined with intracerebral reverse microdialysis for the long-term delivery of neuroactive drugs around the optic fiber and silicone probe. In particular, we show the effect of antagonists of T-type Ca(2+) channels, hyperpolarization-activated cyclic nucleotide-gated channels and metabotropic glutamate receptors on silicone probe-recorded activity of the local opsin-transfected neurons in the ventrobasal thalamus, and demonstrate the changes that the block of these thalamic channels/receptors brings about in the network dynamics of distant somatotopic cortical neuronal ensembles. This is the first demonstration of successfully combining optogenetics and neuronal ensemble recordings with reverse microdialysis. This combination of techniques overcomes some of the disadvantages that are associated with the use of intracerebral injection of a drug-containing solution at the site of laser activation. The combination of reverse microdialysis, silicone probe recordings and optogenetics can unravel the short and long-term effects of specific transmitter- and voltage-gated channels on laser-modulated firing at the site of optogenetic stimulation and the actions that these manipulations exert on distant neuronal populations. Copyright © 2014. Published by Elsevier B.V.

Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints.

PubMed

Kostal, Lubomir; Kobayashi, Ryota

2015-10-01

Information theory quantifies the ultimate limits on reliable information transfer by means of the channel capacity. However, the channel capacity is known to be an asymptotic quantity, assuming unlimited metabolic cost and computational power. We investigate a single-compartment Hodgkin-Huxley type neuronal model under the spike-rate coding scheme and address how the metabolic cost and the decoding complexity affects the optimal information transmission. We find that the sub-threshold stimulation regime, although attaining the smallest capacity, allows for the most efficient balance between the information transmission and the metabolic cost. Furthermore, we determine post-synaptic firing rate histograms that are optimal from the information-theoretic point of view, which enables the comparison of our results with experimental data. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Efficiency turns the table on neural encoding, decoding and noise.

PubMed

Deneve, Sophie; Chalk, Matthew

2016-04-01

Sensory neurons are usually described with an encoding model, for example, a function that predicts their response from the sensory stimulus using a receptive field (RF) or a tuning curve. However, central to theories of sensory processing is the notion of 'efficient coding'. We argue here that efficient coding implies a completely different neural coding strategy. Instead of a fixed encoding model, neural populations would be described by a fixed decoding model (i.e. a model reconstructing the stimulus from the neural responses). Because the population solves a global optimization problem, individual neurons are variable, but not noisy, and have no truly invariant tuning curve or receptive field. We review recent experimental evidence and implications for neural noise correlations, robustness and adaptation. Copyright © 2016. Published by Elsevier Ltd.

An information-theoretic approach to motor action decoding with a reconfigurable parallel architecture.

PubMed

Craciun, Stefan; Brockmeier, Austin J; George, Alan D; Lam, Herman; Príncipe, José C

2011-01-01

Methods for decoding movements from neural spike counts using adaptive filters often rely on minimizing the mean-squared error. However, for non-Gaussian distribution of errors, this approach is not optimal for performance. Therefore, rather than using probabilistic modeling, we propose an alternate non-parametric approach. In order to extract more structure from the input signal (neuronal spike counts) we propose using minimum error entropy (MEE), an information-theoretic approach that minimizes the error entropy as part of an iterative cost function. However, the disadvantage of using MEE as the cost function for adaptive filters is the increase in computational complexity. In this paper we present a comparison between the decoding performance of the analytic Wiener filter and a linear filter trained with MEE, which is then mapped to a parallel architecture in reconfigurable hardware tailored to the computational needs of the MEE filter. We observe considerable speedup from the hardware design. The adaptation of filter weights for the multiple-input, multiple-output linear filters, necessary in motor decoding, is a highly parallelizable algorithm. It can be decomposed into many independent computational blocks with a parallel architecture readily mapped to a field-programmable gate array (FPGA) and scales to large numbers of neurons. By pipelining and parallelizing independent computations in the algorithm, the proposed parallel architecture has sublinear increases in execution time with respect to both window size and filter order.

A Statistical Description of Neural Ensemble Dynamics

PubMed Central

Long, John D.; Carmena, Jose M.

2011-01-01

The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an ensemble. Inferring these connections is often intractable because the set of possible interactions grows exponentially with ensemble size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing changes in the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility. PMID:22319486

Catching the engram: strategies to examine the memory trace

PubMed Central

2012-01-01

Memories are stored within neuronal ensembles in the brain. Modern genetic techniques can be used to not only visualize specific neuronal ensembles that encode memories (e.g., fear, craving) but also to selectively manipulate those neurons. These techniques are now being expanded for the study of various types of memory. In this review, we will summarize the genetic methods used to visualize and manipulate neurons involved in the representation of memory engrams. The methods will help clarify how memory is encoded, stored and processed in the brain. Furthermore, these approaches may contribute to our understanding of the pathological mechanisms associated with human memory disorders and, ultimately, may aid the development of therapeutic strategies to ameliorate these diseases. PMID:22999350

Synaptic Ensemble Underlying the Selection and Consolidation of Neuronal Circuits during Learning.

PubMed

Hoshiba, Yoshio; Wada, Takeyoshi; Hayashi-Takagi, Akiko

2017-01-01

Memories are crucial to the cognitive essence of who we are as human beings. Accumulating evidence has suggested that memories are stored as a subset of neurons that probably fire together in the same ensemble. Such formation of cell ensembles must meet contradictory requirements of being plastic and responsive during learning, but also stable in order to maintain the memory. Although synaptic potentiation is presumed to be the cellular substrate for this process, the link between the two remains correlational. With the application of the latest optogenetic tools, it has been possible to collect direct evidence of the contributions of synaptic potentiation in the formation and consolidation of cell ensemble in a learning task specific manner. In this review, we summarize the current view of the causative role of synaptic plasticity as the cellular mechanism underlying the encoding of memory and recalling of learned memories. In particular, we will be focusing on the latest optoprobe developed for the visualization of such "synaptic ensembles." We further discuss how a new synaptic ensemble could contribute to the formation of cell ensembles during learning and memory. With the development and application of novel research tools in the future, studies on synaptic ensembles will pioneer new discoveries, eventually leading to a comprehensive understanding of how the brain works.

A θ-γ oscillation code for neuronal coordination during motor behavior.

PubMed

Igarashi, Jun; Isomura, Yoshikazu; Arai, Kensuke; Harukuni, Rie; Fukai, Tomoki

2013-11-20

Sequential motor behavior requires a progression of discrete preparation and execution states. However, the organization of state-dependent activity in neuronal ensembles of motor cortex is poorly understood. Here, we recorded neuronal spiking and local field potential activity from rat motor cortex during reward-motivated movement and observed robust behavioral state-dependent coordination between neuronal spiking, γ oscillations, and θ oscillations. Slow and fast γ oscillations appeared during distinct movement states and entrained neuronal firing. γ oscillations, in turn, were coupled to θ oscillations, and neurons encoding different behavioral states fired at distinct phases of θ in a highly layer-dependent manner. These findings indicate that θ and nested dual band γ oscillations serve as the temporal structure for the selection of a conserved set of functional channels in motor cortical layer activity during animal movement. Furthermore, these results also suggest that cross-frequency couplings between oscillatory neuronal ensemble activities are part of the general coding mechanism in cortex.

Training in cortical control of neuroprosthetic devices improves signal extraction from small neuronal ensembles.

PubMed

Helms Tillery, S I; Taylor, D M; Schwartz, A B

2003-01-01

We have recently developed a closed-loop environment in which we can test the ability of primates to control the motion of a virtual device using ensembles of simultaneously recorded neurons /29/. Here we use a maximum likelihood method to assess the information about task performance contained in the neuronal ensemble. We trained two animals to control the motion of a computer cursor in three dimensions. Initially the animals controlled cursor motion using arm movements, but eventually they learned to drive the cursor directly from cortical activity. Using a population vector (PV) based upon the relation between cortical activity and arm motion, the animals were able to control the cursor directly from the brain in a closed-loop environment, but with difficulty. We added a supervised learning method that modified the parameters of the PV according to task performance (adaptive PV), and found that animals were able to exert much finer control over the cursor motion from brain signals. Here we describe a maximum likelihood method (ML) to assess the information about target contained in neuronal ensemble activity. Using this method, we compared the information about target contained in the ensemble during arm control, during brain control early in the adaptive PV, and during brain control after the adaptive PV had settled and the animal could drive the cursor reliably and with fine gradations. During the arm-control task, the ML was able to determine the target of the movement in as few as 10% of the trials, and as many as 75% of the trials, with an average of 65%. This average dropped when the animals used a population vector to control motion of the cursor. On average we could determine the target in around 35% of the trials. This low percentage was also reflected in poor control of the cursor, so that the animal was unable to reach the target in a large percentage of trials. Supervised adjustment of the population vector parameters produced new weighting coefficients and directional tuning parameters for many neurons. This produced a much better performance of the brain-controlled cursor motion. It was also reflected in the maximum likelihood measure of cell activity, producing the correct target based only on neuronal activity in over 80% of the trials on average. The changes in maximum likelihood estimates of target location based on ensemble firing show that an animal's ability to regulate the motion of a cortically controlled device is not crucially dependent on the experimenter's ability to estimate intention from neuronal activity.

Visual coding with a population of direction-selective neurons.

PubMed

Fiscella, Michele; Franke, Felix; Farrow, Karl; Müller, Jan; Roska, Botond; da Silveira, Rava Azeredo; Hierlemann, Andreas

2015-10-01

The brain decodes the visual scene from the action potentials of ∼20 retinal ganglion cell types. Among the retinal ganglion cells, direction-selective ganglion cells (DSGCs) encode motion direction. Several studies have focused on the encoding or decoding of motion direction by recording multiunit activity, mainly in the visual cortex. In this study, we simultaneously recorded from all four types of ON-OFF DSGCs of the rabbit retina using a microelectronics-based high-density microelectrode array (HDMEA) and decoded their concerted activity using probabilistic and linear decoders. Furthermore, we investigated how the modification of stimulus parameters (velocity, size, angle of moving object) and the use of different tuning curve fits influenced decoding precision. Finally, we simulated ON-OFF DSGC activity, based on real data, in order to understand how tuning curve widths and the angular distribution of the cells' preferred directions influence decoding performance. We found that probabilistic decoding strategies outperformed, on average, linear methods and that decoding precision was robust to changes in stimulus parameters such as velocity. The removal of noise correlations among cells, by random shuffling trials, caused a drop in decoding precision. Moreover, we found that tuning curves are broad in order to minimize large errors at the expense of a higher average error, and that the retinal direction-selective system would not substantially benefit, on average, from having more than four types of ON-OFF DSGCs or from a perfect alignment of the cells' preferred directions. Copyright © 2015 the American Physiological Society.

Visual coding with a population of direction-selective neurons

PubMed Central

Farrow, Karl; Müller, Jan; Roska, Botond; Azeredo da Silveira, Rava; Hierlemann, Andreas

2015-01-01

The brain decodes the visual scene from the action potentials of ∼20 retinal ganglion cell types. Among the retinal ganglion cells, direction-selective ganglion cells (DSGCs) encode motion direction. Several studies have focused on the encoding or decoding of motion direction by recording multiunit activity, mainly in the visual cortex. In this study, we simultaneously recorded from all four types of ON-OFF DSGCs of the rabbit retina using a microelectronics-based high-density microelectrode array (HDMEA) and decoded their concerted activity using probabilistic and linear decoders. Furthermore, we investigated how the modification of stimulus parameters (velocity, size, angle of moving object) and the use of different tuning curve fits influenced decoding precision. Finally, we simulated ON-OFF DSGC activity, based on real data, in order to understand how tuning curve widths and the angular distribution of the cells' preferred directions influence decoding performance. We found that probabilistic decoding strategies outperformed, on average, linear methods and that decoding precision was robust to changes in stimulus parameters such as velocity. The removal of noise correlations among cells, by random shuffling trials, caused a drop in decoding precision. Moreover, we found that tuning curves are broad in order to minimize large errors at the expense of a higher average error, and that the retinal direction-selective system would not substantially benefit, on average, from having more than four types of ON-OFF DSGCs or from a perfect alignment of the cells' preferred directions. PMID:26289471

Global cortical activity predicts shape of hand during grasping

PubMed Central

Agashe, Harshavardhan A.; Paek, Andrew Y.; Zhang, Yuhang; Contreras-Vidal, José L.

2015-01-01

Recent studies show that the amplitude of cortical field potentials is modulated in the time domain by grasping kinematics. However, it is unknown if these low frequency modulations persist and contain enough information to decode grasp kinematics in macro-scale activity measured at the scalp via electroencephalography (EEG). Further, it is unclear as to whether joint angle velocities or movement synergies are the optimal kinematics spaces to decode. In this offline decoding study, we infer from human EEG, hand joint angular velocities as well as synergistic trajectories as subjects perform natural reach-to-grasp movements. Decoding accuracy, measured as the correlation coefficient (r) between the predicted and actual movement kinematics, was r = 0.49 ± 0.02 across 15 hand joints. Across the first three kinematic synergies, decoding accuracies were r = 0.59 ± 0.04, 0.47 ± 0.06, and 0.32 ± 0.05. The spatial-temporal pattern of EEG channel recruitment showed early involvement of contralateral frontal-central scalp areas followed by later activation of central electrodes over primary sensorimotor cortical areas. Information content in EEG about the grasp type peaked at 250 ms after movement onset. The high decoding accuracies in this study are significant not only as evidence for time-domain modulation in macro-scale brain activity, but for the field of brain-machine interfaces as well. Our decoding strategy, which harnesses the neural “symphony” as opposed to local members of the neural ensemble (as in intracranial approaches), may provide a means of extracting information about motor intent for grasping without the need for penetrating electrodes and suggests that it may be soon possible to develop non-invasive neural interfaces for the control of prosthetic limbs. PMID:25914616

Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces

NASA Astrophysics Data System (ADS)

Liao, Yuxi; She, Xiwei; Wang, Yiwen; Zhang, Shaomin; Zhang, Qiaosheng; Zheng, Xiaoxiang; Principe, Jose C.

2015-12-01

Objective. Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach. In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat’s motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results. Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance. These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.

Ensemble Response in Mushroom Body Output Neurons of the Honey Bee Outpaces Spatiotemporal Odor Processing Two Synapses Earlier in the Antennal Lobe

PubMed Central

Strube-Bloss, Martin F.; Herrera-Valdez, Marco A.; Smith, Brian H.

2012-01-01

Neural representations of odors are subject to computations that involve sequentially convergent and divergent anatomical connections across different areas of the brains in both mammals and insects. Furthermore, in both mammals and insects higher order brain areas are connected via feedback connections. In order to understand the transformations and interactions that this connectivity make possible, an ideal experiment would compare neural responses across different, sequential processing levels. Here we present results of recordings from a first order olfactory neuropile – the antennal lobe (AL) – and a higher order multimodal integration and learning center – the mushroom body (MB) – in the honey bee brain. We recorded projection neurons (PN) of the AL and extrinsic neurons (EN) of the MB, which provide the outputs from the two neuropils. Recordings at each level were made in different animals in some experiments and simultaneously in the same animal in others. We presented two odors and their mixture to compare odor response dynamics as well as classification speed and accuracy at each neural processing level. Surprisingly, the EN ensemble significantly starts separating odor stimuli rapidly and before the PN ensemble has reached significant separation. Furthermore the EN ensemble at the MB output reaches a maximum separation of odors between 84–120 ms after odor onset, which is 26 to 133 ms faster than the maximum separation at the AL output ensemble two synapses earlier in processing. It is likely that a subset of very fast PNs, which respond before the ENs, may initiate the rapid EN ensemble response. We suggest therefore that the timing of the EN ensemble activity would allow retroactive integration of its signal into the ongoing computation of the AL via centrifugal feedback. PMID:23209711

Phasic and tonic neuron ensemble codes for stimulus-environment conjunctions in the lateral entorhinal cortex.

PubMed

Pilkiw, Maryna; Insel, Nathan; Cui, Younghua; Finney, Caitlin; Morrissey, Mark D; Takehara-Nishiuchi, Kaori

2017-07-06

The lateral entorhinal cortex (LEC) is thought to bind sensory events with the environment where they took place. To compare the relative influence of transient events and temporally stable environmental stimuli on the firing of LEC cells, we recorded neuron spiking patterns in the region during blocks of a trace eyeblink conditioning paradigm performed in two environments and with different conditioning stimuli. Firing rates of some neurons were phasically selective for conditioned stimuli in a way that depended on which room the rat was in; nearly all neurons were tonically selective for environments in a way that depended on which stimuli had been presented in those environments. As rats moved from one environment to another, tonic neuron ensemble activity exhibited prospective information about the conditioned stimulus associated with the environment. Thus, the LEC formed phasic and tonic codes for event-environment associations, thereby accurately differentiating multiple experiences with overlapping features.

Prefrontal neural correlates of memory for sequences.

PubMed

Averbeck, Bruno B; Lee, Daeyeol

2007-02-28

The sequence of actions appropriate to solve a problem often needs to be discovered by trial and error and recalled in the future when faced with the same problem. Here, we show that when monkeys had to discover and then remember a sequence of decisions across trials, ensembles of prefrontal cortex neurons reflected the sequence of decisions the animal would make throughout the interval between trials. This signal could reflect either an explicit memory process or a sequence-planning process that begins far in advance of the actual sequence execution. This finding extended to error trials such that, when the neural activity during the intertrial interval specified the wrong sequence, the animal also attempted to execute an incorrect sequence. More specifically, we used a decoding analysis to predict the sequence the monkey was planning to execute at the end of the fore-period, just before sequence execution. When this analysis was applied to error trials, we were able to predict where in the sequence the error would occur, up to three movements into the future. This suggests that prefrontal neural activity can retain information about sequences between trials, and that regardless of whether information is remembered correctly or incorrectly, the prefrontal activity veridically reflects the animal's action plan.

The random coding bound is tight for the average code.

NASA Technical Reports Server (NTRS)

Gallager, R. G.

1973-01-01

The random coding bound of information theory provides a well-known upper bound to the probability of decoding error for the best code of a given rate and block length. The bound is constructed by upperbounding the average error probability over an ensemble of codes. The bound is known to give the correct exponential dependence of error probability on block length for transmission rates above the critical rate, but it gives an incorrect exponential dependence at rates below a second lower critical rate. Here we derive an asymptotic expression for the average error probability over the ensemble of codes used in the random coding bound. The result shows that the weakness of the random coding bound at rates below the second critical rate is due not to upperbounding the ensemble average, but rather to the fact that the best codes are much better than the average at low rates.

Spatial Memory Engram in the Mouse Retrosplenial Cortex.

PubMed

Milczarek, Michal M; Vann, Seralynne D; Sengpiel, Frank

2018-06-18

Memory relies on lasting adaptations of neuronal properties elicited by stimulus-driven plastic changes [1]. The strengthening (and weakening) of synapses results in the establishment of functional ensembles. It is presumed that such ensembles (or engrams) are activated during memory acquisition and re-activated upon memory retrieval. The retrosplenial cortex (RSC) has emerged as a key brain area supporting memory [2], including episodic and topographical memory in humans [3-5], as well as spatial memory in rodents [6, 7]. Dysgranular RSC is densely connected with dorsal stream visual areas [8] and contains place-like and head-direction cells, making it a prime candidate for integrating navigational information [9]. While previous reports [6, 10] describe the recruitment of RSC ensembles during navigational tasks, such ensembles have never been tracked long enough to provide evidence of stable engrams and have not been related to the retention of long-term memory. Here, we used in vivo 2-photon imaging to analyze patterns of activity of over 6,000 neurons within dysgranular RSC. Eight mice were trained on a spatial memory task. Learning was accompanied by the gradual emergence of a context-specific pattern of neuronal activity over a 3-week period, which was re-instated upon retrieval more than 3 weeks later. The stability of this memory engram was predictive of the degree of forgetting; more stable engrams were associated with better performance. This provides direct evidence for the interdependence of spatial memory consolidation and RSC engram formation. Our results demonstrate the participation of RSC in spatial memory storage at the level of neuronal ensembles. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Cortical Neural Computation by Discrete Results Hypothesis

PubMed Central

Castejon, Carlos; Nuñez, Angel

2016-01-01

One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called “Discrete Results” (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of “Discrete Results” is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel “Discrete Results” concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast-spiking (FS) interneuron may be a key element in our hypothesis providing the basis for this computation. PMID:27807408

Cortical Neural Computation by Discrete Results Hypothesis.

PubMed

Castejon, Carlos; Nuñez, Angel

2016-01-01

One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called "Discrete Results" (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of "Discrete Results" is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel "Discrete Results" concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast-spiking (FS) interneuron may be a key element in our hypothesis providing the basis for this computation.

Noise-robust speech recognition through auditory feature detection and spike sequence decoding.

PubMed

Schafer, Phillip B; Jin, Dezhe Z

2014-03-01

Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.

Error-based analysis of optimal tuning functions explains phenomena observed in sensory neurons.

PubMed

Yaeli, Steve; Meir, Ron

2010-01-01

Biological systems display impressive capabilities in effectively responding to environmental signals in real time. There is increasing evidence that organisms may indeed be employing near optimal Bayesian calculations in their decision-making. An intriguing question relates to the properties of optimal encoding methods, namely determining the properties of neural populations in sensory layers that optimize performance, subject to physiological constraints. Within an ecological theory of neural encoding/decoding, we show that optimal Bayesian performance requires neural adaptation which reflects environmental changes. Specifically, we predict that neuronal tuning functions possess an optimal width, which increases with prior uncertainty and environmental noise, and decreases with the decoding time window. Furthermore, even for static stimuli, we demonstrate that dynamic sensory tuning functions, acting at relatively short time scales, lead to improved performance. Interestingly, the narrowing of tuning functions as a function of time was recently observed in several biological systems. Such results set the stage for a functional theory which may explain the high reliability of sensory systems, and the utility of neuronal adaptation occurring at multiple time scales.

A stimulus-dependent spike threshold is an optimal neural coder

PubMed Central

Jones, Douglas L.; Johnson, Erik C.; Ratnam, Rama

2015-01-01

A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code. PMID:26082710

Adaptive Encoding of Outcome Prediction by Prefrontal Cortex Ensembles Supports Behavioral Flexibility.

PubMed

Del Arco, Alberto; Park, Junchol; Wood, Jesse; Kim, Yunbok; Moghaddam, Bita

2017-08-30

The prefrontal cortex (PFC) is thought to play a critical role in behavioral flexibility by monitoring action-outcome contingencies. How PFC ensembles represent shifts in behavior in response to changes in these contingencies remains unclear. We recorded single-unit activity and local field potentials in the dorsomedial PFC (dmPFC) of male rats during a set-shifting task that required them to update their behavior, among competing options, in response to changes in action-outcome contingencies. As behavior was updated, a subset of PFC ensembles encoded the current trial outcome before the outcome was presented. This novel outcome-prediction encoding was absent in a control task, in which actions were rewarded pseudorandomly, indicating that PFC neurons are not merely providing an expectancy signal. In both control and set-shifting tasks, dmPFC neurons displayed postoutcome discrimination activity, indicating that these neurons also monitor whether a behavior is successful in generating rewards. Gamma-power oscillatory activity increased before the outcome in both tasks but did not differentiate between expected outcomes, suggesting that this measure is not related to set-shifting behavior but reflects expectation of an outcome after action execution. These results demonstrate that PFC neurons support flexible rule-based action selection by predicting outcomes that follow a particular action. SIGNIFICANCE STATEMENT Tracking action-outcome contingencies and modifying behavior when those contingencies change is critical to behavioral flexibility. We find that ensembles of dorsomedial prefrontal cortex neurons differentiate between expected outcomes when action-outcome contingencies change. This predictive mode of signaling may be used to promote a new response strategy at the service of behavioral flexibility. Copyright © 2017 the authors 0270-6474/17/378363-11$15.00/0.

Nonlinear Modeling of Causal Interrelationships in Neuronal Ensembles

PubMed Central

Zanos, Theodoros P.; Courellis, Spiros H.; Berger, Theodore W.; Hampson, Robert E.; Deadwyler, Sam A.; Marmarelis, Vasilis Z.

2009-01-01

The increasing availability of multiunit recordings gives new urgency to the need for effective analysis of “multidimensional” time-series data that are derived from the recorded activity of neuronal ensembles in the form of multiple sequences of action potentials—treated mathematically as point-processes and computationally as spike-trains. Whether in conditions of spontaneous activity or under conditions of external stimulation, the objective is the identification and quantification of possible causal links among the neurons generating the observed binary signals. A multiple-input/multiple-output (MIMO) modeling methodology is presented that can be used to quantify the neuronal dynamics of causal interrelationships in neuronal ensembles using spike-train data recorded from individual neurons. These causal interrelationships are modeled as transformations of spike-trains recorded from a set of neurons designated as the “inputs” into spike-trains recorded from another set of neurons designated as the “outputs.” The MIMO model is composed of a set of multiinput/single-output (MISO) modules, one for each output. Each module is the cascade of a MISO Volterra model and a threshold operator generating the output spikes. The Laguerre expansion approach is used to estimate the Volterra kernels of each MISO module from the respective input–output data using the least-squares method. The predictive performance of the model is evaluated with the use of the receiver operating characteristic (ROC) curve, from which the optimum threshold is also selected. The Mann–Whitney statistic is used to select the significant inputs for each output by examining the statistical significance of improvements in the predictive accuracy of the model when the respective inputs is included. Illustrative examples are presented for a simulated system and for an actual application using multiunit data recordings from the hippocampus of a behaving rat. PMID:18701382

Anti-correlated cortical networks arise from spontaneous neuronal dynamics at slow timescales.

PubMed

Kodama, Nathan X; Feng, Tianyi; Ullett, James J; Chiel, Hillel J; Sivakumar, Siddharth S; Galán, Roberto F

2018-01-12

In the highly interconnected architectures of the cerebral cortex, recurrent intracortical loops disproportionately outnumber thalamo-cortical inputs. These networks are also capable of generating neuronal activity without feedforward sensory drive. It is unknown, however, what spatiotemporal patterns may be solely attributed to intrinsic connections of the local cortical network. Using high-density microelectrode arrays, here we show that in the isolated, primary somatosensory cortex of mice, neuronal firing fluctuates on timescales from milliseconds to tens of seconds. Slower firing fluctuations reveal two spatially distinct neuronal ensembles, which correspond to superficial and deeper layers. These ensembles are anti-correlated: when one fires more, the other fires less and vice versa. This interplay is clearest at timescales of several seconds and is therefore consistent with shifts between active sensing and anticipatory behavioral states in mice.

An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces.

PubMed

Li, Simin; Li, Jie; Li, Zheng

2016-01-01

Brain-machine interfaces (BMIs) seek to connect brains with machines or computers directly, for application in areas such as prosthesis control. For this application, the accuracy of the decoding of movement intentions is crucial. We aim to improve accuracy by designing a better encoding model of primary motor cortical activity during hand movements and combining this with decoder engineering refinements, resulting in a new unscented Kalman filter based decoder, UKF2, which improves upon our previous unscented Kalman filter decoder, UKF1. The new encoding model includes novel acceleration magnitude, position-velocity interaction, and target-cursor-distance features (the decoder does not require target position as input, it is decoded). We add a novel probabilistic velocity threshold to better determine the user's intent to move. We combine these improvements with several other refinements suggested by others in the field. Data from two Rhesus monkeys indicate that the UKF2 generates offline reconstructions of hand movements (mean CC 0.851) significantly more accurately than the UKF1 (0.833) and the popular position-velocity Kalman filter (0.812). The encoding model of the UKF2 could predict the instantaneous firing rate of neurons (mean CC 0.210), given kinematic variables and past spiking, better than the encoding models of these two decoders (UKF1: 0.138, p-v Kalman: 0.098). In closed-loop experiments where each monkey controlled a computer cursor with each decoder in turn, the UKF2 facilitated faster task completion (mean 1.56 s vs. 2.05 s) and higher Fitts's Law bit rate (mean 0.738 bit/s vs. 0.584 bit/s) than the UKF1. These results suggest that the modeling and decoder engineering refinements of the UKF2 improve decoding performance. We believe they can be used to enhance other decoders as well.

An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces

PubMed Central

Li, Simin; Li, Jie; Li, Zheng

2016-01-01

Brain-machine interfaces (BMIs) seek to connect brains with machines or computers directly, for application in areas such as prosthesis control. For this application, the accuracy of the decoding of movement intentions is crucial. We aim to improve accuracy by designing a better encoding model of primary motor cortical activity during hand movements and combining this with decoder engineering refinements, resulting in a new unscented Kalman filter based decoder, UKF2, which improves upon our previous unscented Kalman filter decoder, UKF1. The new encoding model includes novel acceleration magnitude, position-velocity interaction, and target-cursor-distance features (the decoder does not require target position as input, it is decoded). We add a novel probabilistic velocity threshold to better determine the user's intent to move. We combine these improvements with several other refinements suggested by others in the field. Data from two Rhesus monkeys indicate that the UKF2 generates offline reconstructions of hand movements (mean CC 0.851) significantly more accurately than the UKF1 (0.833) and the popular position-velocity Kalman filter (0.812). The encoding model of the UKF2 could predict the instantaneous firing rate of neurons (mean CC 0.210), given kinematic variables and past spiking, better than the encoding models of these two decoders (UKF1: 0.138, p-v Kalman: 0.098). In closed-loop experiments where each monkey controlled a computer cursor with each decoder in turn, the UKF2 facilitated faster task completion (mean 1.56 s vs. 2.05 s) and higher Fitts's Law bit rate (mean 0.738 bit/s vs. 0.584 bit/s) than the UKF1. These results suggest that the modeling and decoder engineering refinements of the UKF2 improve decoding performance. We believe they can be used to enhance other decoders as well. PMID:28066170

A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects.

PubMed

Cacha, L A; Parida, S; Dehuri, S; Cho, S-B; Poznanski, R R

2016-12-01

The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.

On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity.

PubMed

Xu, Zhiming; So, Rosa Q; Toe, Kyaw Kyar; Ang, Kai Keng; Guan, Cuntai

2014-01-01

This paper presents an asynchronously intracortical brain-computer interface (BCI) which allows the subject to continuously drive a mobile robot. This system has a great implication for disabled patients to move around. By carefully designing a multiclass support vector machine (SVM), the subject's self-paced instantaneous movement intents are continuously decoded to control the mobile robot. In particular, we studied the stability of the neural representation of the movement directions. Experimental results on the nonhuman primate showed that the overt movement directions were stably represented in ensemble of recorded units, and our SVM classifier could successfully decode such movements continuously along the desired movement path. However, the neural representation of the stop state for the self-paced control was not stably represented and could drift.

Robust Nonlinear Neural Codes

NASA Astrophysics Data System (ADS)

Yang, Qianli; Pitkow, Xaq

2015-03-01

Most interesting natural sensory stimuli are encoded in the brain in a form that can only be decoded nonlinearly. But despite being a core function of the brain, nonlinear population codes are rarely studied and poorly understood. Interestingly, the few existing models of nonlinear codes are inconsistent with known architectural features of the brain. In particular, these codes have information content that scales with the size of the cortical population, even if that violates the data processing inequality by exceeding the amount of information entering the sensory system. Here we provide a valid theory of nonlinear population codes by generalizing recent work on information-limiting correlations in linear population codes. Although these generalized, nonlinear information-limiting correlations bound the performance of any decoder, they also make decoding more robust to suboptimal computation, allowing many suboptimal decoders to achieve nearly the same efficiency as an optimal decoder. Although these correlations are extremely difficult to measure directly, particularly for nonlinear codes, we provide a simple, practical test by which one can use choice-related activity in small populations of neurons to determine whether decoding is suboptimal or optimal and limited by correlated noise. We conclude by describing an example computation in the vestibular system where this theory applies. QY and XP was supported by a grant from the McNair foundation.

Nonlinear decoding of a complex movie from the mammalian retina

PubMed Central

Deny, Stéphane; Martius, Georg

2018-01-01

Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed “pixel-by-pixel”. We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains. PMID:29746463

Timing, timing, timing: Fast decoding of object information from intracranial field potentials in human visual cortex

PubMed Central

Liu, Hesheng; Agam, Yigal; Madsen, Joseph R.; Kreiman, Gabriel

2010-01-01

Summary The difficulty of visual recognition stems from the need to achieve high selectivity while maintaining robustness to object transformations within hundreds of milliseconds. Theories of visual recognition differ in whether the neuronal circuits invoke recurrent feedback connections or not. The timing of neurophysiological responses in visual cortex plays a key role in distinguishing between bottom-up and top-down theories. Here we quantified at millisecond resolution the amount of visual information conveyed by intracranial field potentials from 912 electrodes in 11 human subjects. We could decode object category information from human visual cortex in single trials as early as 100 ms post-stimulus. Decoding performance was robust to depth rotation and scale changes. The results suggest that physiological activity in the temporal lobe can account for key properties of visual recognition. The fast decoding in single trials is compatible with feed-forward theories and provides strong constraints for computational models of human vision. PMID:19409272

Recording large-scale neuronal ensembles with silicon probes in the anesthetized rat.

PubMed

Schjetnan, Andrea Gomez Palacio; Luczak, Artur

2011-10-19

Large scale electrophysiological recordings from neuronal ensembles offer the opportunity to investigate how the brain orchestrates the wide variety of behaviors from the spiking activity of its neurons. One of the most effective methods to monitor spiking activity from a large number of neurons in multiple local neuronal circuits simultaneously is by using silicon electrode arrays. Action potentials produce large transmembrane voltage changes in the vicinity of cell somata. These output signals can be measured by placing a conductor in close proximity of a neuron. If there are many active (spiking) neurons in the vicinity of the tip, the electrode records combined signal from all of them, where contribution of a single neuron is weighted by its 'electrical distance'. Silicon probes are ideal recording electrodes to monitor multiple neurons because of a large number of recording sites (+64) and a small volume. Furthermore, multiple sites can be arranged over a distance of millimeters, thus allowing for the simultaneous recordings of neuronal activity in the various cortical layers or in multiple cortical columns (Fig. 1). Importantly, the geometrically precise distribution of the recording sites also allows for the determination of the spatial relationship of the isolated single neurons. Here, we describe an acute, large-scale neuronal recording from the left and right forelimb somatosensory cortex simultaneously in an anesthetized rat with silicon probes (Fig. 2).

Recording Large-scale Neuronal Ensembles with Silicon Probes in the Anesthetized Rat

PubMed Central

Schjetnan, Andrea Gomez Palacio; Luczak, Artur

2011-01-01

Large scale electrophysiological recordings from neuronal ensembles offer the opportunity to investigate how the brain orchestrates the wide variety of behaviors from the spiking activity of its neurons. One of the most effective methods to monitor spiking activity from a large number of neurons in multiple local neuronal circuits simultaneously is by using silicon electrode arrays1-3. Action potentials produce large transmembrane voltage changes in the vicinity of cell somata. These output signals can be measured by placing a conductor in close proximity of a neuron. If there are many active (spiking) neurons in the vicinity of the tip, the electrode records combined signal from all of them, where contribution of a single neuron is weighted by its 'electrical distance'. Silicon probes are ideal recording electrodes to monitor multiple neurons because of a large number of recording sites (+64) and a small volume. Furthermore, multiple sites can be arranged over a distance of millimeters, thus allowing for the simultaneous recordings of neuronal activity in the various cortical layers or in multiple cortical columns (Fig. 1). Importantly, the geometrically precise distribution of the recording sites also allows for the determination of the spatial relationship of the isolated single neurons4. Here, we describe an acute, large-scale neuronal recording from the left and right forelimb somatosensory cortex simultaneously in an anesthetized rat with silicon probes (Fig. 2). PMID:22042361

Encoding of Olfactory Information with Oscillating Neural Assemblies

NASA Astrophysics Data System (ADS)

Laurent, Gilles; Davidowitz, Hananel

1994-09-01

In the brain, fast oscillations of local field potentials, which are thought to arise from the coherent and rhythmic activity of large numbers of neurons, were observed first in the olfactory system and have since been described in many neocortical areas. The importance of these oscillations in information coding, however, is controversial. Here, local field potential and intracellular recordings were obtained from the antennal lobe and mushroom body of the locust Schistocerca americana. Different odors evoked coherent oscillations in different, but usually overlapping, ensembles of neurons. The phase of firing of individual neurons relative to the population was not dependent on the odor. The components of a coherently oscillating ensemble of neurons changed over the duration of a single exposure to an odor. It is thus proposed that odors are encoded by specific but dynamic assemblies of coherently oscillating neurons. Such distributed and temporal representation of complex sensory signals may facilitate combinatorial coding and associative learning in these, and possibly other, sensory networks.

Phasic and tonic neuron ensemble codes for stimulus-environment conjunctions in the lateral entorhinal cortex

PubMed Central

Pilkiw, Maryna; Insel, Nathan; Cui, Younghua; Finney, Caitlin; Morrissey, Mark D; Takehara-Nishiuchi, Kaori

2017-01-01

The lateral entorhinal cortex (LEC) is thought to bind sensory events with the environment where they took place. To compare the relative influence of transient events and temporally stable environmental stimuli on the firing of LEC cells, we recorded neuron spiking patterns in the region during blocks of a trace eyeblink conditioning paradigm performed in two environments and with different conditioning stimuli. Firing rates of some neurons were phasically selective for conditioned stimuli in a way that depended on which room the rat was in; nearly all neurons were tonically selective for environments in a way that depended on which stimuli had been presented in those environments. As rats moved from one environment to another, tonic neuron ensemble activity exhibited prospective information about the conditioned stimulus associated with the environment. Thus, the LEC formed phasic and tonic codes for event-environment associations, thereby accurately differentiating multiple experiences with overlapping features. DOI: http://dx.doi.org/10.7554/eLife.28611.001 PMID:28682237

Decoding Face Information in Time, Frequency and Space from Direct Intracranial Recordings of the Human Brain

PubMed Central

Oya, Hiroyuki; Howard, Matthew A.; Adolphs, Ralph

2008-01-01

Faces are processed by a neural system with distributed anatomical components, but the roles of these components remain unclear. A dominant theory of face perception postulates independent representations of invariant aspects of faces (e.g., identity) in ventral temporal cortex including the fusiform gyrus, and changeable aspects of faces (e.g., emotion) in lateral temporal cortex including the superior temporal sulcus. Here we recorded neuronal activity directly from the cortical surface in 9 neurosurgical subjects undergoing epilepsy monitoring while they viewed static and dynamic facial expressions. Applying novel decoding analyses to the power spectrogram of electrocorticograms (ECoG) from over 100 contacts in ventral and lateral temporal cortex, we found better representation of both invariant and changeable aspects of faces in ventral than lateral temporal cortex. Critical information for discriminating faces from geometric patterns was carried by power modulations between 50 to 150 Hz. For both static and dynamic face stimuli, we obtained a higher decoding performance in ventral than lateral temporal cortex. For discriminating fearful from happy expressions, critical information was carried by power modulation between 60–150 Hz and below 30 Hz, and again better decoded in ventral than lateral temporal cortex. Task-relevant attention improved decoding accuracy more than10% across a wide frequency range in ventral but not at all in lateral temporal cortex. Spatial searchlight decoding showed that decoding performance was highest around the middle fusiform gyrus. Finally, we found that the right hemisphere, in general, showed superior decoding to the left hemisphere. Taken together, our results challenge the dominant model for independent face representation of invariant and changeable aspects: information about both face attributes was better decoded from a single region in the middle fusiform gyrus. PMID:19065268

Brain science: from the very small to the very large.

PubMed

Kreiman, Gabriel

2007-09-04

We still lack a clear understanding of how brain imaging signals relate to neuronal activity. Recent work shows that the simultaneous activity of neuronal ensembles strongly correlates with local field potentials and imaging measurements.

Detection of Error Related Neuronal Responses Recorded by Electrocorticography in Humans during Continuous Movements

PubMed Central

Milekovic, Tomislav; Ball, Tonio; Schulze-Bonhage, Andreas; Aertsen, Ad; Mehring, Carsten

2013-01-01

Background Brain-machine interfaces (BMIs) can translate the neuronal activity underlying a user’s movement intention into movements of an artificial effector. In spite of continuous improvements, errors in movement decoding are still a major problem of current BMI systems. If the difference between the decoded and intended movements becomes noticeable, it may lead to an execution error. Outcome errors, where subjects fail to reach a certain movement goal, are also present during online BMI operation. Detecting such errors can be beneficial for BMI operation: (i) errors can be corrected online after being detected and (ii) adaptive BMI decoding algorithm can be updated to make fewer errors in the future. Methodology/Principal Findings Here, we show that error events can be detected from human electrocorticography (ECoG) during a continuous task with high precision, given a temporal tolerance of 300–400 milliseconds. We quantified the error detection accuracy and showed that, using only a small subset of 2×2 ECoG electrodes, 82% of detection information for outcome error and 74% of detection information for execution error available from all ECoG electrodes could be retained. Conclusions/Significance The error detection method presented here could be used to correct errors made during BMI operation or to adapt a BMI algorithm to make fewer errors in the future. Furthermore, our results indicate that smaller ECoG implant could be used for error detection. Reducing the size of an ECoG electrode implant used for BMI decoding and error detection could significantly reduce the medical risk of implantation. PMID:23383315

Massively parallel neural circuits for stereoscopic color vision: encoding, decoding and identification.

PubMed

Lazar, Aurel A; Slutskiy, Yevgeniy B; Zhou, Yiyin

2015-03-01

Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived? We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit. Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modulation of neural activity by reward in medial intraparietal cortex is sensitive to temporal sequence of reward

PubMed Central

Rajalingham, Rishi; Stacey, Richard Greg; Tsoulfas, Georgios

2014-01-01

To restore movements to paralyzed patients, neural prosthetic systems must accurately decode patients' intentions from neural signals. Despite significant advancements, current systems are unable to restore complex movements. Decoding reward-related signals from the medial intraparietal area (MIP) could enhance prosthetic performance. However, the dynamics of reward sensitivity in MIP is not known. Furthermore, reward-related modulation in premotor areas has been attributed to behavioral confounds. Here we investigated the stability of reward encoding in MIP by assessing the effect of reward history on reward sensitivity. We recorded from neurons in MIP while monkeys performed a delayed-reach task under two reward schedules. In the variable schedule, an equal number of small- and large-rewards trials were randomly interleaved. In the constant schedule, one reward size was delivered for a block of trials. The memory period firing rate of most neurons in response to identical rewards varied according to schedule. Using systems identification tools, we attributed the schedule sensitivity to the dependence of neural activity on the history of reward. We did not find schedule-dependent behavioral changes, suggesting that reward modulates neural activity in MIP. Neural discrimination between rewards was less in the variable than in the constant schedule, degrading our ability to decode reach target and reward simultaneously. The effect of schedule was mitigated by adding Haar wavelet coefficients to the decoding model. This raises the possibility of multiple encoding schemes at different timescales and reinforces the potential utility of reward information for prosthetic performance. PMID:25008408

Modulation of neural activity by reward in medial intraparietal cortex is sensitive to temporal sequence of reward.

PubMed

Rajalingham, Rishi; Stacey, Richard Greg; Tsoulfas, Georgios; Musallam, Sam

2014-10-01

To restore movements to paralyzed patients, neural prosthetic systems must accurately decode patients' intentions from neural signals. Despite significant advancements, current systems are unable to restore complex movements. Decoding reward-related signals from the medial intraparietal area (MIP) could enhance prosthetic performance. However, the dynamics of reward sensitivity in MIP is not known. Furthermore, reward-related modulation in premotor areas has been attributed to behavioral confounds. Here we investigated the stability of reward encoding in MIP by assessing the effect of reward history on reward sensitivity. We recorded from neurons in MIP while monkeys performed a delayed-reach task under two reward schedules. In the variable schedule, an equal number of small- and large-rewards trials were randomly interleaved. In the constant schedule, one reward size was delivered for a block of trials. The memory period firing rate of most neurons in response to identical rewards varied according to schedule. Using systems identification tools, we attributed the schedule sensitivity to the dependence of neural activity on the history of reward. We did not find schedule-dependent behavioral changes, suggesting that reward modulates neural activity in MIP. Neural discrimination between rewards was less in the variable than in the constant schedule, degrading our ability to decode reach target and reward simultaneously. The effect of schedule was mitigated by adding Haar wavelet coefficients to the decoding model. This raises the possibility of multiple encoding schemes at different timescales and reinforces the potential utility of reward information for prosthetic performance. Copyright © 2014 the American Physiological Society.

Large-Scale Fluorescence Calcium-Imaging Methods for Studies of Long-Term Memory in Behaving Mammals

PubMed Central

Jercog, Pablo; Rogerson, Thomas; Schnitzer, Mark J.

2016-01-01

During long-term memory formation, cellular and molecular processes reshape how individual neurons respond to specific patterns of synaptic input. It remains poorly understood how such changes impact information processing across networks of mammalian neurons. To observe how networks encode, store, and retrieve information, neuroscientists must track the dynamics of large ensembles of individual cells in behaving animals, over timescales commensurate with long-term memory. Fluorescence Ca2+-imaging techniques can monitor hundreds of neurons in behaving mice, opening exciting avenues for studies of learning and memory at the network level. Genetically encoded Ca2+ indicators allow neurons to be targeted by genetic type or connectivity. Chronic animal preparations permit repeated imaging of neural Ca2+ dynamics over multiple weeks. Together, these capabilities should enable unprecedented analyses of how ensemble neural codes evolve throughout memory processing and provide new insights into how memories are organized in the brain. PMID:27048190

Coding of position by simultaneously recorded sensory neurones in the cat dorsal root ganglion

PubMed Central

Stein, R B; Weber, D J; Aoyagi, Y; Prochazka, A; Wagenaar, J B M; Shoham, S; Normann, R A

2004-01-01

Muscle, cutaneous and joint afferents continuously signal information about the position and movement of individual joints. How does the nervous system extract more global information, for example about the position of the foot in space? To study this question we used microelectrode arrays to record impulses simultaneously from up to 100 discriminable nerve cells in the L6 and L7 dorsal root ganglia (DRG) of the anaesthetized cat. When the hindlimb was displaced passively with a random trajectory, the firing rate of the neurones could be predicted from a linear sum of positions and velocities in Cartesian (x, y), polar or joint angular coordinates. The process could also be reversed to predict the kinematics of the limb from the firing rates of the neurones with an accuracy of 1–2 cm. Predictions of position and velocity could be combined to give an improved fit to limb position. Decoders trained using random movements successfully predicted cyclic movements and movements in which the limb was displaced from a central point to various positions in the periphery. A small number of highly informative neurones (6–8) could account for over 80% of the variance in position and a similar result was obtained in a realistic limb model. In conclusion, this work illustrates how populations of sensory receptors may encode a sense of limb position and how the firing of even a small number of neurones can be used to decode the position of the limb in space. PMID:15331686

Feature selection for the classification of traced neurons.

PubMed

López-Cabrera, José D; Lorenzo-Ginori, Juan V

2018-06-01

The great availability of computational tools to calculate the properties of traced neurons leads to the existence of many descriptors which allow the automated classification of neurons from these reconstructions. This situation determines the necessity to eliminate irrelevant features as well as making a selection of the most appropriate among them, in order to improve the quality of the classification obtained. The dataset used contains a total of 318 traced neurons, classified by human experts in 192 GABAergic interneurons and 126 pyramidal cells. The features were extracted by means of the L-measure software, which is one of the most used computational tools in neuroinformatics to quantify traced neurons. We review some current feature selection techniques as filter, wrapper, embedded and ensemble methods. The stability of the feature selection methods was measured. For the ensemble methods, several aggregation methods based on different metrics were applied to combine the subsets obtained during the feature selection process. The subsets obtained applying feature selection methods were evaluated using supervised classifiers, among which Random Forest, C4.5, SVM, Naïve Bayes, Knn, Decision Table and the Logistic classifier were used as classification algorithms. Feature selection methods of types filter, embedded, wrappers and ensembles were compared and the subsets returned were tested in classification tasks for different classification algorithms. L-measure features EucDistanceSD, PathDistanceSD, Branch_pathlengthAve, Branch_pathlengthSD and EucDistanceAve were present in more than 60% of the selected subsets which provides evidence about their importance in the classification of this neurons. Copyright © 2018 Elsevier B.V. All rights reserved.

Response sensitivity of barrel neuron subpopulations to simulated thalamic input.

PubMed

Pesavento, Michael J; Rittenhouse, Cynthia D; Pinto, David J

2010-06-01

Our goal is to examine the relationship between neuron- and network-level processing in the context of a well-studied cortical function, the processing of thalamic input by whisker-barrel circuits in rodent neocortex. Here we focus on neuron-level processing and investigate the responses of excitatory and inhibitory barrel neurons to simulated thalamic inputs applied using the dynamic clamp method in brain slices. Simulated inputs are modeled after real thalamic inputs recorded in vivo in response to brief whisker deflections. Our results suggest that inhibitory neurons require more input to reach firing threshold, but then fire earlier, with less variability, and respond to a broader range of inputs than do excitatory neurons. Differences in the responses of barrel neuron subtypes depend on their intrinsic membrane properties. Neurons with a low input resistance require more input to reach threshold but then fire earlier than neurons with a higher input resistance, regardless of the neuron's classification. Our results also suggest that the response properties of excitatory versus inhibitory barrel neurons are consistent with the response sensitivities of the ensemble barrel network. The short response latency of inhibitory neurons may serve to suppress ensemble barrel responses to asynchronous thalamic input. Correspondingly, whereas neurons acting as part of the barrel circuit in vivo are highly selective for temporally correlated thalamic input, excitatory barrel neurons acting alone in vitro are less so. These data suggest that network-level processing of thalamic input in barrel cortex depends on neuron-level processing of the same input by excitatory and inhibitory barrel neurons.

Role of Central Amygdala Neuronal Ensembles in Incubation of Nicotine Craving.

PubMed

Funk, Douglas; Coen, Kathleen; Tamadon, Sahar; Hope, Bruce T; Shaham, Yavin; Lê, A D

2016-08-17

The craving response to smoking-associated cues in humans or to intravenous nicotine-associated cues in adult rats progressively increases or incubates after withdrawal. Here, we further characterized incubation of nicotine craving in the rat model by determining whether this incubation is observed after adolescent-onset nicotine self-administration. We also used the neuronal activity marker Fos and the Daun02 chemogenetic inactivation procedure to identify cue-activated neuronal ensembles that mediate incubation of nicotine craving. We trained adolescent and adult male rats to self-administer nicotine (2 h/d for 12 d) and assessed cue-induced nicotine seeking in extinction tests (1 h) after 1, 7, 14, or 28 withdrawal days. In both adult and adolescent rats, nicotine seeking in the relapse tests followed an inverted U-shaped curve, with maximal responding on withdrawal day 14. Independent of the withdrawal day, nicotine seeking in the relapse tests was higher in adult than in adolescent rats. Analysis of Fos expression in different brain areas of adolescent and adult rats on withdrawal days 1 and 14 showed time-dependent increases in the number of Fos-positive neurons in central and basolateral amygdala, orbitofrontal cortex, ventral and dorsal medial prefrontal cortex, and nucleus accumbens core and shell. In adult Fos-lacZ transgenic rats, selective inactivation of nicotine-cue-activated Fos neurons in central amygdala, but not orbitofrontal cortex, decreased "incubated" nicotine seeking on withdrawal day 14. Our results demonstrate that incubation of nicotine craving occurs after adolescent-onset nicotine self-administration and that neuronal ensembles in central amygdala play a critical role in this incubation. The craving response to smoking-associated cues in humans or to intravenous nicotine-associated cues in adult rats progressively increases or incubates after withdrawal. It is currently unknown whether incubation of craving also occurs after adolescent-onset nicotine self-administration. The brain areas that mediate such incubation are also unknown. Here, we used a rat model of incubation of drug craving, the neuronal activity marker Fos, and the Daun02 chemogenetic inactivation method to demonstrate that incubation of nicotine craving is also observed after adolescent-onset nicotine self-administration and that neuronal ensembles in the central nucleus of the amygdala play a critical role in this incubation in adult rats. Copyright © 2016 the authors 0270-6474/16/368612-12$15.00/0.

Role of Central Amygdala Neuronal Ensembles in Incubation of Nicotine Craving

PubMed Central

Coen, Kathleen; Tamadon, Sahar; Hope, Bruce T.; Shaham, Yavin; Lê, A.D.

2016-01-01

The craving response to smoking-associated cues in humans or to intravenous nicotine-associated cues in adult rats progressively increases or incubates after withdrawal. Here, we further characterized incubation of nicotine craving in the rat model by determining whether this incubation is observed after adolescent-onset nicotine self-administration. We also used the neuronal activity marker Fos and the Daun02 chemogenetic inactivation procedure to identify cue-activated neuronal ensembles that mediate incubation of nicotine craving. We trained adolescent and adult male rats to self-administer nicotine (2 h/d for 12 d) and assessed cue-induced nicotine seeking in extinction tests (1 h) after 1, 7, 14, or 28 withdrawal days. In both adult and adolescent rats, nicotine seeking in the relapse tests followed an inverted U-shaped curve, with maximal responding on withdrawal day 14. Independent of the withdrawal day, nicotine seeking in the relapse tests was higher in adult than in adolescent rats. Analysis of Fos expression in different brain areas of adolescent and adult rats on withdrawal days 1 and 14 showed time-dependent increases in the number of Fos-positive neurons in central and basolateral amygdala, orbitofrontal cortex, ventral and dorsal medial prefrontal cortex, and nucleus accumbens core and shell. In adult Fos–lacZ transgenic rats, selective inactivation of nicotine-cue-activated Fos neurons in central amygdala, but not orbitofrontal cortex, decreased “incubated” nicotine seeking on withdrawal day 14. Our results demonstrate that incubation of nicotine craving occurs after adolescent-onset nicotine self-administration and that neuronal ensembles in central amygdala play a critical role in this incubation. SIGNIFICANCE STATEMENT The craving response to smoking-associated cues in humans or to intravenous nicotine-associated cues in adult rats progressively increases or incubates after withdrawal. It is currently unknown whether incubation of craving also occurs after adolescent-onset nicotine self-administration. The brain areas that mediate such incubation are also unknown. Here, we used a rat model of incubation of drug craving, the neuronal activity marker Fos, and the Daun02 chemogenetic inactivation method to demonstrate that incubation of nicotine craving is also observed after adolescent-onset nicotine self-administration and that neuronal ensembles in the central nucleus of the amygdala play a critical role in this incubation in adult rats. PMID:27535909

Coding and decoding with dendrites.

PubMed

Papoutsi, Athanasia; Kastellakis, George; Psarrou, Maria; Anastasakis, Stelios; Poirazi, Panayiota

2014-02-01

Since the discovery of complex, voltage dependent mechanisms in the dendrites of multiple neuron types, great effort has been devoted in search of a direct link between dendritic properties and specific neuronal functions. Over the last few years, new experimental techniques have allowed the visualization and probing of dendritic anatomy, plasticity and integrative schemes with unprecedented detail. This vast amount of information has caused a paradigm shift in the study of memory, one of the most important pursuits in Neuroscience, and calls for the development of novel theories and models that will unify the available data according to some basic principles. Traditional models of memory considered neural cells as the fundamental processing units in the brain. Recent studies however are proposing new theories in which memory is not only formed by modifying the synaptic connections between neurons, but also by modifications of intrinsic and anatomical dendritic properties as well as fine tuning of the wiring diagram. In this review paper we present previous studies along with recent findings from our group that support a key role of dendrites in information processing, including the encoding and decoding of new memories, both at the single cell and the network level. Copyright © 2013 Elsevier Ltd. All rights reserved.

Development of closed-loop neural interface technology in a rat model: combining motor cortex operant conditioning with visual cortex microstimulation.

PubMed

Marzullo, Timothy Charles; Lehmkuhle, Mark J; Gage, Gregory J; Kipke, Daryl R

2010-04-01

Closed-loop neural interface technology that combines neural ensemble decoding with simultaneous electrical microstimulation feedback is hypothesized to improve deep brain stimulation techniques, neuromotor prosthetic applications, and epilepsy treatment. Here we describe our iterative results in a rat model of a sensory and motor neurophysiological feedback control system. Three rats were chronically implanted with microelectrode arrays in both the motor and visual cortices. The rats were subsequently trained over a period of weeks to modulate their motor cortex ensemble unit activity upon delivery of intra-cortical microstimulation (ICMS) of the visual cortex in order to receive a food reward. Rats were given continuous feedback via visual cortex ICMS during the response periods that was representative of the motor cortex ensemble dynamics. Analysis revealed that the feedback provided the animals with indicators of the behavioral trials. At the hardware level, this preparation provides a tractable test model for improving the technology of closed-loop neural devices.

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons.

PubMed

Tyukin, Ivan; Gorban, Alexander N; Calvo, Carlos; Makarova, Julia; Makarov, Valeri A

2018-03-19

Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as the hippocampus. In this particular case, single neurons in the CA1 region receive a highly multidimensional input from the CA3 area, which is a hub for information processing. We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing. We show that single neurons can selectively detect and learn arbitrary information items, given that they operate in high dimensions. The argument is based on stochastic separation theorems and the concentration of measure phenomena. We demonstrate that a simple enough functional neuronal model is capable of explaining: (i) the extreme selectivity of single neurons to the information content, (ii) simultaneous separation of several uncorrelated stimuli or informational items from a large set, and (iii) dynamic learning of new items by associating them with already "known" ones. These results constitute a basis for organization of complex memories in ensembles of single neurons. Moreover, they show that no a priori assumptions on the structural organization of neuronal ensembles are necessary for explaining basic concepts of static and dynamic memories.

Decoding visual object categories from temporal correlations of ECoG signals.

PubMed

Majima, Kei; Matsuo, Takeshi; Kawasaki, Keisuke; Kawai, Kensuke; Saito, Nobuhito; Hasegawa, Isao; Kamitani, Yukiyasu

2014-04-15

How visual object categories are represented in the brain is one of the key questions in neuroscience. Studies on low-level visual features have shown that relative timings or phases of neural activity between multiple brain locations encode information. However, whether such temporal patterns of neural activity are used in the representation of visual objects is unknown. Here, we examined whether and how visual object categories could be predicted (or decoded) from temporal patterns of electrocorticographic (ECoG) signals from the temporal cortex in five patients with epilepsy. We used temporal correlations between electrodes as input features, and compared the decoding performance with features defined by spectral power and phase from individual electrodes. While using power or phase alone, the decoding accuracy was significantly better than chance, correlations alone or those combined with power outperformed other features. Decoding performance with correlations was degraded by shuffling the order of trials of the same category in each electrode, indicating that the relative time series between electrodes in each trial is critical. Analysis using a sliding time window revealed that decoding performance with correlations began to rise earlier than that with power. This earlier increase in performance was replicated by a model using phase differences to encode categories. These results suggest that activity patterns arising from interactions between multiple neuronal units carry additional information on visual object categories. Copyright © 2013 Elsevier Inc. All rights reserved.

Neuroscience-inspired computational systems for speech recognition under noisy conditions

NASA Astrophysics Data System (ADS)

Schafer, Phillip B.

Humans routinely recognize speech in challenging acoustic environments with background music, engine sounds, competing talkers, and other acoustic noise. However, today's automatic speech recognition (ASR) systems perform poorly in such environments. In this dissertation, I present novel methods for ASR designed to approach human-level performance by emulating the brain's processing of sounds. I exploit recent advances in auditory neuroscience to compute neuron-based representations of speech, and design novel methods for decoding these representations to produce word transcriptions. I begin by considering speech representations modeled on the spectrotemporal receptive fields of auditory neurons. These representations can be tuned to optimize a variety of objective functions, which characterize the response properties of a neural population. I propose an objective function that explicitly optimizes the noise invariance of the neural responses, and find that it gives improved performance on an ASR task in noise compared to other objectives. The method as a whole, however, fails to significantly close the performance gap with humans. I next consider speech representations that make use of spiking model neurons. The neurons in this method are feature detectors that selectively respond to spectrotemporal patterns within short time windows in speech. I consider a number of methods for training the response properties of the neurons. In particular, I present a method using linear support vector machines (SVMs) and show that this method produces spikes that are robust to additive noise. I compute the spectrotemporal receptive fields of the neurons for comparison with previous physiological results. To decode the spike-based speech representations, I propose two methods designed to work on isolated word recordings. The first method uses a classical ASR technique based on the hidden Markov model. The second method is a novel template-based recognition scheme that takes advantage of the neural representation's invariance in noise. The scheme centers on a speech similarity measure based on the longest common subsequence between spike sequences. The combined encoding and decoding scheme outperforms a benchmark system in extremely noisy acoustic conditions. Finally, I consider methods for decoding spike representations of continuous speech. To help guide the alignment of templates to words, I design a syllable detection scheme that robustly marks the locations of syllabic nuclei. The scheme combines SVM-based training with a peak selection algorithm designed to improve noise tolerance. By incorporating syllable information into the ASR system, I obtain strong recognition results in noisy conditions, although the performance in noiseless conditions is below the state of the art. The work presented here constitutes a novel approach to the problem of ASR that can be applied in the many challenging acoustic environments in which we use computer technologies today. The proposed spike-based processing methods can potentially be exploited in effcient hardware implementations and could significantly reduce the computational costs of ASR. The work also provides a framework for understanding the advantages of spike-based acoustic coding in the human brain.

Decoding stimulus features in primate somatosensory cortex during perceptual categorization

PubMed Central

Alvarez, Manuel; Zainos, Antonio; Romo, Ranulfo

2015-01-01

Neurons of the primary somatosensory cortex (S1) respond as functions of frequency or amplitude of a vibrotactile stimulus. However, whether S1 neurons encode both frequency and amplitude of the vibrotactile stimulus or whether each sensory feature is encoded by separate populations of S1 neurons is not known, To further address these questions, we recorded S1 neurons while trained monkeys categorized only one sensory feature of the vibrotactile stimulus: frequency, amplitude, or duration. The results suggest a hierarchical encoding scheme in S1: from neurons that encode all sensory features of the vibrotactile stimulus to neurons that encode only one sensory feature. We hypothesize that the dynamic representation of each sensory feature in S1 might serve for further downstream processing that leads to the monkey’s psychophysical behavior observed in these tasks. PMID:25825711

Task-Dependent Changes in Cross-Level Coupling between Single Neurons and Oscillatory Activity in Multiscale Networks

PubMed Central

Canolty, Ryan T.; Ganguly, Karunesh; Carmena, Jose M.

2012-01-01

Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP) activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC) or under direct neural control through a brain-machine interface (Brain Control, BC). In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10–45 Hz) during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to spike rate and timing may hold for models of computation and communication in distributed functional brain networks. PMID:23284276

Neuronal population coding of perceived and memorized visual features in the lateral prefrontal cortex

PubMed Central

Mendoza-Halliday, Diego; Martinez-Trujillo, Julio C.

2017-01-01

The primate lateral prefrontal cortex (LPFC) encodes visual stimulus features while they are perceived and while they are maintained in working memory. However, it remains unclear whether perceived and memorized features are encoded by the same or different neurons and population activity patterns. Here we record LPFC neuronal activity while monkeys perceive the motion direction of a stimulus that remains visually available, or memorize the direction if the stimulus disappears. We find neurons with a wide variety of combinations of coding strength for perceived and memorized directions: some neurons encode both to similar degrees while others preferentially or exclusively encode either one. Reading out the combined activity of all neurons, a machine-learning algorithm reliably decode the motion direction and determine whether it is perceived or memorized. Our results indicate that a functionally diverse population of LPFC neurons provides a substrate for discriminating between perceptual and mnemonic representations of visual features. PMID:28569756

Role of Immediate-Early Genes in Synaptic Plasticity and Neuronal Ensembles Underlying the Memory Trace

PubMed Central

Minatohara, Keiichiro; Akiyoshi, Mika; Okuno, Hiroyuki

2016-01-01

In the brain, neuronal gene expression is dynamically changed in response to neuronal activity. In particular, the expression of immediate-early genes (IEGs) such as egr-1, c-fos, and Arc is rapidly and selectively upregulated in subsets of neurons in specific brain regions associated with learning and memory formation. IEG expression has therefore been widely used as a molecular marker for neuronal populations that undergo plastic changes underlying formation of long-term memory. In recent years, optogenetic and pharmacogenetic studies of neurons expressing c-fos or Arc have revealed that, during learning, IEG-positive neurons encode and store information that is required for memory recall, suggesting that they may be involved in formation of the memory trace. However, despite accumulating evidence for the role of IEGs in synaptic plasticity, the molecular and cellular mechanisms associated with this process remain unclear. In this review, we first summarize recent literature concerning the role of IEG-expressing neuronal ensembles in organizing the memory trace. We then focus on the physiological significance of IEGs, especially Arc, in synaptic plasticity, and describe our hypotheses about the importance of Arc expression in various types of input-specific circuit reorganization. Finally, we offer perspectives on Arc function that would unveil the role of IEG-expressing neurons in the formation of memory traces in the hippocampus and other brain areas. PMID:26778955

Validation of the Air Force Weather Agency Ensemble Prediction Systems

DTIC Science & Technology

2014-03-27

by Mr. Evan L. Kuchera. Also, I would like to express my gratitude to Mr. Jeff H. Zaunter for painstakingly working with me to provided station...my fellow AFIT classmates, Capt Jeremy J. Hromsco, Capt Haley A. Homan, Capt Kyle R. Thurmond and 2Lt Coy C. Fischer for their support and...Codes. The raw METARs and SPECIs were decoded and provided for this research by Mr. Jeff Zautner, 14/WS Meteorologist, Tailored Product Analyst

Decoding visual object categories in early somatosensory cortex.

PubMed

Smith, Fraser W; Goodale, Melvyn A

2015-04-01

Neurons, even in the earliest sensory areas of cortex, are subject to a great deal of contextual influence from both within and across modality connections. In the present work, we investigated whether the earliest regions of somatosensory cortex (S1 and S2) would contain content-specific information about visual object categories. We reasoned that this might be possible due to the associations formed through experience that link different sensory aspects of a given object. Participants were presented with visual images of different object categories in 2 fMRI experiments. Multivariate pattern analysis revealed reliable decoding of familiar visual object category in bilateral S1 (i.e., postcentral gyri) and right S2. We further show that this decoding is observed for familiar but not unfamiliar visual objects in S1. In addition, whole-brain searchlight decoding analyses revealed several areas in the parietal lobe that could mediate the observed context effects between vision and somatosensation. These results demonstrate that even the first cortical stages of somatosensory processing carry information about the category of visually presented familiar objects. © The Author 2013. Published by Oxford University Press.

Decoding Visual Object Categories in Early Somatosensory Cortex

PubMed Central

Smith, Fraser W.; Goodale, Melvyn A.

2015-01-01

Neurons, even in the earliest sensory areas of cortex, are subject to a great deal of contextual influence from both within and across modality connections. In the present work, we investigated whether the earliest regions of somatosensory cortex (S1 and S2) would contain content-specific information about visual object categories. We reasoned that this might be possible due to the associations formed through experience that link different sensory aspects of a given object. Participants were presented with visual images of different object categories in 2 fMRI experiments. Multivariate pattern analysis revealed reliable decoding of familiar visual object category in bilateral S1 (i.e., postcentral gyri) and right S2. We further show that this decoding is observed for familiar but not unfamiliar visual objects in S1. In addition, whole-brain searchlight decoding analyses revealed several areas in the parietal lobe that could mediate the observed context effects between vision and somatosensation. These results demonstrate that even the first cortical stages of somatosensory processing carry information about the category of visually presented familiar objects. PMID:24122136

Elevated correlations in neuronal ensembles of mouse auditory cortex following parturition.

PubMed

Rothschild, Gideon; Cohen, Lior; Mizrahi, Adi; Nelken, Israel

2013-07-31

The auditory cortex is malleable by experience. Previous studies of auditory plasticity have described experience-dependent changes in response profiles of single neurons or changes in global tonotopic organization. However, experience-dependent changes in the dynamics of local neural populations have remained unexplored. In this study, we examined the influence of a dramatic yet natural experience in the life of female mice, giving birth and becoming a mother on single neurons and neuronal ensembles in the primary auditory cortex (A1). Using in vivo two-photon calcium imaging and electrophysiological recordings from layer 2/3 in A1 of mothers and age-matched virgin mice, we monitored changes in the responses to a set of artificial and natural sounds. Population dynamics underwent large changes as measured by pairwise and higher-order correlations, with noise correlations increasing as much as twofold in lactating mothers. Concomitantly, changes in response properties of single neurons were modest and selective. Remarkably, despite the large changes in correlations, information about stimulus identity remained essentially the same in the two groups. Our results demonstrate changes in the correlation structure of neuronal activity as a result of a natural life event.

Endogenous Sequential Cortical Activity Evoked by Visual Stimuli

PubMed Central

Miller, Jae-eun Kang; Hamm, Jordan P.; Jackson, Jesse; Yuste, Rafael

2015-01-01

Although the functional properties of individual neurons in primary visual cortex have been studied intensely, little is known about how neuronal groups could encode changing visual stimuli using temporal activity patterns. To explore this, we used in vivo two-photon calcium imaging to record the activity of neuronal populations in primary visual cortex of awake mice in the presence and absence of visual stimulation. Multidimensional analysis of the network activity allowed us to identify neuronal ensembles defined as groups of cells firing in synchrony. These synchronous groups of neurons were themselves activated in sequential temporal patterns, which repeated at much higher proportions than chance and were triggered by specific visual stimuli such as natural visual scenes. Interestingly, sequential patterns were also present in recordings of spontaneous activity without any sensory stimulation and were accompanied by precise firing sequences at the single-cell level. Moreover, intrinsic dynamics could be used to predict the occurrence of future neuronal ensembles. Our data demonstrate that visual stimuli recruit similar sequential patterns to the ones observed spontaneously, consistent with the hypothesis that already existing Hebbian cell assemblies firing in predefined temporal sequences could be the microcircuit substrate that encodes visual percepts changing in time. PMID:26063915

Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment

PubMed Central

Legenstein, Robert; Maass, Wolfgang

2014-01-01

It has recently been shown that networks of spiking neurons with noise can emulate simple forms of probabilistic inference through “neural sampling”, i.e., by treating spikes as samples from a probability distribution of network states that is encoded in the network. Deficiencies of the existing model are its reliance on single neurons for sampling from each random variable, and the resulting limitation in representing quickly varying probabilistic information. We show that both deficiencies can be overcome by moving to a biologically more realistic encoding of each salient random variable through the stochastic firing activity of an ensemble of neurons. The resulting model demonstrates that networks of spiking neurons with noise can easily track and carry out basic computational operations on rapidly varying probability distributions, such as the odds of getting rewarded for a specific behavior. We demonstrate the viability of this new approach towards neural coding and computation, which makes use of the inherent parallelism of generic neural circuits, by showing that this model can explain experimentally observed firing activity of cortical neurons for a variety of tasks that require rapid temporal integration of sensory information. PMID:25340749

A thesaurus for a neural population code

PubMed Central

Ganmor, Elad; Segev, Ronen; Schneidman, Elad

2015-01-01

Information is carried in the brain by the joint spiking patterns of large groups of noisy, unreliable neurons. This noise limits the capacity of the neural code and determines how information can be transmitted and read-out. To accurately decode, the brain must overcome this noise and identify which patterns are semantically similar. We use models of network encoding noise to learn a thesaurus for populations of neurons in the vertebrate retina responding to artificial and natural videos, measuring the similarity between population responses to visual stimuli based on the information they carry. This thesaurus reveals that the code is organized in clusters of synonymous activity patterns that are similar in meaning but may differ considerably in their structure. This organization is highly reminiscent of the design of engineered codes. We suggest that the brain may use this structure and show how it allows accurate decoding of novel stimuli from novel spiking patterns. DOI: http://dx.doi.org/10.7554/eLife.06134.001 PMID:26347983

Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task

NASA Astrophysics Data System (ADS)

Laubach, Mark; Wessberg, Johan; Nicolelis, Miguel A. L.

2000-06-01

When an animal learns to make movements in response to different stimuli, changes in activity in the motor cortex seem to accompany and underlie this learning. The precise nature of modifications in cortical motor areas during the initial stages of motor learning, however, is largely unknown. Here we address this issue by chronically recording from neuronal ensembles located in the rat motor cortex, throughout the period required for rats to learn a reaction-time task. Motor learning was demonstrated by a decrease in the variance of the rats' reaction times and an increase in the time the animals were able to wait for a trigger stimulus. These behavioural changes were correlated with a significant increase in our ability to predict the correct or incorrect outcome of single trials based on three measures of neuronal ensemble activity: average firing rate, temporal patterns of firing, and correlated firing. This increase in prediction indicates that an association between sensory cues and movement emerged in the motor cortex as the task was learned. Such modifications in cortical ensemble activity may be critical for the initial learning of motor tasks.

Decoding Speech With Integrated Hybrid Signals Recorded From the Human Ventral Motor Cortex.

PubMed

Ibayashi, Kenji; Kunii, Naoto; Matsuo, Takeshi; Ishishita, Yohei; Shimada, Seijiro; Kawai, Kensuke; Saito, Nobuhito

2018-01-01

Restoration of speech communication for locked-in patients by means of brain computer interfaces (BCIs) is currently an important area of active research. Among the neural signals obtained from intracranial recordings, single/multi-unit activity (SUA/MUA), local field potential (LFP), and electrocorticography (ECoG) are good candidates for an input signal for BCIs. However, the question of which signal or which combination of the three signal modalities is best suited for decoding speech production remains unverified. In order to record SUA, LFP, and ECoG simultaneously from a highly localized area of human ventral sensorimotor cortex (vSMC), we fabricated an electrode the size of which was 7 by 13 mm containing sparsely arranged microneedle and conventional macro contacts. We determined which signal modality is the most capable of decoding speech production, and tested if the combination of these signals could improve the decoding accuracy of spoken phonemes. Feature vectors were constructed from spike frequency obtained from SUAs and event-related spectral perturbation derived from ECoG and LFP signals, then input to the decoder. The results showed that the decoding accuracy for five spoken vowels was highest when features from multiple signals were combined and optimized for each subject, and reached 59% when averaged across all six subjects. This result suggests that multi-scale signals convey complementary information for speech articulation. The current study demonstrated that simultaneous recording of multi-scale neuronal activities could raise decoding accuracy even though the recording area is limited to a small portion of cortex, which is advantageous for future implementation of speech-assisting BCIs.

Decoding Speech With Integrated Hybrid Signals Recorded From the Human Ventral Motor Cortex

PubMed Central

Ibayashi, Kenji; Kunii, Naoto; Matsuo, Takeshi; Ishishita, Yohei; Shimada, Seijiro; Kawai, Kensuke; Saito, Nobuhito

2018-01-01

Restoration of speech communication for locked-in patients by means of brain computer interfaces (BCIs) is currently an important area of active research. Among the neural signals obtained from intracranial recordings, single/multi-unit activity (SUA/MUA), local field potential (LFP), and electrocorticography (ECoG) are good candidates for an input signal for BCIs. However, the question of which signal or which combination of the three signal modalities is best suited for decoding speech production remains unverified. In order to record SUA, LFP, and ECoG simultaneously from a highly localized area of human ventral sensorimotor cortex (vSMC), we fabricated an electrode the size of which was 7 by 13 mm containing sparsely arranged microneedle and conventional macro contacts. We determined which signal modality is the most capable of decoding speech production, and tested if the combination of these signals could improve the decoding accuracy of spoken phonemes. Feature vectors were constructed from spike frequency obtained from SUAs and event-related spectral perturbation derived from ECoG and LFP signals, then input to the decoder. The results showed that the decoding accuracy for five spoken vowels was highest when features from multiple signals were combined and optimized for each subject, and reached 59% when averaged across all six subjects. This result suggests that multi-scale signals convey complementary information for speech articulation. The current study demonstrated that simultaneous recording of multi-scale neuronal activities could raise decoding accuracy even though the recording area is limited to a small portion of cortex, which is advantageous for future implementation of speech-assisting BCIs. PMID:29674950

Sensory Afferents Use Different Coding Strategies for Heat and Cold.

PubMed

Wang, Feng; Bélanger, Erik; Côté, Sylvain L; Desrosiers, Patrick; Prescott, Steven A; Côté, Daniel C; De Koninck, Yves

2018-05-15

Primary afferents transduce environmental stimuli into electrical activity that is transmitted centrally to be decoded into corresponding sensations. However, it remains unknown how afferent populations encode different somatosensory inputs. To address this, we performed two-photon Ca 2+ imaging from thousands of dorsal root ganglion (DRG) neurons in anesthetized mice while applying mechanical and thermal stimuli to hind paws. We found that approximately half of all neurons are polymodal and that heat and cold are encoded very differently. As temperature increases, more heating-sensitive neurons are activated, and most individual neurons respond more strongly, consistent with graded coding at population and single-neuron levels, respectively. In contrast, most cooling-sensitive neurons respond in an ungraded fashion, inconsistent with graded coding and suggesting combinatorial coding, based on which neurons are co-activated. Although individual neurons may respond to multiple stimuli, our results show that different stimuli activate distinct combinations of diversely tuned neurons, enabling rich population-level coding. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

NASA Astrophysics Data System (ADS)

Wessberg, Johan; Stambaugh, Christopher R.; Kralik, Jerald D.; Beck, Pamela D.; Laubach, Mark; Chapin, John K.; Kim, Jung; Biggs, S. James; Srinivasan, Mandayam A.; Nicolelis, Miguel A. L.

2000-11-01

Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.

The search for a hippocampal engram.

PubMed

Mayford, Mark

2014-01-05

Understanding the molecular and cellular changes that underlie memory, the engram, requires the identification, isolation and manipulation of the neurons involved. This presents a major difficulty for complex forms of memory, for example hippocampus-dependent declarative memory, where the participating neurons are likely to be sparse, anatomically distributed and unique to each individual brain and learning event. In this paper, I discuss several new approaches to this problem. In vivo calcium imaging techniques provide a means of assessing the activity patterns of large numbers of neurons over long periods of time with precise anatomical identification. This provides important insight into how the brain represents complex information and how this is altered with learning. The development of techniques for the genetic modification of neural ensembles based on their natural, sensory-evoked, activity along with optogenetics allows direct tests of the coding function of these ensembles. These approaches provide a new methodological framework in which to examine the mechanisms of complex forms of learning at the level of the neurons involved in a specific memory.

The search for a hippocampal engram

PubMed Central

Mayford, Mark

2014-01-01

Understanding the molecular and cellular changes that underlie memory, the engram, requires the identification, isolation and manipulation of the neurons involved. This presents a major difficulty for complex forms of memory, for example hippocampus-dependent declarative memory, where the participating neurons are likely to be sparse, anatomically distributed and unique to each individual brain and learning event. In this paper, I discuss several new approaches to this problem. In vivo calcium imaging techniques provide a means of assessing the activity patterns of large numbers of neurons over long periods of time with precise anatomical identification. This provides important insight into how the brain represents complex information and how this is altered with learning. The development of techniques for the genetic modification of neural ensembles based on their natural, sensory-evoked, activity along with optogenetics allows direct tests of the coding function of these ensembles. These approaches provide a new methodological framework in which to examine the mechanisms of complex forms of learning at the level of the neurons involved in a specific memory. PMID:24298162

BORC/kinesin-1 ensemble drives polarized transport of lysosomes into the axon

PubMed Central

Farías, Ginny G.; Guardia, Carlos M.; De Pace, Raffaella; Britt, Dylan J.; Bonifacino, Juan S.

2017-01-01

The ability of lysosomes to move within the cytoplasm is important for many cellular functions. This ability is particularly critical in neurons, which comprise vast, highly differentiated domains such as the axon and dendrites. The mechanisms that control lysosome movement in these domains, however, remain poorly understood. Here we show that an ensemble of BORC, Arl8, SKIP, and kinesin-1, previously shown to mediate centrifugal transport of lysosomes in nonneuronal cells, specifically drives lysosome transport into the axon, and not the dendrites, in cultured rat hippocampal neurons. This transport is essential for maintenance of axonal growth-cone dynamics and autophagosome turnover. Our findings illustrate how a general mechanism for lysosome dispersal in nonneuronal cells is adapted to drive polarized transport in neurons, and emphasize the importance of this mechanism for critical axonal processes. PMID:28320970

BORC/kinesin-1 ensemble drives polarized transport of lysosomes into the axon.

PubMed

Farías, Ginny G; Guardia, Carlos M; De Pace, Raffaella; Britt, Dylan J; Bonifacino, Juan S

2017-04-04

The ability of lysosomes to move within the cytoplasm is important for many cellular functions. This ability is particularly critical in neurons, which comprise vast, highly differentiated domains such as the axon and dendrites. The mechanisms that control lysosome movement in these domains, however, remain poorly understood. Here we show that an ensemble of BORC, Arl8, SKIP, and kinesin-1, previously shown to mediate centrifugal transport of lysosomes in nonneuronal cells, specifically drives lysosome transport into the axon, and not the dendrites, in cultured rat hippocampal neurons. This transport is essential for maintenance of axonal growth-cone dynamics and autophagosome turnover. Our findings illustrate how a general mechanism for lysosome dispersal in nonneuronal cells is adapted to drive polarized transport in neurons, and emphasize the importance of this mechanism for critical axonal processes.

Population interactions between parietal and primary motor cortices during reach

PubMed Central

Rao, Naveen G.; Bondy, Adrian; Truccolo, Wilson; Donoghue, John P.

2014-01-01

Neural interactions between parietal area 2/5 and primary motor cortex (M1) were examined to determine the timing and behavioral correlates of cortico-cortical interactions. Neural activity in areas 2/5 and M1 was simultaneously recorded with 96-channel microelectrode arrays in three rhesus monkeys performing a center-out reach task. We introduce a new method to reveal parietal-motor interactions at a population level using partial spike-field coherence (PSFC) between ensembles of neurons in one area and a local field potential (LFP) in another. PSFC reflects the extent of phase locking between spike times and LFP, after removing the coherence between LFPs in the two areas. Spectral analysis of M1 LFP revealed three bands: low, medium, and high, differing in power between movement preparation and performance. We focus on PSFC in the 1–10 Hz band, in which coherence was strongest. PSFC was also present in the 10–40 Hz band during movement preparation in many channels but generally nonsignificant in the 60–200 Hz band. Ensemble PSFC revealed stronger interactions than single cell-LFP pairings. PSFC of area 2/5 ensembles with M1 LFP typically rose around movement onset and peaked ∼500 ms afterward. PSFC was typically stronger for subsets of area 2/5 neurons and M1 LFPs with similar directional bias than for those with opposite bias, indicating that area 2/5 contributes movement direction information. Together with linear prediction of M1 LFP by area 2/5 spiking, the ensemble-LFP pairing approach reveals interactions missed by single neuron-LFP pairing, demonstrating that cortico-cortical communication can be more readily observed at the ensemble level. PMID:25210154

Acoustic Processing of Temporally Modulated Sounds in Infants: Evidence from a Combined Near-Infrared Spectroscopy and EEG Study

PubMed Central

Telkemeyer, Silke; Rossi, Sonja; Nierhaus, Till; Steinbrink, Jens; Obrig, Hellmuth; Wartenburger, Isabell

2010-01-01

Speech perception requires rapid extraction of the linguistic content from the acoustic signal. The ability to efficiently process rapid changes in auditory information is important for decoding speech and thereby crucial during language acquisition. Investigating functional networks of speech perception in infancy might elucidate neuronal ensembles supporting perceptual abilities that gate language acquisition. Interhemispheric specializations for language have been demonstrated in infants. How these asymmetries are shaped by basic temporal acoustic properties is under debate. We recently provided evidence that newborns process non-linguistic sounds sharing temporal features with language in a differential and lateralized fashion. The present study used the same material while measuring brain responses of 6 and 3 month old infants using simultaneous recordings of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). NIRS reveals that the lateralization observed in newborns remains constant over the first months of life. While fast acoustic modulations elicit bilateral neuronal activations, slow modulations lead to right-lateralized responses. Additionally, auditory-evoked potentials and oscillatory EEG responses show differential responses for fast and slow modulations indicating a sensitivity for temporal acoustic variations. Oscillatory responses reveal an effect of development, that is, 6 but not 3 month old infants show stronger theta-band desynchronization for slowly modulated sounds. Whether this developmental effect is due to increasing fine-grained perception for spectrotemporal sounds in general remains speculative. Our findings support the notion that a more general specialization for acoustic properties can be considered the basis for lateralization of speech perception. The results show that concurrent assessment of vascular based imaging and electrophysiological responses have great potential in the research on language acquisition. PMID:21716574

Horizontal integration and cortical dynamics.

PubMed

Gilbert, C D

1992-07-01

We have discussed several results that lead to a view that cells in the visual system are endowed with dynamic properties, influenced by context, expectation, and long-term modifications of the cortical network. These observations will be important for understanding how neuronal ensembles produce a system that perceives, remembers, and adapts to injury. The advantage to being able to observe changes at early stages in a sensory pathway is that one may be able to understand the way in which neuronal ensembles encode and represent images at the level of their receptive field properties, of cortical topographies, and of the patterns of connections between cells participating in a network.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.

PubMed

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V; Boahen, Kwabena

2013-06-01

Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

Neuronal cell fate specification by the molecular convergence of different spatio-temporal cues on a common initiator terminal selector gene

PubMed Central

Stratmann, Johannes

2017-01-01

The extensive genetic regulatory flows underlying specification of different neuronal subtypes are not well understood at the molecular level. The Nplp1 neuropeptide neurons in the developing Drosophila nerve cord belong to two sub-classes; Tv1 and dAp neurons, generated by two distinct progenitors. Nplp1 neurons are specified by spatial cues; the Hox homeotic network and GATA factor grn, and temporal cues; the hb -> Kr -> Pdm -> cas -> grh temporal cascade. These spatio-temporal cues combine into two distinct codes; one for Tv1 and one for dAp neurons that activate a common terminal selector feedforward cascade of col -> ap/eya -> dimm -> Nplp1. Here, we molecularly decode the specification of Nplp1 neurons, and find that the cis-regulatory organization of col functions as an integratory node for the different spatio-temporal combinatorial codes. These findings may provide a logical framework for addressing spatio-temporal control of neuronal sub-type specification in other systems. PMID:28414802

A dynamic code for economic object valuation in prefrontal cortex neurons

PubMed Central

Tsutsui, Ken-Ichiro; Grabenhorst, Fabian; Kobayashi, Shunsuke; Schultz, Wolfram

2016-01-01

Neuronal reward valuations provide the physiological basis for economic behaviour. Yet, how such valuations are converted to economic decisions remains unclear. Here we show that the dorsolateral prefrontal cortex (DLPFC) implements a flexible value code based on object-specific valuations by single neurons. As monkeys perform a reward-based foraging task, individual DLPFC neurons signal the value of specific choice objects derived from recent experience. These neuronal object values satisfy principles of competitive choice mechanisms, track performance fluctuations and follow predictions of a classical behavioural model (Herrnstein’s matching law). Individual neurons dynamically encode both, the updating of object values from recently experienced rewards, and their subsequent conversion to object choices during decision-making. Decoding from unselected populations enables a read-out of motivational and decision variables not emphasized by individual neurons. These findings suggest a dynamic single-neuron and population value code in DLPFC that advances from reward experiences to economic object values and future choices. PMID:27618960

Is he playing solo or within an ensemble? How the context, visual information, and expertise may impact upon the perception of musical expressivity.

PubMed

Glowinski, Donald; Riolfo, Arianna; Shirole, Kanika; Torres-Eliard, Kim; Chiorri, Carlo; Grandjean, Didier

2014-01-01

Visual information is imperative when developing a concrete and context-sensitive understanding of how music performance is perceived. Recent studies highlight natural, automatic, and nonconscious dependence on visual cues that ultimately refer to body expressions observed in the musician. The current study investigated how the social context of a performing musician (eg playing alone or within an ensemble) and the musical expertise of the perceivers influence the strategies used to understand and decode the visual features of music performance. Results revealed that both perceiver groups, nonmusicians and musicians, have a higher sensitivity towards gaze information; therefore, an impoverished stimulus such as a point-light display is insufficient to understand the social context in which the musician is performing. Implications for these findings are discussed.

The role of ECoG magnitude and phase in decoding position, velocity, and acceleration during continuous motor behavior

PubMed Central

Hammer, Jiri; Fischer, Jörg; Ruescher, Johanna; Schulze-Bonhage, Andreas; Aertsen, Ad; Ball, Tonio

2013-01-01

In neuronal population signals, including the electroencephalogram (EEG) and electrocorticogram (ECoG), the low-frequency component (LFC) is particularly informative about motor behavior and can be used for decoding movement parameters for brain-machine interface (BMI) applications. An idea previously expressed, but as of yet not quantitatively tested, is that it is the LFC phase that is the main source of decodable information. To test this issue, we analyzed human ECoG recorded during a game-like, one-dimensional, continuous motor task with a novel decoding method suitable for unfolding magnitude and phase explicitly into a complex-valued, time-frequency signal representation, enabling quantification of the decodable information within the temporal, spatial and frequency domains and allowing disambiguation of the phase contribution from that of the spectral magnitude. The decoding accuracy based only on phase information was substantially (at least 2 fold) and significantly higher than that based only on magnitudes for position, velocity and acceleration. The frequency profile of movement-related information in the ECoG data matched well with the frequency profile expected when assuming a close time-domain correlate of movement velocity in the ECoG, e.g., a (noisy) “copy” of hand velocity. No such match was observed with the frequency profiles expected when assuming a copy of either hand position or acceleration. There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range. Thus, our study contributes to elucidating the nature of the informative LFC of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance. PMID:24198757

The spectrotemporal filter mechanism of auditory selective attention

PubMed Central

Lakatos, Peter; Musacchia, Gabriella; O’Connell, Monica N.; Falchier, Arnaud Y.; Javitt, Daniel C.; Schroeder, Charles E.

2013-01-01

SUMMARY While we have convincing evidence that attention to auditory stimuli modulates neuronal responses at or before the level of primary auditory cortex (A1), the underlying physiological mechanisms are unknown. We found that attending to rhythmic auditory streams resulted in the entrainment of ongoing oscillatory activity reflecting rhythmic excitability fluctuations in A1. Strikingly, while the rhythm of the entrained oscillations in A1 neuronal ensembles reflected the temporal structure of the attended stream, the phase depended on the attended frequency content. Counter-phase entrainment across differently tuned A1 regions resulted in both the amplification and sharpening of responses at attended time points, in essence acting as a spectrotemporal filter mechanism. Our data suggest that selective attention generates a dynamically evolving model of attended auditory stimulus streams in the form of modulatory subthreshold oscillations across tonotopically organized neuronal ensembles in A1 that enhances the representation of attended stimuli. PMID:23439126

Task-phase-specific dynamics of basal forebrain neuronal ensembles

PubMed Central

Tingley, David; Alexander, Andrew S.; Kolbu, Sean; de Sa, Virginia R.; Chiba, Andrea A.; Nitz, Douglas A.

2014-01-01

Cortically projecting basal forebrain neurons play a critical role in learning and attention, and their degeneration accompanies age-related impairments in cognition. Despite the impressive anatomical and cell-type complexity of this system, currently available data suggest that basal forebrain neurons lack complexity in their response fields, with activity primarily reflecting only macro-level brain states such as sleep and wake, onset of relevant stimuli and/or reward obtainment. The current study examined the spiking activity of basal forebrain neuron populations across multiple phases of a selective attention task, addressing, in particular, the issue of complexity in ensemble firing patterns across time. Clustering techniques applied to the full population revealed a large number of distinct categories of task-phase-specific activity patterns. Unique population firing-rate vectors defined each task phase and most categories of task-phase-specific firing had counterparts with opposing firing patterns. An analogous set of task-phase-specific firing patterns was also observed in a population of posterior parietal cortex neurons. Thus, consistent with the known anatomical complexity, basal forebrain population dynamics are capable of differentially modulating their cortical targets according to the unique sets of environmental stimuli, motor requirements, and cognitive processes associated with different task phases. PMID:25309352

Ensemble stacking mitigates biases in inference of synaptic connectivity.

PubMed

Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

2018-01-01

A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

Population decoding of motor cortical activity using a generalized linear model with hidden states.

PubMed

Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas; Paninski, Liam

2010-06-15

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (reducing the mean square error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Population Decoding of Motor Cortical Activity using a Generalized Linear Model with Hidden States

PubMed Central

Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas G.; Paninski, Liam

2010-01-01

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (lowering the Mean Square Error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. PMID:20359500

Role of Dorsomedial Striatum Neuronal Ensembles in Incubation of Methamphetamine Craving after Voluntary Abstinence

PubMed Central

Venniro, Marco; Zhang, Michelle; Bossert, Jennifer M.; Warren, Brandon L.; Hope, Bruce T.

2017-01-01

We recently developed a rat model of incubation of methamphetamine craving after choice-based voluntary abstinence. Here, we studied the role of dorsolateral striatum (DLS) and dorsomedial striatum (DMS) in this incubation. We trained rats to self-administer palatable food pellets (6 d, 6 h/d) and methamphetamine (12 d, 6 h/d). We then assessed relapse to methamphetamine seeking under extinction conditions after 1 and 21 abstinence days. Between tests, the rats underwent voluntary abstinence (using a discrete choice procedure between methamphetamine and food; 20 trials/d) for 19 d. We used in situ hybridization to measure the colabeling of the activity marker Fos with Drd1 and Drd2 in DMS and DLS after the tests. Based on the in situ hybridization colabeling results, we tested the causal role of DMS D1 and D2 family receptors, and DMS neuronal ensembles in “incubated” methamphetamine seeking, using selective dopamine receptor antagonists (SCH39166 or raclopride) and the Daun02 chemogenetic inactivation procedure, respectively. Methamphetamine seeking was higher after 21 d of voluntary abstinence than after 1 d (incubation of methamphetamine craving). The incubated response was associated with increased Fos expression in DMS but not in DLS; Fos was colabeled with both Drd1 and Drd2. DMS injections of SCH39166 or raclopride selectively decreased methamphetamine seeking after 21 abstinence days. In Fos-lacZ transgenic rats, selective inactivation of relapse test-activated Fos neurons in DMS on abstinence day 18 decreased incubated methamphetamine seeking on day 21. Results demonstrate a role of DMS dopamine D1 and D2 receptors in the incubation of methamphetamine craving after voluntary abstinence and that DMS neuronal ensembles mediate this incubation. SIGNIFICANCE STATEMENT In human addicts, abstinence is often self-imposed and relapse can be triggered by exposure to drug-associated cues that induce drug craving. We recently developed a rat model of incubation of methamphetamine craving after choice-based voluntary abstinence. Here, we used classical pharmacology, in situ hybridization, immunohistochemistry, and the Daun02 inactivation procedure to demonstrate a critical role of dorsomedial striatum neuronal ensembles in this new form of incubation of drug craving. PMID:28123032

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding

PubMed Central

Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-01-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information. PMID:27304526

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding.

PubMed

Huang, Chao; Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-06-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

NASA Astrophysics Data System (ADS)

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

2013-06-01

Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

Brain Oscillations Forever--Neurophysiology in Future Research of Child Psychiatric Problems

ERIC Educational Resources Information Center

Rothenberger, Aribert

2009-01-01

For decades neurophysiology has successfully contributed to research and clinical care in child psychiatry. Recently, methodological progress has led to a revival of interest in brain oscillations (i.e., a band of periodic neuronal frequencies with a wave-duration from milliseconds to several seconds which may code and decode information). These…

The Effects of Theta Precession on Spatial Learning and Simplicial Complex Dynamics in a Topological Model of the Hippocampal Spatial Map

PubMed Central

Arai, Mamiko; Brandt, Vicky; Dabaghian, Yuri

2014-01-01

Learning arises through the activity of large ensembles of cells, yet most of the data neuroscientists accumulate is at the level of individual neurons; we need models that can bridge this gap. We have taken spatial learning as our starting point, computationally modeling the activity of place cells using methods derived from algebraic topology, especially persistent homology. We previously showed that ensembles of hundreds of place cells could accurately encode topological information about different environments (“learn” the space) within certain values of place cell firing rate, place field size, and cell population; we called this parameter space the learning region. Here we advance the model both technically and conceptually. To make the model more physiological, we explored the effects of theta precession on spatial learning in our virtual ensembles. Theta precession, which is believed to influence learning and memory, did in fact enhance learning in our model, increasing both speed and the size of the learning region. Interestingly, theta precession also increased the number of spurious loops during simplicial complex formation. We next explored how downstream readout neurons might define co-firing by grouping together cells within different windows of time and thereby capturing different degrees of temporal overlap between spike trains. Our model's optimum coactivity window correlates well with experimental data, ranging from ∼150–200 msec. We further studied the relationship between learning time, window width, and theta precession. Our results validate our topological model for spatial learning and open new avenues for connecting data at the level of individual neurons to behavioral outcomes at the neuronal ensemble level. Finally, we analyzed the dynamics of simplicial complex formation and loop transience to propose that the simplicial complex provides a useful working description of the spatial learning process. PMID:24945927

MK-801 Impairs Cognitive Coordination on a Rotating Arena (Carousel) and Contextual Specificity of Hippocampal Immediate-Early Gene Expression in a Rat Model of Psychosis

PubMed Central

Kubík, Štěpán; Buchtová, Helena; Valeš, Karel; Stuchlík, Aleš

2014-01-01

Flexible behavior in dynamic, real-world environments requires more than static spatial learning and memory. Discordant and unstable cues must be organized in coherent subsets to give rise to meaningful spatial representations. We model this form of cognitive coordination on a rotating arena – Carousel where arena- and room-bound spatial cues are dissociated. Hippocampal neuronal ensemble activity can repeatedly switch between multiple representations of such an environment. Injection of tetrodotoxin into one hippocampus prevents cognitive coordination during avoidance of a stationary room-defined place on the Carousel and increases coactivity of previously unrelated neurons in the uninjected hippocampus. Place avoidance on the Carousel is impaired after systemic administration of non-competitive NMDAr blockers (MK-801) used to model schizophrenia in animals and people. We tested if this effect is due to cognitive disorganization or other effect of NMDAr antagonism such as hyperlocomotion, spatial memory impairment, or general learning deficit. We also examined if the same dose of MK-801 alters patterns of immediate-early gene (IEG) expression in the hippocampus. IEG expression is triggered in neuronal nuclei in a context-specific manner after behavioral exploration and it is used to map activity in neuronal populations. IEG expression is critical for maintenance of synaptic plasticity and memory consolidation. We show that the same dose of MK-801 that impairs spatial coordination of rats on the Carousel also eliminates contextual specificity of IEG expression in hippocampal CA1 ensembles. This effect is due to increased similarity between ensembles activated in different environments, consistent with the idea that it is caused by increased coactivity between neurons, which did not previously fire together. Our data support the proposition of the Hypersynchrony theory that cognitive disorganization in psychosis is due to increased coactivity between unrelated neurons. PMID:24659959

Focal versus distributed temporal cortex activity for speech sound category assignment

PubMed Central

Bouton, Sophie; Chambon, Valérian; Tyrand, Rémi; Seeck, Margitta; Karkar, Sami; van de Ville, Dimitri; Giraud, Anne-Lise

2018-01-01

Percepts and words can be decoded from distributed neural activity measures. However, the existence of widespread representations might conflict with the more classical notions of hierarchical processing and efficient coding, which are especially relevant in speech processing. Using fMRI and magnetoencephalography during syllable identification, we show that sensory and decisional activity colocalize to a restricted part of the posterior superior temporal gyrus (pSTG). Next, using intracortical recordings, we demonstrate that early and focal neural activity in this region distinguishes correct from incorrect decisions and can be machine-decoded to classify syllables. Crucially, significant machine decoding was possible from neuronal activity sampled across different regions of the temporal and frontal lobes, despite weak or absent sensory or decision-related responses. These findings show that speech-sound categorization relies on an efficient readout of focal pSTG neural activity, while more distributed activity patterns, although classifiable by machine learning, instead reflect collateral processes of sensory perception and decision. PMID:29363598

Population coding in sparsely connected networks of noisy neurons.

PubMed

Tripp, Bryan P; Orchard, Jeff

2012-01-01

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons.

Taste quality decoding parallels taste sensations.

PubMed

Crouzet, Sébastien M; Busch, Niko A; Ohla, Kathrin

2015-03-30

In most species, the sense of taste is key in the distinction of potentially nutritious and harmful food constituents and thereby in the acceptance (or rejection) of food. Taste quality is encoded by specialized receptors on the tongue, which detect chemicals corresponding to each of the basic tastes (sweet, salty, sour, bitter, and savory [1]), before taste quality information is transmitted via segregated neuronal fibers [2], distributed coding across neuronal fibers [3], or dynamic firing patterns [4] to the gustatory cortex in the insula. In rodents, both hardwired coding by labeled lines [2] and flexible, learning-dependent representations [5] and broadly tuned neurons [6] seem to coexist. It is currently unknown how, when, and where taste quality representations are established in the cortex and whether these representations are used for perceptual decisions. Here, we show that neuronal response patterns allow to decode which of four tastants (salty, sweet, sour, and bitter) participants tasted in a given trial by using time-resolved multivariate pattern analyses of large-scale electrophysiological brain responses. The onset of this prediction coincided with the earliest taste-evoked responses originating from the insula and opercular cortices, indicating that quality is among the first attributes of a taste represented in the central gustatory system. These response patterns correlated with perceptual decisions of taste quality: tastes that participants discriminated less accurately also evoked less discriminated brain response patterns. The results therefore provide the first evidence for a link between taste-related decision-making and the predictive value of these brain response patterns. Copyright © 2015 Elsevier Ltd. All rights reserved.

Limb-state information encoded by peripheral and central somatosensory neurons: Implications for an afferent interface

PubMed Central

Weber, Douglas J.; London, Brian M.; Hokanson, James A.; Ayers, Christopher A.; Gaunt, Robert A.; Torres, Ricardo R.; Zaaimi, Boubker; Miller, Lee E.

2013-01-01

A major issue to be addressed in the development of neural interfaces for prosthetic control is the need for somatosensory feedback. Here, we investigate two possible strategies: electrical stimulation of either dorsal root ganglia (DRG) or primary somatosensory cortex (S1). In each approach, we must determine a model that reflects the representation of limb state in terms of neural discharge. This model can then be used to design stimuli that artificially activate the nervous system to convey information about limb state to the subject. Electrically activating DRG neurons using naturalistic stimulus patterns, modeled on recordings made during passive limb movement, evoked activity in S1 that was similar to that of the original movement. We also found that S1 neural populations could accurately discriminate different patterns of DRG stimulation across a wide range of stimulus pulse-rates. In studying the neural coding of limb-state in S1, we also decoded the kinematics of active limb movement using multi-electrode recordings in the monkey. Neurons having both proprioceptive and cutaneous receptive fields contributed equally to this decoding. Some neurons were most informative of limb state in the recent past, but many others appeared to signal upcoming movements suggesting that they also were modulated by an efference copy signal. Finally, we show that a monkey was able to detect stimulation through a large percentage of electrodes implanted in area 2. We discuss the design of appropriate stimulus paradigms for conveying time-varying limb state information, and the relative merits and limitations of central and peripheral approaches. PMID:21878419

Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble

PubMed Central

Jolivet, Renaud; Coggan, Jay S.; Allaman, Igor; Magistretti, Pierre J.

2015-01-01

Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain’s metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging. PMID:25719367

Multi-timescale modeling of activity-dependent metabolic coupling in the neuron-glia-vasculature ensemble.

PubMed

Jolivet, Renaud; Coggan, Jay S; Allaman, Igor; Magistretti, Pierre J

2015-02-01

Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain's metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging.

Metastable neural dynamics mediates expectation

NASA Astrophysics Data System (ADS)

Mazzucato, Luca; La Camera, Giancarlo; Fontanini, Alfredo

Sensory stimuli are processed faster when their presentation is expected compared to when they come as a surprise. We previously showed that, in multiple single-unit recordings from alert rat gustatory cortex, taste stimuli can be decoded faster from neural activity if preceded by a stimulus-predicting cue. However, the specific computational process mediating this anticipatory neural activity is unknown. Here, we propose a biologically plausible model based on a recurrent network of spiking neurons with clustered architecture. In the absence of stimulation, the model neural activity unfolds through sequences of metastable states, each state being a population vector of firing rates. We modeled taste stimuli and cue (the same for all stimuli) as two inputs targeting subsets of excitatory neurons. As observed in experiment, stimuli evoked specific state sequences, characterized in terms of `coding states', i.e., states occurring significantly more often for a particular stimulus. When stimulus presentation is preceded by a cue, coding states show a faster and more reliable onset, and expected stimuli can be decoded more quickly than unexpected ones. This anticipatory effect is unrelated to changes of firing rates in stimulus-selective neurons and is absent in homogeneous balanced networks, suggesting that a clustered organization is necessary to mediate the expectation of relevant events. Our results demonstrate a novel mechanism for speeding up sensory coding in cortical circuits. NIDCD K25-DC013557 (LM); NIDCD R01-DC010389 (AF); NSF IIS-1161852 (GL).

On brain activity mapping: insights and lessons from Brain Decoding Project to map memory patterns in the hippocampus.

PubMed

Tsien, Joe Z; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longnian; Wang, Phillip Lei; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

2013-09-01

The BRAIN project recently announced by the president Obama is the reflection of unrelenting human quest for cracking the brain code, the patterns of neuronal activity that define who we are and what we are. While the Brain Activity Mapping proposal has rightly emphasized on the need to develop new technologies for measuring every spike from every neuron, it might be helpful to consider both the theoretical and experimental aspects that would accelerate our search for the organizing principles of the brain code. Here we share several insights and lessons from the similar proposal, namely, Brain Decoding Project that we initiated since 2007. We provide a specific example in our initial mapping of real-time memory traces from one part of the memory circuit, namely, the CA1 region of the mouse hippocampus. We show how innovative behavioral tasks and appropriate mathematical analyses of large datasets can play equally, if not more, important roles in uncovering the specific-to-general feature-coding cell assembly mechanism by which episodic memory, semantic knowledge, and imagination are generated and organized. Our own experiences suggest that the bottleneck of the Brain Project is not only at merely developing additional new technologies, but also the lack of efficient avenues to disseminate cutting edge platforms and decoding expertise to neuroscience community. Therefore, we propose that in order to harness unique insights and extensive knowledge from various investigators working in diverse neuroscience subfields, ranging from perception and emotion to memory and social behaviors, the BRAIN project should create a set of International and National Brain Decoding Centers at which cutting-edge recording technologies and expertise on analyzing large datasets analyses can be made readily available to the entire community of neuroscientists who can apply and schedule to perform cutting-edge research.

Ensemble Recordings in Awake Rats: Achieving Behavioral Regularity during Multimodal Stimulus Processing and Discriminative Learning

ERIC Educational Resources Information Center

Lee, Eunjeong; Oliveira-Ferreira, Ana I.; de Water, Ed; Gerritsen, Hans; Bakker, Mattijs C.; Kalwij, Jan A. W.; van Goudoever, Tjerk; Buster, Wietze H.; Pennartz, Cyriel M. A.

2009-01-01

To meet an increasing need to examine the neurophysiological underpinnings of behavior in rats, we developed a behavioral system for studying sensory processing, attention and discrimination learning in rats while recording firing patterns of neurons in one or more brain areas of interest. Because neuronal activity is sensitive to variations in…

Neural Plasticity: Single Neuron Models for Discrimination and Generalization and an Experimental Ensemble Approach.

DTIC Science & Technology

1983-06-01

Commuents Regarding the Antagonistic Mechanisms Approach .0...... .................................... 67 C. Cognitive Applications...similarities between stimuli, and differentiation* a separation process. An analogous dichotomy in cognitive theory has been extensively studied by Tversky...tasks including perception. cognition , and action. Not all neurons are identical, there exist several anatomically defined categories of these cells

Methods for Assessment of Memory Reactivation.

PubMed

Liu, Shizhao; Grosmark, Andres D; Chen, Zhe

2018-04-13

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

Desynchronizing electrical and sensory coordinated reset neuromodulation

PubMed Central

Popovych, Oleksandr V.; Tass, Peter A.

2012-01-01

Coordinated reset (CR) stimulation is a desynchronizing stimulation technique based on timely coordinated phase resets of sub-populations of a synchronized neuronal ensemble. It has initially been computationally developed for electrical deep brain stimulation (DBS), to enable an effective desynchronization and unlearning of pathological synchrony and connectivity (anti-kindling). Here we computationally show for ensembles of spiking and bursting model neurons interacting via excitatory and inhibitory adaptive synapses that a phase reset of neuronal populations as well as a desynchronization and an anti-kindling can robustly be achieved by direct electrical stimulation or indirect (synaptically-mediated) excitatory and inhibitory stimulation. Our findings are relevant for DBS as well as for sensory stimulation in neurological disorders characterized by pathological neuronal synchrony. Based on the obtained results, we may expect that the local effects in the vicinity of a depth electrode (realized by direct stimulation of the neurons' somata or stimulation of axon terminals) and the non-local CR effects (realized by stimulation of excitatory or inhibitory efferent fibers) of deep brain CR neuromodulation may be similar or even identical. Furthermore, our results indicate that an effective desynchronization and anti-kindling can even be achieved by non-invasive, sensory CR neuromodulation. We discuss the concept of sensory CR neuromodulation in the context of neurological disorders. PMID:22454622

Desynchronizing electrical and sensory coordinated reset neuromodulation.

PubMed

Popovych, Oleksandr V; Tass, Peter A

2012-01-01

Coordinated reset (CR) stimulation is a desynchronizing stimulation technique based on timely coordinated phase resets of sub-populations of a synchronized neuronal ensemble. It has initially been computationally developed for electrical deep brain stimulation (DBS), to enable an effective desynchronization and unlearning of pathological synchrony and connectivity (anti-kindling). Here we computationally show for ensembles of spiking and bursting model neurons interacting via excitatory and inhibitory adaptive synapses that a phase reset of neuronal populations as well as a desynchronization and an anti-kindling can robustly be achieved by direct electrical stimulation or indirect (synaptically-mediated) excitatory and inhibitory stimulation. Our findings are relevant for DBS as well as for sensory stimulation in neurological disorders characterized by pathological neuronal synchrony. Based on the obtained results, we may expect that the local effects in the vicinity of a depth electrode (realized by direct stimulation of the neurons' somata or stimulation of axon terminals) and the non-local CR effects (realized by stimulation of excitatory or inhibitory efferent fibers) of deep brain CR neuromodulation may be similar or even identical. Furthermore, our results indicate that an effective desynchronization and anti-kindling can even be achieved by non-invasive, sensory CR neuromodulation. We discuss the concept of sensory CR neuromodulation in the context of neurological disorders.

Spatial scale and distribution of neurovascular signals underlying decoding of orientation and eye of origin from fMRI data

PubMed Central

Harrison, Charlotte; Jackson, Jade; Oh, Seung-Mock; Zeringyte, Vaida

2016-01-01

Multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data is widely used, yet the spatial scales and origin of neurovascular signals underlying such analyses remain unclear. We compared decoding performance for stimulus orientation and eye of origin from fMRI measurements in human visual cortex with predictions based on the columnar organization of each feature and estimated the spatial scales of patterns driving decoding. Both orientation and eye of origin could be decoded significantly above chance in early visual areas (V1–V3). Contrary to predictions based on a columnar origin of response biases, decoding performance for eye of origin in V2 and V3 was not significantly lower than that in V1, nor did decoding performance for orientation and eye of origin differ significantly. Instead, response biases for both features showed large-scale organization, evident as a radial bias for orientation, and a nasotemporal bias for eye preference. To determine whether these patterns could drive classification, we quantified the effect on classification performance of binning voxels according to visual field position. Consistent with large-scale biases driving classification, binning by polar angle yielded significantly better decoding performance for orientation than random binning in V1–V3. Similarly, binning by hemifield significantly improved decoding performance for eye of origin. Patterns of orientation and eye preference bias in V2 and V3 showed a substantial degree of spatial correlation with the corresponding patterns in V1, suggesting that response biases in these areas originate in V1. Together, these findings indicate that multivariate classification results need not reflect the underlying columnar organization of neuronal response selectivities in early visual areas. NEW & NOTEWORTHY Large-scale response biases can account for decoding of orientation and eye of origin in human early visual areas V1–V3. For eye of origin this pattern is a nasotemporal bias; for orientation it is a radial bias. Differences in decoding performance across areas and stimulus features are not well predicted by differences in columnar-scale organization of each feature. Large-scale biases in extrastriate areas are spatially correlated with those in V1, suggesting biases originate in primary visual cortex. PMID:27903637

Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains.

PubMed

Pillow, Jonathan W; Ahmadian, Yashar; Paninski, Liam

2011-01-01

One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.

Temporal Processing in the Visual Cortex of the Awake and Anesthetized Rat.

PubMed

Aasebø, Ida E J; Lepperød, Mikkel E; Stavrinou, Maria; Nøkkevangen, Sandra; Einevoll, Gaute; Hafting, Torkel; Fyhn, Marianne

2017-01-01

The activity pattern and temporal dynamics within and between neuron ensembles are essential features of information processing and believed to be profoundly affected by anesthesia. Much of our general understanding of sensory information processing, including computational models aimed at mathematically simulating sensory information processing, rely on parameters derived from recordings conducted on animals under anesthesia. Due to the high variety of neuronal subtypes in the brain, population-based estimates of the impact of anesthesia may conceal unit- or ensemble-specific effects of the transition between states. Using chronically implanted tetrodes into primary visual cortex (V1) of rats, we conducted extracellular recordings of single units and followed the same cell ensembles in the awake and anesthetized states. We found that the transition from wakefulness to anesthesia involves unpredictable changes in temporal response characteristics. The latency of single-unit responses to visual stimulation was delayed in anesthesia, with large individual variations between units. Pair-wise correlations between units increased under anesthesia, indicating more synchronized activity. Further, the units within an ensemble show reproducible temporal activity patterns in response to visual stimuli that is changed between states, suggesting state-dependent sequences of activity. The current dataset, with recordings from the same neural ensembles across states, is well suited for validating and testing computational network models. This can lead to testable predictions, bring a deeper understanding of the experimental findings and improve models of neural information processing. Here, we exemplify such a workflow using a Brunel network model.

Temporal Processing in the Visual Cortex of the Awake and Anesthetized Rat

PubMed Central

Aasebø, Ida E. J.; Stavrinou, Maria; Nøkkevangen, Sandra; Einevoll, Gaute

2017-01-01

Abstract The activity pattern and temporal dynamics within and between neuron ensembles are essential features of information processing and believed to be profoundly affected by anesthesia. Much of our general understanding of sensory information processing, including computational models aimed at mathematically simulating sensory information processing, rely on parameters derived from recordings conducted on animals under anesthesia. Due to the high variety of neuronal subtypes in the brain, population-based estimates of the impact of anesthesia may conceal unit- or ensemble-specific effects of the transition between states. Using chronically implanted tetrodes into primary visual cortex (V1) of rats, we conducted extracellular recordings of single units and followed the same cell ensembles in the awake and anesthetized states. We found that the transition from wakefulness to anesthesia involves unpredictable changes in temporal response characteristics. The latency of single-unit responses to visual stimulation was delayed in anesthesia, with large individual variations between units. Pair-wise correlations between units increased under anesthesia, indicating more synchronized activity. Further, the units within an ensemble show reproducible temporal activity patterns in response to visual stimuli that is changed between states, suggesting state-dependent sequences of activity. The current dataset, with recordings from the same neural ensembles across states, is well suited for validating and testing computational network models. This can lead to testable predictions, bring a deeper understanding of the experimental findings and improve models of neural information processing. Here, we exemplify such a workflow using a Brunel network model. PMID:28791331

Parvalbumin interneurons constrain the size of the lateral amygdala engram.

PubMed

Morrison, Dano J; Rashid, Asim J; Yiu, Adelaide P; Yan, Chen; Frankland, Paul W; Josselyn, Sheena A

2016-11-01

Memories are thought to be represented by discrete physiological changes in the brain, collectively referred to as an engram, that allow patterns of activity present during learning to be reactivated in the future. During the formation of a conditioned fear memory, a subset of principal (excitatory) neurons in the lateral amygdala (LA) are allocated to a neuronal ensemble that encodes an association between an initially neutral stimulus and a threatening aversive stimulus. Previous experimental and computational work suggests that this subset consists of only a small proportion of all LA neurons, and that this proportion remains constant across different memories. Here we examine the mechanisms that contribute to the stability of the size of the LA component of an engram supporting a fear memory. Visualizing expression of the activity-dependent gene Arc following memory retrieval to identify neurons allocated to an engram, we first show that the overall size of the LA engram remains constant across conditions of different memory strength. That is, the strength of a memory was not correlated with the number of LA neurons allocated to the engram supporting that memory. We then examine potential mechanisms constraining the size of the LA engram by expressing inhibitory DREADDS (designer receptors exclusively activated by designer drugs) in parvalbumin-positive (PV + ) interneurons of the amygdala. We find that silencing PV + neurons during conditioning increases the size of the engram, especially in the dorsal subnucleus of the LA. These results confirm predictions from modeling studies regarding the role of inhibition in shaping the size of neuronal memory ensembles and provide additional support for the idea that neurons in the LA are sparsely allocated to the engram based on relative neuronal excitability. Copyright © 2016 Elsevier Inc. All rights reserved.

Sensory Cortical Population Dynamics Uniquely Track Behavior across Learning and Extinction

PubMed Central

Katz, Donald B.

2014-01-01

Neural responses in many cortical regions encode information relevant to behavior: information that necessarily changes as that behavior changes with learning. Although such responses are reasonably theorized to be related to behavior causation, the true nature of that relationship cannot be clarified by simple learning studies, which show primarily that responses change with experience. Neural activity that truly tracks behavior (as opposed to simply changing with experience) will not only change with learning but also change back when that learning is extinguished. Here, we directly probed for this pattern, recording the activity of ensembles of gustatory cortical single neurons as rats that normally consumed sucrose avidly were trained first to reject it (i.e., conditioned taste aversion learning) and then to enjoy it again (i.e., extinction), all within 49 h. Both learning and extinction altered cortical responses, consistent with the suggestion (based on indirect evidence) that extinction is a novel form of learning. But despite the fact that, as expected, postextinction single-neuron responses did not resemble “naive responses,” ensemble response dynamics changed with learning and reverted with extinction: both the speed of stimulus processing and the relationships among ensemble responses to the different stimuli tracked behavioral relevance. These data suggest that population coding is linked to behavior with a fidelity that single-neuron coding is not. PMID:24453316

Ensemble codes involving hippocampal neurons are at risk during delayed performance tests.

PubMed

Hampson, R E; Deadwyler, S A

1996-11-26

Multielectrode recording techniques were used to record ensemble activity from 10 to 16 simultaneously active CA1 and CA3 neurons in the rat hippocampus during performance of a spatial delayed-nonmatch-to-sample task. Extracted sources of variance were used to assess the nature of two different types of errors that accounted for 30% of total trials. The two types of errors included ensemble "miscodes" of sample phase information and errors associated with delay-dependent corruption or disappearance of sample information at the time of the nonmatch response. Statistical assessment of trial sequences and associated "strength" of hippocampal ensemble codes revealed that miscoded error trials always followed delay-dependent error trials in which encoding was "weak," indicating that the two types of errors were "linked." It was determined that the occurrence of weakly encoded, delay-dependent error trials initiated an ensemble encoding "strategy" that increased the chances of being correct on the next trial and avoided the occurrence of further delay-dependent errors. Unexpectedly, the strategy involved "strongly" encoding response position information from the prior (delay-dependent) error trial and carrying it forward to the sample phase of the next trial. This produced a miscode type error on trials in which the "carried over" information obliterated encoding of the sample phase response on the next trial. Application of this strategy, irrespective of outcome, was sufficient to reorient the animal to the proper between trial sequence of response contingencies (nonmatch-to-sample) and boost performance to 73% correct on subsequent trials. The capacity for ensemble analyses of strength of information encoding combined with statistical assessment of trial sequences therefore provided unique insight into the "dynamic" nature of the role hippocampus plays in delay type memory tasks.

Time evolution of coherent structures in networks of Hindmarch Rose neurons

NASA Astrophysics Data System (ADS)

Mainieri, M. S.; Erichsen, R.; Brunnet, L. G.

2005-08-01

In the regime of partial synchronization, networks of diffusively coupled Hindmarch-Rose neurons show coherent structures developing in a region of the phase space which is wider than in the correspondent single neuron. Such structures are kept, without important changes, during several bursting periods. In this work, we study the time evolution of these structures and their dynamical stability under damage. This system may model the behavior of ensembles of neurons coupled through a bidirectional gap junction or, in a broader sense, it could also account for the molecular cascades present in the formation of flash and short time memory.

An Information Transmission Measure for the Analysis of Effective Connectivity among Cortical Neurons

PubMed Central

Law, Andrew J.; Sharma, Gaurav; Schieber, Marc H.

2014-01-01

We present a methodology for detecting effective connections between simultaneously recorded neurons using an information transmission measure to identify the presence and direction of information flow from one neuron to another. Using simulated and experimentally-measured data, we evaluate the performance of our proposed method and compare it to the traditional transfer entropy approach. In simulations, our measure of information transmission outperforms transfer entropy in identifying the effective connectivity structure of a neuron ensemble. For experimentally recorded data, where ground truth is unavailable, the proposed method also yields a more plausible connectivity structure than transfer entropy. PMID:21096617

Robustness of neuroprosthetic decoding algorithms.

PubMed

Serruya, Mijail; Hatsopoulos, Nicholas; Fellows, Matthew; Paninski, Liam; Donoghue, John

2003-03-01

We assessed the ability of two algorithms to predict hand kinematics from neural activity as a function of the amount of data used to determine the algorithm parameters. Using chronically implanted intracortical arrays, single- and multineuron discharge was recorded during trained step tracking and slow continuous tracking tasks in macaque monkeys. The effect of increasing the amount of data used to build a neural decoding model on the ability of that model to predict hand kinematics accurately was examined. We evaluated how well a maximum-likelihood model classified discrete reaching directions and how well a linear filter model reconstructed continuous hand positions over time within and across days. For each of these two models we asked two questions: (1) How does classification performance change as the amount of data the model is built upon increases? (2) How does varying the time interval between the data used to build the model and the data used to test the model affect reconstruction? Less than 1 min of data for the discrete task (8 to 13 neurons) and less than 3 min (8 to 18 neurons) for the continuous task were required to build optimal models. Optimal performance was defined by a cost function we derived that reflects both the ability of the model to predict kinematics accurately and the cost of taking more time to build such models. For both the maximum-likelihood classifier and the linear filter model, increasing the duration between the time of building and testing the model within a day did not cause any significant trend of degradation or improvement in performance. Linear filters built on one day and tested on neural data on a subsequent day generated error-measure distributions that were not significantly different from those generated when the linear filters were tested on neural data from the initial day (p<0.05, Kolmogorov-Smirnov test). These data show that only a small amount of data from a limited number of cortical neurons appears to be necessary to construct robust models to predict kinematic parameters for the subsequent hours. Motor-control signals derived from neurons in motor cortex can be reliably acquired for use in neural prosthetic devices. Adequate decoding models can be built rapidly from small numbers of cells and maintained with daily calibration sessions.

Role of Dorsomedial Striatum Neuronal Ensembles in Incubation of Methamphetamine Craving after Voluntary Abstinence.

PubMed

Caprioli, Daniele; Venniro, Marco; Zhang, Michelle; Bossert, Jennifer M; Warren, Brandon L; Hope, Bruce T; Shaham, Yavin

2017-01-25

We recently developed a rat model of incubation of methamphetamine craving after choice-based voluntary abstinence. Here, we studied the role of dorsolateral striatum (DLS) and dorsomedial striatum (DMS) in this incubation. We trained rats to self-administer palatable food pellets (6 d, 6 h/d) and methamphetamine (12 d, 6 h/d). We then assessed relapse to methamphetamine seeking under extinction conditions after 1 and 21 abstinence days. Between tests, the rats underwent voluntary abstinence (using a discrete choice procedure between methamphetamine and food; 20 trials/d) for 19 d. We used in situ hybridization to measure the colabeling of the activity marker Fos with Drd1 and Drd2 in DMS and DLS after the tests. Based on the in situ hybridization colabeling results, we tested the causal role of DMS D 1 and D 2 family receptors, and DMS neuronal ensembles in "incubated" methamphetamine seeking, using selective dopamine receptor antagonists (SCH39166 or raclopride) and the Daun02 chemogenetic inactivation procedure, respectively. Methamphetamine seeking was higher after 21 d of voluntary abstinence than after 1 d (incubation of methamphetamine craving). The incubated response was associated with increased Fos expression in DMS but not in DLS; Fos was colabeled with both Drd1 and Drd2 DMS injections of SCH39166 or raclopride selectively decreased methamphetamine seeking after 21 abstinence days. In Fos-lacZ transgenic rats, selective inactivation of relapse test-activated Fos neurons in DMS on abstinence day 18 decreased incubated methamphetamine seeking on day 21. Results demonstrate a role of DMS dopamine D 1 and D 2 receptors in the incubation of methamphetamine craving after voluntary abstinence and that DMS neuronal ensembles mediate this incubation. In human addicts, abstinence is often self-imposed and relapse can be triggered by exposure to drug-associated cues that induce drug craving. We recently developed a rat model of incubation of methamphetamine craving after choice-based voluntary abstinence. Here, we used classical pharmacology, in situ hybridization, immunohistochemistry, and the Daun02 inactivation procedure to demonstrate a critical role of dorsomedial striatum neuronal ensembles in this new form of incubation of drug craving. Copyright © 2017 the authors 0270-6474/17/371014-14$15.00/0.

Neuronal activity determines distinct gliotransmitter release from a single astrocyte

PubMed Central

Covelo, Ana

2018-01-01

Accumulating evidence indicates that astrocytes are actively involved in brain function by regulating synaptic activity and plasticity. Different gliotransmitters, such as glutamate, ATP, GABA or D-serine, released form astrocytes have been shown to induce different forms of synaptic regulation. However, whether a single astrocyte may release different gliotransmitters is unknown. Here we show that mouse hippocampal astrocytes activated by endogenous (neuron-released endocannabinoids or GABA) or exogenous (single astrocyte Ca2+ uncaging) stimuli modulate putative single CA3-CA1 hippocampal synapses. The astrocyte-mediated synaptic modulation was biphasic and consisted of an initial glutamate-mediated potentiation followed by a purinergic-mediated depression of neurotransmitter release. The temporal dynamic properties of this biphasic synaptic regulation depended on the firing frequency and duration of the neuronal activity that stimulated astrocytes. Present results indicate that single astrocytes can decode neuronal activity and, in response, release distinct gliotransmitters to differentially regulate neurotransmission at putative single synapses. PMID:29380725

Two-photon calcium imaging during fictive navigation in virtual environments

PubMed Central

Ahrens, Misha B.; Huang, Kuo Hua; Narayan, Sujatha; Mensh, Brett D.; Engert, Florian

2013-01-01

A full understanding of nervous system function requires recording from large populations of neurons during naturalistic behaviors. Here we enable paralyzed larval zebrafish to fictively navigate two-dimensional virtual environments while we record optically from many neurons with two-photon imaging. Electrical recordings from motor nerves in the tail are decoded into intended forward swims and turns, which are used to update a virtual environment displayed underneath the fish. Several behavioral features—such as turning responses to whole-field motion and dark avoidance—are well-replicated in this virtual setting. We readily observed neuronal populations in the hindbrain with laterally selective responses that correlated with right or left optomotor behavior. We also observed neurons in the habenula, pallium, and midbrain with response properties specific to environmental features. Beyond single-cell correlations, the classification of network activity in such virtual settings promises to reveal principles of brainwide neural dynamics during behavior. PMID:23761738

Two-photon calcium imaging during fictive navigation in virtual environments.

PubMed

Ahrens, Misha B; Huang, Kuo Hua; Narayan, Sujatha; Mensh, Brett D; Engert, Florian

2013-01-01

A full understanding of nervous system function requires recording from large populations of neurons during naturalistic behaviors. Here we enable paralyzed larval zebrafish to fictively navigate two-dimensional virtual environments while we record optically from many neurons with two-photon imaging. Electrical recordings from motor nerves in the tail are decoded into intended forward swims and turns, which are used to update a virtual environment displayed underneath the fish. Several behavioral features-such as turning responses to whole-field motion and dark avoidance-are well-replicated in this virtual setting. We readily observed neuronal populations in the hindbrain with laterally selective responses that correlated with right or left optomotor behavior. We also observed neurons in the habenula, pallium, and midbrain with response properties specific to environmental features. Beyond single-cell correlations, the classification of network activity in such virtual settings promises to reveal principles of brainwide neural dynamics during behavior.

Primate amygdala neurons evaluate the progress of self-defined economic choice sequences

PubMed Central

Grabenhorst, Fabian; Hernadi, Istvan; Schultz, Wolfram

2016-01-01

The amygdala is a prime valuation structure yet its functions in advanced behaviors are poorly understood. We tested whether individual amygdala neurons encode a critical requirement for goal-directed behavior: the evaluation of progress during sequential choices. As monkeys progressed through choice sequences toward rewards, amygdala neurons showed phasic, gradually increasing responses over successive choice steps. These responses occurred in the absence of external progress cues or motor preplanning. They were often specific to self-defined sequences, typically disappearing during instructed control sequences with similar reward expectation. Their build-up rate reflected prospectively the forthcoming choice sequence, suggesting adaptation to an internal plan. Population decoding demonstrated a high-accuracy progress code. These findings indicate that amygdala neurons evaluate the progress of planned, self-defined behavioral sequences. Such progress signals seem essential for aligning stepwise choices with internal plans. Their presence in amygdala neurons may inform understanding of human conditions with amygdala dysfunction and deregulated reward pursuit. DOI: http://dx.doi.org/10.7554/eLife.18731.001 PMID:27731795

Neuronal prediction of opponent's behavior during cooperative social interchange in primates.

PubMed

Haroush, Keren; Williams, Ziv M

2015-03-12

A cornerstone of successful social interchange is the ability to anticipate each other's intentions or actions. While generating these internal predictions is essential for constructive social behavior, their single neuronal basis and causal underpinnings are unknown. Here, we discover specific neurons in the primate dorsal anterior cingulate that selectively predict an opponent's yet unknown decision to invest in their common good or defect and distinct neurons that encode the monkey's own current decision based on prior outcomes. Mixed population predictions of the other was remarkably near optimal compared to behavioral decoders. Moreover, disrupting cingulate activity selectively biased mutually beneficial interactions between the monkeys but, surprisingly, had no influence on their decisions when no net-positive outcome was possible. These findings identify a group of other-predictive neurons in the primate anterior cingulate essential for enacting cooperative interactions and may pave a way toward the targeted treatment of social behavioral disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Primate amygdala neurons evaluate the progress of self-defined economic choice sequences.

PubMed

Grabenhorst, Fabian; Hernadi, Istvan; Schultz, Wolfram

2016-10-12

The amygdala is a prime valuation structure yet its functions in advanced behaviors are poorly understood. We tested whether individual amygdala neurons encode a critical requirement for goal-directed behavior: the evaluation of progress during sequential choices. As monkeys progressed through choice sequences toward rewards, amygdala neurons showed phasic, gradually increasing responses over successive choice steps. These responses occurred in the absence of external progress cues or motor preplanning. They were often specific to self-defined sequences, typically disappearing during instructed control sequences with similar reward expectation. Their build-up rate reflected prospectively the forthcoming choice sequence, suggesting adaptation to an internal plan. Population decoding demonstrated a high-accuracy progress code. These findings indicate that amygdala neurons evaluate the progress of planned, self-defined behavioral sequences. Such progress signals seem essential for aligning stepwise choices with internal plans. Their presence in amygdala neurons may inform understanding of human conditions with amygdala dysfunction and deregulated reward pursuit.

A hidden Markov model for decoding and the analysis of replay in spike trains.

PubMed

Box, Marc; Jones, Matt W; Whiteley, Nick

2016-12-01

We present a hidden Markov model that describes variation in an animal's position associated with varying levels of activity in action potential spike trains of individual place cell neurons. The model incorporates a coarse-graining of position, which we find to be a more parsimonious description of the system than other models. We use a sequential Monte Carlo algorithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding). We obtain greater accuracy over other methods in the conditions of high temporal resolution and small neuronal sample size. We also present a novel, model-based approach to the study of replay: the expression of spike train activity related to behaviour during times of motionlessness or sleep, thought to be integral to the consolidation of long-term memories. We demonstrate how we can detect the time, information content and compression rate of replay events in simulated and real hippocampal data recorded from rats in two different environments, and verify the correlation between the times of detected replay events and of sharp wave/ripples in the local field potential.

Effects of isoflurane anesthesia on ensemble patterns of Ca2+ activity in mouse v1: reduced direction selectivity independent of increased correlations in cellular activity.

PubMed

Goltstein, Pieter M; Montijn, Jorrit S; Pennartz, Cyriel M A

2015-01-01

Anesthesia affects brain activity at the molecular, neuronal and network level, but it is not well-understood how tuning properties of sensory neurons and network connectivity change under its influence. Using in vivo two-photon calcium imaging we matched neuron identity across episodes of wakefulness and anesthesia in the same mouse and recorded spontaneous and visually evoked activity patterns of neuronal ensembles in these two states. Correlations in spontaneous patterns of calcium activity between pairs of neurons were increased under anesthesia. While orientation selectivity remained unaffected by anesthesia, this treatment reduced direction selectivity, which was attributable to an increased response to the null-direction. As compared to anesthesia, populations of V1 neurons coded more mutual information on opposite stimulus directions during wakefulness, whereas information on stimulus orientation differences was lower. Increases in correlations of calcium activity during visual stimulation were correlated with poorer population coding, which raised the hypothesis that the anesthesia-induced increase in correlations may be causal to degrading directional coding. Visual stimulation under anesthesia, however, decorrelated ongoing activity patterns to a level comparable to wakefulness. Because visual stimulation thus appears to 'break' the strength of pairwise correlations normally found in spontaneous activity under anesthesia, the changes in correlational structure cannot explain the awake-anesthesia difference in direction coding. The population-wide decrease in coding for stimulus direction thus occurs independently of anesthesia-induced increments in correlations of spontaneous activity.

Effects of Isoflurane Anesthesia on Ensemble Patterns of Ca2+ Activity in Mouse V1: Reduced Direction Selectivity Independent of Increased Correlations in Cellular Activity

PubMed Central

Goltstein, Pieter M.; Montijn, Jorrit S.; Pennartz, Cyriel M. A.

2015-01-01

Anesthesia affects brain activity at the molecular, neuronal and network level, but it is not well-understood how tuning properties of sensory neurons and network connectivity change under its influence. Using in vivo two-photon calcium imaging we matched neuron identity across episodes of wakefulness and anesthesia in the same mouse and recorded spontaneous and visually evoked activity patterns of neuronal ensembles in these two states. Correlations in spontaneous patterns of calcium activity between pairs of neurons were increased under anesthesia. While orientation selectivity remained unaffected by anesthesia, this treatment reduced direction selectivity, which was attributable to an increased response to the null-direction. As compared to anesthesia, populations of V1 neurons coded more mutual information on opposite stimulus directions during wakefulness, whereas information on stimulus orientation differences was lower. Increases in correlations of calcium activity during visual stimulation were correlated with poorer population coding, which raised the hypothesis that the anesthesia-induced increase in correlations may be causal to degrading directional coding. Visual stimulation under anesthesia, however, decorrelated ongoing activity patterns to a level comparable to wakefulness. Because visual stimulation thus appears to ‘break’ the strength of pairwise correlations normally found in spontaneous activity under anesthesia, the changes in correlational structure cannot explain the awake-anesthesia difference in direction coding. The population-wide decrease in coding for stimulus direction thus occurs independently of anesthesia-induced increments in correlations of spontaneous activity. PMID:25706867

Face-selective and auditory neurons in the primate orbitofrontal cortex.

PubMed

Rolls, Edmund T; Critchley, Hugo D; Browning, Andrew S; Inoue, Kazuo

2006-03-01

Neurons with responses selective for faces are described in the macaque orbitofrontal cortex. The neurons typically respond 2-13 times more to the best face than to the best non-face stimulus, and have response latencies which are typically in the range of 130-220 ms. Some of these face-selective neurons respond to identity, and others to facial expression. Some of the neurons do not have different responses to different views of a face, which is a useful property of neurons responding to face identity. Other neurons have view-dependent responses, and some respond to moving but not still heads. The neurons with face expression, face movement, or face view-dependent responses would all be useful as part of a system decoding and representing signals important in social interactions. The representation of face identity is also important in social interactions, for it provides some of the information needed in order to make different responses to different individuals. In addition, some orbitofrontal cortex neurons were shown to be tuned to auditory stimuli, including for some neurons, the sound of vocalizations. The findings are relevant to understanding the functions of the primate including human orbitofrontal cortex in normal behaviour, and to understanding the effects of damage to this region in humans.

Modality-independent representations of small quantities based on brain activation patterns.

PubMed

Damarla, Saudamini Roy; Cherkassky, Vladimir L; Just, Marcel Adam

2016-04-01

Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these techniques to investigate whether neural representations of quantities depicted in one modality (say, visual) can be decoded from brain activation patterns evoked by quantities depicted in the other modality (say, auditory). The main finding demonstrated, for the first time, that quantities of dots were decodable by a classifier that was trained on the neural patterns evoked by quantities of auditory tones, and vice-versa. The representations that were common across modalities were mainly right-lateralized in frontal and parietal regions. A second finding was that the neural patterns in parietal cortex that represent quantities were common across participants. These findings demonstrate a common neuronal foundation for the representation of quantities across sensory modalities and participants and provide insight into the role of parietal cortex in the representation of quantity information. © 2016 Wiley Periodicals, Inc.

Decoding the dynamic representation of musical pitch from human brain activity.

PubMed

Sankaran, N; Thompson, W F; Carlile, S; Carlson, T A

2018-01-16

In music, the perception of pitch is governed largely by its tonal function given the preceding harmonic structure of the music. While behavioral research has advanced our understanding of the perceptual representation of musical pitch, relatively little is known about its representational structure in the brain. Using Magnetoencephalography (MEG), we recorded evoked neural responses to different tones presented within a tonal context. Multivariate Pattern Analysis (MVPA) was applied to "decode" the stimulus that listeners heard based on the underlying neural activity. We then characterized the structure of the brain's representation using decoding accuracy as a proxy for representational distance, and compared this structure to several well established perceptual and acoustic models. The observed neural representation was best accounted for by a model based on the Standard Tonal Hierarchy, whereby differences in the neural encoding of musical pitches correspond to their differences in perceived stability. By confirming that perceptual differences honor those in the underlying neuronal population coding, our results provide a crucial link in understanding the cognitive foundations of musical pitch across psychological and neural domains.

Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate

PubMed Central

Padmanaban, Subash; Baker, Justin; Greger, Bradley

2018-01-01

Objective: The performance of machine learning algorithms used for neural decoding of dexterous tasks may be impeded due to problems arising when dealing with high-dimensional data. The objective of feature selection algorithms is to choose a near-optimal subset of features from the original feature space to improve the performance of the decoding algorithm. The aim of our study was to compare the effects of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis (PCA), and Mutual Information Maximization on SVM classification performance for a dexterous decoding task. Approach: A nonhuman primate (NHP) was trained to perform small coordinated movements—similar to typing. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials (AP) during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon AP firing rates. We used the SVM classification to examine the functional parameters of (i) robustness to simulated failure and (ii) longevity of classification. We also compared the effect of using isolated-neuron and multi-unit firing rates as the feature vector supplied to the SVM. Main results: The average decoding accuracy for multi-unit features and single-unit features using Mutual Information Maximization (MIM) across 47 sessions was 96.74 ± 3.5% and 97.65 ± 3.36% respectively. The reduction in decoding accuracy between using 100% of the features and 10% of features based on MIM was 45.56% (from 93.7 to 51.09%) and 4.75% (from 95.32 to 90.79%) for multi-unit and single-unit features respectively. MIM had best performance compared to other feature selection methods. Significance: These results suggest improved decoding performance can be achieved by using optimally selected features. The results based on clinically relevant performance metrics also suggest that the decoding algorithm can be made robust by using optimal features and feature selection algorithms. We believe that even a few percent increase in performance is important and improves the decoding accuracy of the machine learning algorithm potentially increasing the ease of use of a brain machine interface. PMID:29467602

An optogenetics- and imaging-assisted simultaneous multiple patch-clamp recording system for decoding complex neural circuits

PubMed Central

Wang, Guangfu; Wyskiel, Daniel R; Yang, Weiguo; Wang, Yiqing; Milbern, Lana C; Lalanne, Txomin; Jiang, Xiaolong; Shen, Ying; Sun, Qian-Quan; Zhu, J Julius

2015-01-01

Deciphering neuronal circuitry is central to understanding brain function and dysfunction, yet it remains a daunting task. To facilitate the dissection of neuronal circuits, a process requiring functional analysis of synaptic connections and morphological identification of interconnected neurons, we present here a method for stable simultaneous octuple patch-clamp recordings. This method allows physiological analysis of synaptic interconnections among 4–8 simultaneously recorded neurons and/or 10–30 sequentially recorded neurons, and it allows anatomical identification of >85% of recorded interneurons and >99% of recorded principal neurons. We describe how to apply the method to rodent tissue slices; however, it can be used on other model organisms. We also describe the latest refinements and optimizations of mechanics, electronics, optics and software programs that are central to the realization of a combined single- and two-photon microscopy–based, optogenetics- and imaging-assisted, stable, simultaneous quadruple–viguple patch-clamp recording system. Setting up the system, from the beginning of instrument assembly and software installation to full operation, can be completed in 3–4 d. PMID:25654757

Decoding the ubiquitous role of microRNAs in neurogenesis.

PubMed

Nampoothiri, Sreekala S; Rajanikant, G K

2017-04-01

Neurogenesis generates fledgling neurons that mature to form an intricate neuronal circuitry. The delusion on adult neurogenesis was far resolved in the past decade and became one of the largely explored domains to identify multifaceted mechanisms bridging neurodevelopment and neuropathology. Neurogenesis encompasses multiple processes including neural stem cell proliferation, neuronal differentiation, and cell fate determination. Each neurogenic process is specifically governed by manifold signaling pathways, several growth factors, coding, and non-coding RNAs. A class of small non-coding RNAs, microRNAs (miRNAs), is ubiquitously expressed in the brain and has emerged to be potent regulators of neurogenesis. It functions by fine-tuning the expression of specific neurogenic gene targets at the post-transcriptional level and modulates the development of mature neurons from neural progenitor cells. Besides the commonly discussed intrinsic factors, the neuronal morphogenesis is also under the control of several extrinsic temporal cues, which in turn are regulated by miRNAs. This review enlightens on dicer controlled switch from neurogenesis to gliogenesis, miRNA regulation of neuronal maturation and the differential expression of miRNAs in response to various extrinsic cues affecting neurogenesis.

Overlapping memory trace indispensable for linking, but not recalling, individual memories.

PubMed

Yokose, Jun; Okubo-Suzuki, Reiko; Nomoto, Masanori; Ohkawa, Noriaki; Nishizono, Hirofumi; Suzuki, Akinobu; Matsuo, Mina; Tsujimura, Shuhei; Takahashi, Yukari; Nagase, Masashi; Watabe, Ayako M; Sasahara, Masakiyo; Kato, Fusao; Inokuchi, Kaoru

2017-01-27

Memories are not stored in isolation from other memories but are integrated into associative networks. However, the mechanisms underlying memory association remain elusive. Using two amygdala-dependent behavioral paradigms-conditioned taste aversion (CTA) and auditory-cued fear conditioning (AFC)-in mice, we found that presenting the conditioned stimulus used for the CTA task triggered the conditioned response of the AFC task after natural coreactivation of the memories. This was accompanied through an increase in the overlapping neuronal ensemble in the basolateral amygdala. Silencing of the overlapping ensemble suppressed CTA retrieval-induced freezing. However, retrieval of the original CTA or AFC memory was not affected. A small population of coshared neurons thus mediates the link between memories. They are not necessary for recalling individual memories. Copyright © 2017, American Association for the Advancement of Science.

A cortical neural prosthesis for restoring and enhancing memory

NASA Astrophysics Data System (ADS)

Berger, Theodore W.; Hampson, Robert E.; Song, Dong; Goonawardena, Anushka; Marmarelis, Vasilis Z.; Deadwyler, Sam A.

2011-08-01

A primary objective in developing a neural prosthesis is to replace neural circuitry in the brain that no longer functions appropriately. Such a goal requires artificial reconstruction of neuron-to-neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. In this study, the application of a specially designed neural prosthesis using a multi-input/multi-output (MIMO) nonlinear model is demonstrated by using trains of electrical stimulation pulses to substitute for MIMO model derived ensemble firing patterns. Ensembles of CA3 and CA1 hippocampal neurons, recorded from rats performing a delayed-nonmatch-to-sample (DNMS) memory task, exhibited successful encoding of trial-specific sample lever information in the form of different spatiotemporal firing patterns. MIMO patterns, identified online and in real-time, were employed within a closed-loop behavioral paradigm. Results showed that the model was able to predict successful performance on the same trial. Also, MIMO model-derived patterns, delivered as electrical stimulation to the same electrodes, improved performance under normal testing conditions and, more importantly, were capable of recovering performance when delivered to animals with ensemble hippocampal activity compromised by pharmacologic blockade of synaptic transmission. These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.

Odors regulate Arc expression in neuronal ensembles engaged in odor processing.

PubMed

Guthrie, K; Rayhanabad, J; Kuhl, D; Gall, C

2000-06-26

Synaptic activity is critical to developmental and plastic processes that produce long-term changes in neuronal connectivity and function. Genes expressed by neurons in an activity-dependent fashion are of particular interest since the proteins they encode may mediate neuronal plasticity. One such gene encodes the activity-regulated cytoskeleton-associated protein, Arc. The present study evaluated the effects of odor stimulation on Arc expression in rat olfactory bulb. Arc mRNA was rapidly increased in functionally linked cohorts of neurons topographically activated by odor stimuli. These included neurons surrounding individual glomeruli, mitral cells and transynaptically activated granule cells. Dendritic Arc immunoreactivity was also increased in odor-activated glomeruli. Our results suggest that odor regulation of Arc expression may contribute to activity-dependent structural changes associated with olfactory experience.

Neural Prediction of Multidimensional Decisions in Monkey Superior Colliculus

NASA Astrophysics Data System (ADS)

Hasegawa, Ryohei P.; Hasegawa, Yukako T.; Segraves, Mark A.

To examine the function of the superior colliculus (SC) in decision-making processes and the application of its single trial activity for “neural mind reading,” we recorded from SC deep layers while two monkeys performed oculomotor go/no-go tasks. We have recently focused on monitoring single trial activities in single SC neurons, and designed a virtual decision function (VDF) to provide a good estimation of single-dimensional decisions (go/no-go decisions for a cue presented at a specific visual field, a response field of each neuron). In this study, we used two VDFs for multidimensional decisions (go/no-go decisions at two cue locations) with the ensemble activity which was simultaneously recorded from a small group (4 to 6) of neurons at both sides of the SC. VDFs predicted cue locations as well as go/no-go decisions. These results suggest that monitoring of ensemble SC activity had sufficient capacity to predict multidimensional decisions on a trial-by-trial basis, which is an ideal candidate to serve for cognitive brain-machine interfaces (BMI) such as two-dimensional word spellers.

Identification of cytokine-specific sensory neural signals by decoding murine vagus nerve activity.

PubMed

Zanos, Theodoros P; Silverman, Harold A; Levy, Todd; Tsaava, Tea; Battinelli, Emily; Lorraine, Peter W; Ashe, Jeffrey M; Chavan, Sangeeta S; Tracey, Kevin J; Bouton, Chad E

2018-05-22

The nervous system maintains physiological homeostasis through reflex pathways that modulate organ function. This process begins when changes in the internal milieu (e.g., blood pressure, temperature, or pH) activate visceral sensory neurons that transmit action potentials along the vagus nerve to the brainstem. IL-1β and TNF, inflammatory cytokines produced by immune cells during infection and injury, and other inflammatory mediators have been implicated in activating sensory action potentials in the vagus nerve. However, it remains unclear whether neural responses encode cytokine-specific information. Here we develop methods to isolate and decode specific neural signals to discriminate between two different cytokines. Nerve impulses recorded from the vagus nerve of mice exposed to IL-1β and TNF were sorted into groups based on their shape and amplitude, and their respective firing rates were computed. This revealed sensory neural groups responding specifically to TNF and IL-1β in a dose-dependent manner. These cytokine-mediated responses were subsequently decoded using a Naive Bayes algorithm that discriminated between no exposure and exposures to IL-1β and TNF (mean successful identification rate 82.9 ± 17.8%, chance level 33%). Recordings obtained in IL-1 receptor-KO mice were devoid of IL-1β-related signals but retained their responses to TNF. Genetic ablation of TRPV1 neurons attenuated the vagus neural signals mediated by IL-1β, and distal lidocaine nerve block attenuated all vagus neural signals recorded. The results obtained in this study using the methodological framework suggest that cytokine-specific information is present in sensory neural signals within the vagus nerve. Copyright © 2018 the Author(s). Published by PNAS.

Matrix Metalloproteinase-9 regulates neuronal circuit development and excitability

PubMed Central

Murase, Sachiko; Lantz, Crystal; Kim, Eunyoung; Gupta, Nitin; Higgins, Richard; Stopfer, Mark; Hoffman, Dax A.; Quinlan, Elizabeth M.

2015-01-01

In early postnatal development, naturally occurring cell death, dendritic outgrowth and synaptogenesis sculpt neuronal ensembles into functional neuronal circuits. Here we demonstrate that deletion of the extracellular proteinase MMP-9 affects each of these processes, resulting in maladapted neuronal circuitry. MMP-9 deletion increases the number of CA1 pyramidal neurons, but decreases dendritic length and complexity while dendritic spine density is unchanged. Parallel changes in neuronal morphology are observed in primary visual cortex, and persist into adulthood. Individual CA1 neurons in MMP-9−/− mice have enhanced input resistance and a significant increase in the frequency, but not amplitude, of miniature excitatory postsynaptic currents (mEPSCs). Additionally, deletion of MMP-9 significant increases spontaneous neuronal activity in awake MMP-9−/− mice and enhances response to acute challenge by the excitotoxin kainate. Thus MMP-9-dependent proteolysis regulates several aspects of circuit maturation to constrain excitability throughout life. PMID:26093382

Attention Enhances Synaptic Efficacy and Signal-to-Noise in Neural Circuits

PubMed Central

Briggs, Farran; Mangun, George R.; Usrey, W. Martin

2013-01-01

Summary Attention is a critical component of perception. However, the mechanisms by which attention modulates neuronal communication to guide behavior are poorly understood. To elucidate the synaptic mechanisms of attention, we developed a sensitive assay of attentional modulation of neuronal communication. In alert monkeys performing a visual spatial attention task, we probed thalamocortical communication by electrically stimulating neurons in the lateral geniculate nucleus of the thalamus while simultaneously recording shock-evoked responses from monosynaptically connected neurons in primary visual cortex. We found that attention enhances neuronal communication by (1) increasing the efficacy of presynaptic input in driving postsynaptic responses, (2) increasing synchronous responses among ensembles of postsynaptic neurons receiving independent input, and (3) decreasing redundant signals between postsynaptic neurons receiving common input. These results demonstrate that attention finely tunes neuronal communication at the synaptic level by selectively altering synaptic weights, enabling enhanced detection of salient events in the noisy sensory milieu. PMID:23803766

Nonsinusoidal neuronal oscillations: bug or feature?

PubMed

Lozano-Soldevilla, Diego

2018-05-01

There is compiling evidence suggesting that independent neuronal ensembles are coordinated in time and space through cross-frequency coupling (CFC). However, recent studies have convincingly demonstrated that nonsinusoidal oscillations produce serious biases in state of the art CFC metrics. Although most of studies treat nonsinusoidal waves as a nuisance or just ignore them, fortunately some scientists are starting to exploit their neurophysiological relevance opening new research vistas with critical implications.

Modulation of synaptic transmission from segmental afferents by spontaneous activity of dorsal horn spinal neurones in the cat.

PubMed

Manjarrez, E; Rojas-Piloni, J G; Jimenez, I; Rudomin, P

2000-12-01

We examined, in the anaesthetised cat, the influence of the neuronal ensembles producing spontaneous negative cord dorsum potentials (nCDPs) on segmental pathways mediating primary afferent depolarisation (PAD) of cutaneous and group I muscle afferents and on Ia monosynaptic activation of spinal motoneurones. The intraspinal distribution of the field potentials associated with the spontaneous nCDPs indicated that the neuronal ensembles involved in the generation of these potentials were located in the dorsal horn of lumbar segments, in the same region of termination of low-threshold cutaneous afferents. During the occurrence of spontaneous nCDPs, transmission from low-threshold cutaneous afferents to second order neurones in laminae III-VI, as well as transmission along pathways mediating PAD of cutaneous and Ib afferents, was facilitated. PAD of Ia afferents was instead inhibited. Monosynaptic reflexes of flexors and extensors were facilitated during the spontaneous nCDPs. The magnitude of the facilitation was proportional to the amplitude of the 'conditioning' spontaneous nCDPs. This led to a high positive correlation between amplitude fluctuations of spontaneous nCDPs and fluctuations of monosynaptic reflexes. Stimulation of low-threshold cutaneous afferents transiently reduced the probability of occurrence of spontaneous nCDPs as well as the fluctuations of monosynaptic reflexes. It is concluded that the spontaneous nCDPs were produced by the activation of a population of dorsal horn neurones that shared the same functional pathways and involved the same set of neurones as those responding monosynaptically to stimulation of large cutaneous afferents. The spontaneous activity of these neurones was probably the main cause of the fluctuations of the monosynaptic reflexes observed under anaesthesia and could provide a dynamic linkage between segmental sensory and motor pathways.

Unique processing during a period of high excitation/inhibition balance in adult-born neurons.

PubMed

Marín-Burgin, Antonia; Mongiat, Lucas A; Pardi, M Belén; Schinder, Alejandro F

2012-03-09

The adult dentate gyrus generates new granule cells (GCs) that develop over several weeks and integrate into the preexisting network. Although adult hippocampal neurogenesis has been implicated in learning and memory, the specific role of new GCs remains unclear. We examined whether immature adult-born neurons contribute to information encoding. By combining calcium imaging and electrophysiology in acute slices, we found that weak afferent activity recruits few mature GCs while activating a substantial proportion of the immature neurons. These different activation thresholds are dictated by an enhanced excitation/inhibition balance transiently expressed in immature GCs. Immature GCs exhibit low input specificity that switches with time toward a highly specific responsiveness. Therefore, activity patterns entering the dentate gyrus can undergo differential decoding by a heterogeneous population of GCs originated at different times.

The neuronal encoding of information in the brain.

PubMed

Rolls, Edmund T; Treves, Alessandro

2011-11-01

We describe the results of quantitative information theoretic analyses of neural encoding, particularly in the primate visual, olfactory, taste, hippocampal, and orbitofrontal cortex. Most of the information turns out to be encoded by the firing rates of the neurons, that is by the number of spikes in a short time window. This has been shown to be a robust code, for the firing rate representations of different neurons are close to independent for small populations of neurons. Moreover, the information can be read fast from such encoding, in as little as 20 ms. In quantitative information theoretic studies, only a little additional information is available in temporal encoding involving stimulus-dependent synchronization of different neurons, or the timing of spikes within the spike train of a single neuron. Feature binding appears to be solved by feature combination neurons rather than by temporal synchrony. The code is sparse distributed, with the spike firing rate distributions close to exponential or gamma. A feature of the code is that it can be read by neurons that take a synaptically weighted sum of their inputs. This dot product decoding is biologically plausible. Understanding the neural code is fundamental to understanding not only how the cortex represents, but also processes, information. Copyright © 2011 Elsevier Ltd. All rights reserved.

Testing Neuronal Accounts of Anisotropic Motion Perception with Computational Modelling

PubMed Central

Wong, William; Chiang Price, Nicholas Seow

2014-01-01

There is an over-representation of neurons in early visual cortical areas that respond most strongly to cardinal (horizontal and vertical) orientations and directions of visual stimuli, and cardinal- and oblique-preferring neurons are reported to have different tuning curves. Collectively, these neuronal anisotropies can explain two commonly-reported phenomena of motion perception – the oblique effect and reference repulsion – but it remains unclear whether neuronal anisotropies can simultaneously account for both perceptual effects. We show in psychophysical experiments that reference repulsion and the oblique effect do not depend on the duration of a moving stimulus, and that brief adaptation to a single direction simultaneously causes a reference repulsion in the orientation domain, and the inverse of the oblique effect in the direction domain. We attempted to link these results to underlying neuronal anisotropies by implementing a large family of neuronal decoding models with parametrically varied levels of anisotropy in neuronal direction-tuning preferences, tuning bandwidths and spiking rates. Surprisingly, no model instantiation was able to satisfactorily explain our perceptual data. We argue that the oblique effect arises from the anisotropic distribution of preferred directions evident in V1 and MT, but that reference repulsion occurs separately, perhaps reflecting a process of categorisation occurring in higher-order cortical areas. PMID:25409518

On the Number of Neurons and Time Scale of Integration Underlying the Formation of Percepts in the Brain

PubMed Central

Wohrer, Adrien; Machens, Christian K.

2015-01-01

All of our perceptual experiences arise from the activity of neural populations. Here we study the formation of such percepts under the assumption that they emerge from a linear readout, i.e., a weighted sum of the neurons’ firing rates. We show that this assumption constrains the trial-to-trial covariance structure of neural activities and animal behavior. The predicted covariance structure depends on the readout parameters, and in particular on the temporal integration window w and typical number of neurons K used in the formation of the percept. Using these predictions, we show how to infer the readout parameters from joint measurements of a subject’s behavior and neural activities. We consider three such scenarios: (1) recordings from the complete neural population, (2) recordings of neuronal sub-ensembles whose size exceeds K, and (3) recordings of neuronal sub-ensembles that are smaller than K. Using theoretical arguments and artificially generated data, we show that the first two scenarios allow us to recover the typical spatial and temporal scales of the readout. In the third scenario, we show that the readout parameters can only be recovered by making additional assumptions about the structure of the full population activity. Our work provides the first thorough interpretation of (feed-forward) percept formation from a population of sensory neurons. We discuss applications to experimental recordings in classic sensory decision-making tasks, which will hopefully provide new insights into the nature of perceptual integration. PMID:25793393

Causal Interrogation of Neuronal Networks and Behavior through Virally Transduced Ivermectin Receptors.

PubMed

Obenhaus, Horst A; Rozov, Andrei; Bertocchi, Ilaria; Tang, Wannan; Kirsch, Joachim; Betz, Heinrich; Sprengel, Rolf

2016-01-01

The causal interrogation of neuronal networks involved in specific behaviors requires the spatially and temporally controlled modulation of neuronal activity. For long-term manipulation of neuronal activity, chemogenetic tools provide a reasonable alternative to short-term optogenetic approaches. Here we show that virus mediated gene transfer of the ivermectin (IVM) activated glycine receptor mutant GlyRα1 (AG) can be used for the selective and reversible silencing of specific neuronal networks in mice. In the striatum, dorsal hippocampus, and olfactory bulb, GlyRα1 (AG) promoted IVM dependent effects in representative behavioral assays. Moreover, GlyRα1 (AG) mediated silencing had a strong and reversible impact on neuronal ensemble activity and c-Fos activation in the olfactory bulb. Together our results demonstrate that long-term, reversible and re-inducible neuronal silencing via GlyRα1 (AG) is a promising tool for the interrogation of network mechanisms underlying the control of behavior and memory formation.

Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm

NASA Astrophysics Data System (ADS)

Makin, Joseph G.; O'Doherty, Joseph E.; Cardoso, Mariana M. B.; Sabes, Philip N.

2018-04-01

Objective. The aim of this work is to improve the state of the art for motor-control with a brain-machine interface (BMI). BMIs use neurological recording devices and decoding algorithms to transform brain activity directly into real-time control of a machine, archetypically a robotic arm or a cursor. The standard procedure treats neural activity—vectors of spike counts in small temporal windows—as noisy observations of the kinematic state (position, velocity, acceleration) of the fingertip. Inferring the state from the observations then takes the form of a dynamical filter, typically some variant on Kalman’s (KF). The KF, however, although fairly robust in practice, is optimal only when the relationships between variables are linear and the noise is Gaussian, conditions usually violated in practice. Approach. To overcome these limitations we introduce a new filter, the ‘recurrent exponential-family harmonium’ (rEFH), that models the spike counts explicitly as Poisson-distributed, and allows for arbitrary nonlinear dynamics and observation models. Furthermore, the model underlying the filter is acquired through unsupervised learning, which allows temporal correlations in spike counts to be explained by latent dynamics that do not necessarily correspond to the kinematic state of the fingertip. Main results. We test the rEFH on offline reconstruction of the kinematics of reaches in the plane. The rEFH outperforms the standard, as well as three other state-of-the-art, decoders, across three monkeys, two different tasks, most kinematic variables, and a range of bin widths, amounts of training data, and numbers of neurons. Significance. Our algorithm establishes a new state of the art for offline decoding of reaches—in particular, for fingertip velocities, the variable used for control in most online decoders.

Extraction of Inter-Aural Time Differences Using a Spiking Neuron Network Model of the Medial Superior Olive.

PubMed

Encke, Jörg; Hemmert, Werner

2018-01-01

The mammalian auditory system is able to extract temporal and spectral features from sound signals at the two ears. One important cue for localization of low-frequency sound sources in the horizontal plane are inter-aural time differences (ITDs) which are first analyzed in the medial superior olive (MSO) in the brainstem. Neural recordings of ITD tuning curves at various stages along the auditory pathway suggest that ITDs in the mammalian brainstem are not represented in form of a Jeffress-type place code. An alternative is the hemispheric opponent-channel code, according to which ITDs are encoded as the difference in the responses of the MSO nuclei in the two hemispheres. In this study, we present a physiologically-plausible, spiking neuron network model of the mammalian MSO circuit and apply two different methods of extracting ITDs from arbitrary sound signals. The network model is driven by a functional model of the auditory periphery and physiological models of the cochlear nucleus and the MSO. Using a linear opponent-channel decoder, we show that the network is able to detect changes in ITD with a precision down to 10 μs and that the sensitivity of the decoder depends on the slope of the ITD-rate functions. A second approach uses an artificial neuronal network to predict ITDs directly from the spiking output of the MSO and ANF model. Using this predictor, we show that the MSO-network is able to reliably encode static and time-dependent ITDs over a large frequency range, also for complex signals like speech.

Neural coordination during reach-to-grasp

PubMed Central

Vaidya, Mukta; Kording, Konrad; Saleh, Maryam; Takahashi, Kazutaka

2015-01-01

When reaching to grasp, we coordinate how we preshape the hand with how we move it. To ask how motor cortical neurons participate in this coordination, we examined the interactions between reach- and grasp-related neuronal ensembles while monkeys reached to grasp a variety of different objects in different locations. By describing the dynamics of these two ensembles as trajectories in a low-dimensional state space, we examined their coupling in time. We found evidence for temporal compensation across many different reach-to-grasp conditions such that if one neural trajectory led in time the other tended to catch up, reducing the asynchrony between the trajectories. Granger causality revealed bidirectional interactions between reach and grasp neural trajectories beyond that which could be attributed to the joint kinematics that were consistently stronger in the grasp-to-reach direction. Characterizing cortical coordination dynamics provides a new framework for understanding the functional interactions between neural populations. PMID:26224773

Activity-dependent switch of GABAergic inhibition into glutamatergic excitation in astrocyte-neuron networks.

PubMed

Perea, Gertrudis; Gómez, Ricardo; Mederos, Sara; Covelo, Ana; Ballesteros, Jesús J; Schlosser, Laura; Hernández-Vivanco, Alicia; Martín-Fernández, Mario; Quintana, Ruth; Rayan, Abdelrahman; Díez, Adolfo; Fuenzalida, Marco; Agarwal, Amit; Bergles, Dwight E; Bettler, Bernhard; Manahan-Vaughan, Denise; Martín, Eduardo D; Kirchhoff, Frank; Araque, Alfonso

2016-12-24

Interneurons are critical for proper neural network function and can activate Ca 2+ signaling in astrocytes. However, the impact of the interneuron-astrocyte signaling into neuronal network operation remains unknown. Using the simplest hippocampal Astrocyte-Neuron network, i.e., GABAergic interneuron, pyramidal neuron, single CA3-CA1 glutamatergic synapse, and astrocytes, we found that interneuron-astrocyte signaling dynamically affected excitatory neurotransmission in an activity- and time-dependent manner, and determined the sign (inhibition vs potentiation) of the GABA-mediated effects. While synaptic inhibition was mediated by GABA A receptors, potentiation involved astrocyte GABA B receptors, astrocytic glutamate release, and presynaptic metabotropic glutamate receptors. Using conditional astrocyte-specific GABA B receptor ( Gabbr1 ) knockout mice, we confirmed the glial source of the interneuron-induced potentiation, and demonstrated the involvement of astrocytes in hippocampal theta and gamma oscillations in vivo. Therefore, astrocytes decode interneuron activity and transform inhibitory into excitatory signals, contributing to the emergence of novel network properties resulting from the interneuron-astrocyte interplay.

A Causal Role for V5/MT Neurons Coding Motion-Disparity Conjunctions in Resolving Perceptual Ambiguity

PubMed Central

Krug, Kristine; Cicmil, Nela; Parker, Andrew J.; Cumming, Bruce G.

2013-01-01

Summary Judgments about the perceptual appearance of visual objects require the combination of multiple parameters, like location, direction, color, speed, and depth. Our understanding of perceptual judgments has been greatly informed by studies of ambiguous figures, which take on different appearances depending upon the brain state of the observer. Here we probe the neural mechanisms hypothesized as responsible for judging the apparent direction of rotation of ambiguous structure from motion (SFM) stimuli. Resolving the rotation direction of SFM cylinders requires the conjoint decoding of direction of motion and binocular depth signals [1, 2]. Within cortical visual area V5/MT of two macaque monkeys, we applied electrical stimulation at sites with consistent multiunit tuning to combinations of binocular depth and direction of motion, while the monkey made perceptual decisions about the rotation of SFM stimuli. For both ambiguous and unambiguous SFM figures, rotation judgments shifted as if we had added a specific conjunction of disparity and motion signals to the stimulus elements. This is the first causal demonstration that the activity of neurons in V5/MT contributes directly to the perception of SFM stimuli and by implication to decoding the specific conjunction of disparity and motion, the two different visual cues whose combination drives the perceptual judgment. PMID:23871244

Measuring Fisher Information Accurately in Correlated Neural Populations

PubMed Central

Kohn, Adam; Pouget, Alexandre

2015-01-01

Neural responses are known to be variable. In order to understand how this neural variability constrains behavioral performance, we need to be able to measure the reliability with which a sensory stimulus is encoded in a given population. However, such measures are challenging for two reasons: First, they must take into account noise correlations which can have a large influence on reliability. Second, they need to be as efficient as possible, since the number of trials available in a set of neural recording is usually limited by experimental constraints. Traditionally, cross-validated decoding has been used as a reliability measure, but it only provides a lower bound on reliability and underestimates reliability substantially in small datasets. We show that, if the number of trials per condition is larger than the number of neurons, there is an alternative, direct estimate of reliability which consistently leads to smaller errors and is much faster to compute. The superior performance of the direct estimator is evident both for simulated data and for neuronal population recordings from macaque primary visual cortex. Furthermore we propose generalizations of the direct estimator which measure changes in stimulus encoding across conditions and the impact of correlations on encoding and decoding, typically denoted by Ishuffle and Idiag respectively. PMID:26030735

Direct cortical control of 3D neuroprosthetic devices.

PubMed

Taylor, Dawn M; Tillery, Stephen I Helms; Schwartz, Andrew B

2002-06-07

Three-dimensional (3D) movement of neuroprosthetic devices can be controlled by the activity of cortical neurons when appropriate algorithms are used to decode intended movement in real time. Previous studies assumed that neurons maintain fixed tuning properties, and the studies used subjects who were unaware of the movements predicted by their recorded units. In this study, subjects had real-time visual feedback of their brain-controlled trajectories. Cell tuning properties changed when used for brain-controlled movements. By using control algorithms that track these changes, subjects made long sequences of 3D movements using far fewer cortical units than expected. Daily practice improved movement accuracy and the directional tuning of these units.

An online brain-machine interface using decoding of movement direction from the human electrocorticogram

NASA Astrophysics Data System (ADS)

Milekovic, Tomislav; Fischer, Jörg; Pistohl, Tobias; Ruescher, Johanna; Schulze-Bonhage, Andreas; Aertsen, Ad; Rickert, Jörn; Ball, Tonio; Mehring, Carsten

2012-08-01

A brain-machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single neurons. We show that the same approach can be realized using brain activity measured directly from the surface of the human cortex using electrocorticography (ECoG). Five subjects, implanted with ECoG implants for the purpose of epilepsy assessment, took part in our study. Subjects used directionally dependent ECoG signals, recorded during active movements of a single arm, to control a computer cursor in one out of two directions. Significant BMI control was achieved in four out of five subjects with correct directional decoding in 69%-86% of the trials (75% on average). Our results demonstrate the feasibility of an online BMI using decoding of movement direction from human ECoG signals. Thus, to achieve such BMIs, ECoG signals might be used in conjunction with or as an alternative to intracortical neural signals.

Shape encoding consistency across colors in primate V4

PubMed Central

Bushnell, Brittany N.

2012-01-01

Neurons in primate cortical area V4 are sensitive to the form and color of visual stimuli. To determine whether form selectivity remains consistent across colors, we studied the responses of single V4 neurons in awake monkeys to a set of two-dimensional shapes presented in two different colors. For each neuron, we chose two colors that were visually distinct and that evoked reliable and different responses. Across neurons, the correlation coefficient between responses in the two colors ranged from −0.03 to 0.93 (median 0.54). Neurons with highly consistent shape responses, i.e., high correlation coefficients, showed greater dispersion in their responses to the different shapes, i.e., greater shape selectivity, and also tended to have less eccentric receptive field locations; among shape-selective neurons, shape consistency ranged from 0.16 to 0.93 (median 0.63). Consistency of shape responses was independent of the physical difference between the stimulus colors used and the strength of neuronal color tuning. Finally, we found that our measurement of shape response consistency was strongly influenced by the number of stimulus repeats: consistency estimates based on fewer than 10 repeats were substantially underestimated. In conclusion, our results suggest that neurons that are likely to contribute to shape perception and discrimination exhibit shape responses that are largely consistent across colors, facilitating the use of simpler algorithms for decoding shape information from V4 neuronal populations. PMID:22673324

Decoding the Dopamine Signal in Macaque Prefrontal Cortex: A Simulation Study Using the Cx3Dp Simulator

PubMed Central

Spühler, Isabelle Ayumi; Hauri, Andreas

2013-01-01

Dopamine transmission in the prefrontal cortex plays an important role in reward based learning, working memory and attention. Dopamine is thought to be released non-synaptically into the extracellular space and to reach distant receptors through diffusion. This simulation study examines how the dopamine signal might be decoded by the recipient neuron. The simulation was based on parameters from the literature and on our own quantified, structural data from macaque prefrontal area 10. The change in extracellular dopamine concentration was estimated at different distances from release sites and related to the affinity of the dopamine receptors. Due to the sparse and random distribution of release sites, a transient heterogeneous pattern of dopamine concentration emerges. Our simulation predicts, however, that at any point in the simulation volume there is sufficient dopamine to bind and activate high-affinity dopamine receptors. We propose that dopamine is broadcast to its distant receptors and any change from the local baseline concentration might be decoded by a transient change in the binding probability of dopamine receptors. Dopamine could thus provide a graduated ‘teaching’ signal to reinforce concurrently active synapses and cell assemblies. In conditions of highly reduced or highly elevated dopamine levels the simulations predict that relative changes in the dopamine signal can no longer be decoded, which might explain why cognitive deficits are observed in patients with Parkinson’s disease, or induced through drugs blocking dopamine reuptake. PMID:23951205

Toward Optimal Target Placement for Neural Prosthetic Devices

PubMed Central

Cunningham, John P.; Yu, Byron M.; Gilja, Vikash; Ryu, Stephen I.; Shenoy, Krishna V.

2008-01-01

Neural prosthetic systems have been designed to estimate continuous reach trajectories (motor prostheses) and to predict discrete reach targets (communication prostheses). In the latter case, reach targets are typically decoded from neural spiking activity during an instructed delay period before the reach begins. Such systems use targets placed in radially symmetric geometries independent of the tuning properties of the neurons available. Here we seek to automate the target placement process and increase decode accuracy in communication prostheses by selecting target locations based on the neural population at hand. Motor prostheses that incorporate intended target information could also benefit from this consideration. We present an optimal target placement algorithm that approximately maximizes decode accuracy with respect to target locations. In simulated neural spiking data fit from two monkeys, the optimal target placement algorithm yielded statistically significant improvements up to 8 and 9% for two and sixteen targets, respectively. For four and eight targets, gains were more modest, as the target layouts found by the algorithm closely resembled the canonical layouts. We trained a monkey in this paradigm and tested the algorithm with experimental neural data to confirm some of the results found in simulation. In all, the algorithm can serve not only to create new target layouts that outperform canonical layouts, but it can also confirm or help select among multiple canonical layouts. The optimal target placement algorithm developed here is the first algorithm of its kind, and it should both improve decode accuracy and help automate target placement for neural prostheses. PMID:18829845

Neural noise and movement-related codes in the macaque supplementary motor area.

PubMed

Averbeck, Bruno B; Lee, Daeyeol

2003-08-20

We analyzed the variability of spike counts and the coding capacity of simultaneously recorded pairs of neurons in the macaque supplementary motor area (SMA). We analyzed the mean-variance functions for single neurons, as well as signal and noise correlations between pairs of neurons. All three statistics showed a strong dependence on the bin width chosen for analysis. Changes in the correlation structure of single neuron spike trains over different bin sizes affected the mean-variance function, and signal and noise correlations between pairs of neurons were much smaller at small bin widths, increasing monotonically with the width of the bin. Analyses in the frequency domain showed that the noise between pairs of neurons, on average, was most strongly correlated at low frequencies, which explained the increase in noise correlation with increasing bin width. The coding performance was analyzed to determine whether the temporal precision of spike arrival times and the interactions within and between neurons could improve the prediction of the upcoming movement. We found that in approximately 62% of neuron pairs, the arrival times of spikes at a resolution between 66 and 40 msec carried more information than spike counts in a 200 msec bin. In addition, in 19% of neuron pairs, inclusion of within (11%)- or between-neuron (8%) correlations in spike trains improved decoding accuracy. These results suggest that in some SMA neurons elements of the spatiotemporal pattern of activity may be relevant for neural coding.

Lateral and feedforward inhibition suppress asynchronous activity in a large, biophysically-detailed computational model of the striatal network

PubMed Central

Moyer, Jason T.; Halterman, Benjamin L.; Finkel, Leif H.; Wolf, John A.

2014-01-01

Striatal medium spiny neurons (MSNs) receive lateral inhibitory projections from other MSNs and feedforward inhibitory projections from fast-spiking, parvalbumin-containing striatal interneurons (FSIs). The functional roles of these connections are unknown, and difficult to study in an experimental preparation. We therefore investigated the functionality of both lateral (MSN-MSN) and feedforward (FSI-MSN) inhibition using a large-scale computational model of the striatal network. The model consists of 2744 MSNs comprised of 189 compartments each and 121 FSIs comprised of 148 compartments each, with dendrites explicitly represented and almost all known ionic currents included and strictly constrained by biological data as appropriate. Our analysis of the model indicates that both lateral inhibition and feedforward inhibition function at the population level to limit non-ensemble MSN spiking while preserving ensemble MSN spiking. Specifically, lateral inhibition enables large ensembles of MSNs firing synchronously to strongly suppress non-ensemble MSNs over a short time-scale (10–30 ms). Feedforward inhibition enables FSIs to strongly inhibit weakly activated, non-ensemble MSNs while moderately inhibiting activated ensemble MSNs. Importantly, FSIs appear to more effectively inhibit MSNs when FSIs fire asynchronously. Both types of inhibition would increase the signal-to-noise ratio of responding MSN ensembles and contribute to the formation and dissolution of MSN ensembles in the striatal network. PMID:25505406

Using big data to map the network organization of the brain.

PubMed

Swain, James E; Sripada, Chandra; Swain, John D

2014-02-01

The past few years have shown a major rise in network analysis of "big data" sets in the social sciences, revealing non-obvious patterns of organization and dynamic principles. We speculate that the dependency dimension - individuality versus sociality - might offer important insights into the dynamics of neurons and neuronal ensembles. Connectomic neural analyses, informed by social network theory, may be helpful in understanding underlying fundamental principles of brain organization.

Using big data to map the network organization of the brain

PubMed Central

Swain, James E.; Sripada, Chandra; Swain, John D.

2015-01-01

The past few years have shown a major rise in network analysis of “big data” sets in the social sciences, revealing non-obvious patterns of organization and dynamic principles. We speculate that the dependency dimension – individuality versus sociality – might offer important insights into the dynamics of neurons and neuronal ensembles. Connectomic neural analyses, informed by social network theory, may be helpful in understanding underlying fundamental principles of brain organization. PMID:24572243

Processing of frequency-modulated sounds in the lateral auditory belt cortex of the rhesus monkey.

PubMed

Tian, Biao; Rauschecker, Josef P

2004-11-01

Single neurons were recorded from the lateral belt areas, anterolateral (AL), mediolateral (ML), and caudolateral (CL), of nonprimary auditory cortex in 4 adult rhesus monkeys under gas anesthesia, while the neurons were stimulated with frequency-modulated (FM) sweeps. Responses to FM sweeps, measured as the firing rate of the neurons, were invariably greater than those to tone bursts. In our stimuli, frequency changed linearly from low to high frequencies (FM direction "up") or high to low frequencies ("down") at varying speeds (FM rates). Neurons were highly selective to the rate and direction of the FM sweep. Significant differences were found between the 3 lateral belt areas with regard to their FM rate preferences: whereas neurons in ML responded to the whole range of FM rates, AL neurons responded better to slower FM rates in the range of naturally occurring communication sounds. CL neurons generally responded best to fast FM rates at a speed of several hundred Hz/ms, which have the broadest frequency spectrum. These selectivities are consistent with a role of AL in the decoding of communication sounds and of CL in the localization of sounds, which works best with broader bandwidths. Together, the results support the hypothesis of parallel streams for the processing of different aspects of sounds, including auditory objects and auditory space.

Differential Activation of Fast-Spiking and Regular-Firing Neuron Populations During Movement and Reward in the Dorsal Medial Frontal Cortex

PubMed Central

Insel, Nathan; Barnes, Carol A.

2015-01-01

The medial prefrontal cortex is thought to be important for guiding behavior according to an animal's expectations. Efforts to decode the region have focused not only on the question of what information it computes, but also how distinct circuit components become engaged during behavior. We find that the activity of regular-firing, putative projection neurons contains rich information about behavioral context and firing fields cluster around reward sites, while activity among putative inhibitory and fast-spiking neurons is most associated with movement and accompanying sensory stimulation. These dissociations were observed even between adjacent neurons with apparently reciprocal, inhibitory–excitatory connections. A smaller population of projection neurons with burst-firing patterns did not show clustered firing fields around rewards; these neurons, although heterogeneous, were generally less selective for behavioral context than regular-firing cells. The data suggest a network that tracks an animal's behavioral situation while, at the same time, regulating excitation levels to emphasize high valued positions. In this scenario, the function of fast-spiking inhibitory neurons is to constrain network output relative to incoming sensory flow. This scheme could serve as a bridge between abstract sensorimotor information and single-dimensional codes for value, providing a neural framework to generate expectations from behavioral state. PMID:24700585

Local and Global Spatial Organization of Interaural Level Difference and Frequency Preferences in Auditory Cortex

PubMed Central

Panniello, Mariangela; King, Andrew J; Dahmen, Johannes C; Walker, Kerry M M

2018-01-01

Abstract Despite decades of microelectrode recordings, fundamental questions remain about how auditory cortex represents sound-source location. Here, we used in vivo 2-photon calcium imaging to measure the sensitivity of layer II/III neurons in mouse primary auditory cortex (A1) to interaural level differences (ILDs), the principal spatial cue in this species. Although most ILD-sensitive neurons preferred ILDs favoring the contralateral ear, neurons with either midline or ipsilateral preferences were also present. An opponent-channel decoder accurately classified ILDs using the difference in responses between populations of neurons that preferred contralateral-ear-greater and ipsilateral-ear-greater stimuli. We also examined the spatial organization of binaural tuning properties across the imaged neurons with unprecedented resolution. Neurons driven exclusively by contralateral ear stimuli or by binaural stimulation occasionally formed local clusters, but their binaural categories and ILD preferences were not spatially organized on a more global scale. In contrast, the sound frequency preferences of most neurons within local cortical regions fell within a restricted frequency range, and a tonotopic gradient was observed across the cortical surface of individual mice. These results indicate that the representation of ILDs in mouse A1 is comparable to that of most other mammalian species, and appears to lack systematic or consistent spatial order. PMID:29136122

Somatosensory responses in a human motor cortex

PubMed Central

Donoghue, John P.; Hochberg, Leigh R.

2013-01-01

Somatic sensory signals provide a major source of feedback to motor cortex. Changes in somatosensory systems after stroke or injury could profoundly influence brain computer interfaces (BCI) being developed to create new output signals from motor cortex activity patterns. We had the unique opportunity to study the responses of hand/arm area neurons in primary motor cortex to passive joint manipulation in a person with a long-standing brain stem stroke but intact sensory pathways. Neurons responded to passive manipulation of the contralateral shoulder, elbow, or wrist as predicted from prior studies of intact primates. Thus fundamental properties and organization were preserved despite arm/hand paralysis and damage to cortical outputs. The same neurons were engaged by attempted arm actions. These results indicate that intact sensory pathways retain the potential to influence primary motor cortex firing rates years after cortical outputs are interrupted and may contribute to online decoding of motor intentions for BCI applications. PMID:23343902

Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ericson, Milton Nance; McKnight, Timothy E; Melechko, Anatoli Vasilievich

2012-01-01

Neural chips, which are capable of simultaneous, multi-site neural recording and stimulation, have been used to detect and modulate neural activity for almost 30 years. As a neural interface, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information ofmore » neuroplasticity. This novel nano-neuron interface can potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single cell level and even inside the cell.« less

Oscillatory integration windows in neurons

PubMed Central

Gupta, Nitin; Singh, Swikriti Saran; Stopfer, Mark

2016-01-01

Oscillatory synchrony among neurons occurs in many species and brain areas, and has been proposed to help neural circuits process information. One hypothesis states that oscillatory input creates cyclic integration windows: specific times in each oscillatory cycle when postsynaptic neurons become especially responsive to inputs. With paired local field potential (LFP) and intracellular recordings and controlled stimulus manipulations we directly test this idea in the locust olfactory system. We find that inputs arriving in Kenyon cells (KCs) sum most effectively in a preferred window of the oscillation cycle. With a computational model, we show that the non-uniform structure of noise in the membrane potential helps mediate this process. Further experiments performed in vivo demonstrate that integration windows can form in the absence of inhibition and at a broad range of oscillation frequencies. Our results reveal how a fundamental coincidence-detection mechanism in a neural circuit functions to decode temporally organized spiking. PMID:27976720

Pharmacogenetic reactivation of the original engram evokes an extinguished fear memory.

PubMed

Yoshii, Takahiro; Hosokawa, Hiroshi; Matsuo, Naoki

2017-02-01

Fear memory extinction has several characteristic behavioral features, such as spontaneous recovery, renewal, and reinstatement, suggesting that extinction training does not erase the original association between the conditioned stimulus (CS) and the unconditioned stimulus (US). However, it is unclear whether reactivation of the original physical record of memory (i.e., memory trace) is sufficient to produce conditioned fear response after extinction. Here, we performed pharmacogenetic neuronal activation using transgenic mice expressing hM3Dq DREADD (designer receptor exclusively activated by designer drug) under the control of the activity-dependent c-fos gene promoter. Neuronal ensembles activated during fear-conditioned learning were tagged with hM3Dq and subsequently reactivated after extinction training. The mice exhibited significant freezing, even when the fear memory was no longer triggered by external CS, indicating that the artificial reactivation of a specific neuronal ensemble was sufficient to evoke the extinguished fear response. This freezing was not observed in non-fear-conditioned mice expressing hM3dq in the same brain areas. These results directly demonstrated that at least part of the original fear memory trace remains after extinction, and such residual plasticity might reflect the persistent memory. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamics of Multistable States during Ongoing and Evoked Cortical Activity

PubMed Central

Mazzucato, Luca

2015-01-01

Single-trial analyses of ensemble activity in alert animals demonstrate that cortical circuits dynamics evolve through temporal sequences of metastable states. Metastability has been studied for its potential role in sensory coding, memory, and decision-making. Yet, very little is known about the network mechanisms responsible for its genesis. It is often assumed that the onset of state sequences is triggered by an external stimulus. Here we show that state sequences can be observed also in the absence of overt sensory stimulation. Analysis of multielectrode recordings from the gustatory cortex of alert rats revealed ongoing sequences of states, where single neurons spontaneously attain several firing rates across different states. This single-neuron multistability represents a challenge to existing spiking network models, where typically each neuron is at most bistable. We present a recurrent spiking network model that accounts for both the spontaneous generation of state sequences and the multistability in single-neuron firing rates. Each state results from the activation of neural clusters with potentiated intracluster connections, with the firing rate in each cluster depending on the number of active clusters. Simulations show that the model's ensemble activity hops among the different states, reproducing the ongoing dynamics observed in the data. When probed with external stimuli, the model predicts the quenching of single-neuron multistability into bistability and the reduction of trial-by-trial variability. Both predictions were confirmed in the data. Together, these results provide a theoretical framework that captures both ongoing and evoked network dynamics in a single mechanistic model. PMID:26019337

A theory of neural dimensionality, dynamics, and measurement

NASA Astrophysics Data System (ADS)

Ganguli, Surya

In many experiments, neuroscientists tightly control behavior, record many trials, and obtain trial-averaged firing rates from hundreds of neurons in circuits containing millions of behaviorally relevant neurons. Dimensionality reduction has often shown that such datasets are strikingly simple; they can be described using a much smaller number of dimensions than the number of recorded neurons, and the resulting projections onto these dimensions yield a remarkably insightful dynamical portrait of circuit computation. This ubiquitous simplicity raises several profound and timely conceptual questions. What is the origin of this simplicity and its implications for the complexity of brain dynamics? Would neuronal datasets become more complex if we recorded more neurons? How and when can we trust dynamical portraits obtained from only hundreds of neurons in circuits containing millions of neurons? We present a theory that answers these questions, and test it using neural data recorded from reaching monkeys. Overall, this theory yields a picture of the neural measurement process as a random projection of neural dynamics, conceptual insights into how we can reliably recover dynamical portraits in such under-sampled measurement regimes, and quantitative guidelines for the design of future experiments. Moreover, it reveals the existence of phase transition boundaries in our ability to successfully decode cognition and behavior as a function of the number of recorded neurons, the complexity of the task, and the smoothness of neural dynamics. membership pending.

Memory on time

PubMed Central

Eichenbaum, Howard

2013-01-01

Considerable recent work has shown that the hippocampus is critical for remembering the order of events in distinct experiences, a defining feature of episodic memory. Correspondingly, hippocampal neuronal activity can ‘replay’ sequential events in memories and hippocampal neuronal ensembles represent a gradually changing temporal context signal. Most strikingly, single hippocampal neurons – called time cells – encode moments in temporally structured experiences much as the well-known place cells encode locations in spatially structured experiences. These observations bridge largely disconnected literatures on the role of the hippocampus in episodic memory and spatial mapping, and suggest that the fundamental function of the hippocampus is to establish spatio-temporal frameworks for organizing memories. PMID:23318095

Genealogy of the "grandmother cell".

PubMed

Gross, Charles G

2002-10-01

A "grandmother cell" is a hypothetical neuron that responds only to a highly complex, specific, and meaningful stimulus, such as the image of one's grandmother. The term originated in a parable Jerry Lettvin told in 1967. A similar concept had been systematically developed a few years earlier by Jerzy Konorski who called such cells "gnostic" units. This essay discusses the origin, influence, and current status of these terms and of the alternative view that complex stimuli are represented by the pattern of firing across ensembles of neurons.

Hippocampal Sharp Wave Bursts Coincide with Neocortical "Up-State" Transitions

ERIC Educational Resources Information Center

Battaglia, Francesco P.; Sutherland, Gary R.; McNaughton, Bruce L.

2004-01-01

The sleeping neocortex shows nested oscillatory activity in different frequency ranges, characterized by fluctuations between "up-states" and "down-states." High-density neuronal ensemble recordings in rats now reveal the interaction between synchronized activity in the hippocampus and neocortex: Electroencephalographic sharp…

Chimera states in two-dimensional networks of locally coupled oscillators

NASA Astrophysics Data System (ADS)

Kundu, Srilena; Majhi, Soumen; Bera, Bidesh K.; Ghosh, Dibakar; Lakshmanan, M.

2018-02-01

Chimera state is defined as a mixed type of collective state in which synchronized and desynchronized subpopulations of a network of coupled oscillators coexist and the appearance of such anomalous behavior has strong connection to diverse neuronal developments. Most of the previous studies on chimera states are not extensively done in two-dimensional ensembles of coupled oscillators by taking neuronal systems with nonlinear coupling function into account while such ensembles of oscillators are more realistic from a neurobiological point of view. In this paper, we report the emergence and existence of chimera states by considering locally coupled two-dimensional networks of identical oscillators where each node is interacting through nonlinear coupling function. This is in contrast with the existence of chimera states in two-dimensional nonlocally coupled oscillators with rectangular kernel in the coupling function. We find that the presence of nonlinearity in the coupling function plays a key role to produce chimera states in two-dimensional locally coupled oscillators. We analytically verify explicitly in the case of a network of coupled Stuart-Landau oscillators in two dimensions that the obtained results using Ott-Antonsen approach and our analytical finding very well matches with the numerical results. Next, we consider another type of important nonlinear coupling function which exists in neuronal systems, namely chemical synaptic function, through which the nearest-neighbor (locally coupled) neurons interact with each other. It is shown that such synaptic interacting function promotes the emergence of chimera states in two-dimensional lattices of locally coupled neuronal oscillators. In numerical simulations, we consider two paradigmatic neuronal oscillators, namely Hindmarsh-Rose neuron model and Rulkov map for each node which exhibit bursting dynamics. By associating various spatiotemporal behaviors and snapshots at particular times, we study the chimera states in detail over a large range of coupling parameter. The existence of chimera states is confirmed by instantaneous angular frequency, order parameter and strength of incoherence.

Chimera states in two-dimensional networks of locally coupled oscillators.

PubMed

Kundu, Srilena; Majhi, Soumen; Bera, Bidesh K; Ghosh, Dibakar; Lakshmanan, M

2018-02-01

Chimera state is defined as a mixed type of collective state in which synchronized and desynchronized subpopulations of a network of coupled oscillators coexist and the appearance of such anomalous behavior has strong connection to diverse neuronal developments. Most of the previous studies on chimera states are not extensively done in two-dimensional ensembles of coupled oscillators by taking neuronal systems with nonlinear coupling function into account while such ensembles of oscillators are more realistic from a neurobiological point of view. In this paper, we report the emergence and existence of chimera states by considering locally coupled two-dimensional networks of identical oscillators where each node is interacting through nonlinear coupling function. This is in contrast with the existence of chimera states in two-dimensional nonlocally coupled oscillators with rectangular kernel in the coupling function. We find that the presence of nonlinearity in the coupling function plays a key role to produce chimera states in two-dimensional locally coupled oscillators. We analytically verify explicitly in the case of a network of coupled Stuart-Landau oscillators in two dimensions that the obtained results using Ott-Antonsen approach and our analytical finding very well matches with the numerical results. Next, we consider another type of important nonlinear coupling function which exists in neuronal systems, namely chemical synaptic function, through which the nearest-neighbor (locally coupled) neurons interact with each other. It is shown that such synaptic interacting function promotes the emergence of chimera states in two-dimensional lattices of locally coupled neuronal oscillators. In numerical simulations, we consider two paradigmatic neuronal oscillators, namely Hindmarsh-Rose neuron model and Rulkov map for each node which exhibit bursting dynamics. By associating various spatiotemporal behaviors and snapshots at particular times, we study the chimera states in detail over a large range of coupling parameter. The existence of chimera states is confirmed by instantaneous angular frequency, order parameter and strength of incoherence.

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

PubMed

Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline; Sanchez, Justin C

2012-01-01

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

Conversion of Phase Information into a Spike-Count Code by Bursting Neurons

PubMed Central

Samengo, Inés; Montemurro, Marcelo A.

2010-01-01

Single neurons in the cerebral cortex are immersed in a fluctuating electric field, the local field potential (LFP), which mainly originates from synchronous synaptic input into the local neural neighborhood. As shown by recent studies in visual and auditory cortices, the angular phase of the LFP at the time of spike generation adds significant extra information about the external world, beyond the one contained in the firing rate alone. However, no biologically plausible mechanism has yet been suggested that allows downstream neurons to infer the phase of the LFP at the soma of their pre-synaptic afferents. Therefore, so far there is no evidence that the nervous system can process phase information. Here we study a model of a bursting pyramidal neuron, driven by a time-dependent stimulus. We show that the number of spikes per burst varies systematically with the phase of the fluctuating input at the time of burst onset. The mapping between input phase and number of spikes per burst is a robust response feature for a broad range of stimulus statistics. Our results suggest that cortical bursting neurons could play a crucial role in translating LFP phase information into an easily decodable spike count code. PMID:20300632

Synchrony suppression in ensembles of coupled oscillators via adaptive vanishing feedback.

PubMed

Montaseri, Ghazal; Yazdanpanah, Mohammad Javad; Pikovsky, Arkady; Rosenblum, Michael

2013-09-01

Synchronization and emergence of a collective mode is a general phenomenon, frequently observed in ensembles of coupled self-sustained oscillators of various natures. In several circumstances, in particular in cases of neurological pathologies, this state of the active medium is undesirable. Destruction of this state by a specially designed stimulation is a challenge of high clinical relevance. Typically, the precise effect of an external action on the ensemble is unknown, since the microscopic description of the oscillators and their interactions are not available. We show that, desynchronization in case of a large degree of uncertainty about important features of the system is nevertheless possible; it can be achieved by virtue of a feedback loop with an additional adaptation of parameters. The adaptation also ensures desynchronization of ensembles with non-stationary, time-varying parameters. We perform the stability analysis of the feedback-controlled system and demonstrate efficient destruction of synchrony for several models, including those of spiking and bursting neurons.

Synchrony suppression in ensembles of coupled oscillators via adaptive vanishing feedback

NASA Astrophysics Data System (ADS)

Montaseri, Ghazal; Javad Yazdanpanah, Mohammad; Pikovsky, Arkady; Rosenblum, Michael

2013-09-01

Synchronization and emergence of a collective mode is a general phenomenon, frequently observed in ensembles of coupled self-sustained oscillators of various natures. In several circumstances, in particular in cases of neurological pathologies, this state of the active medium is undesirable. Destruction of this state by a specially designed stimulation is a challenge of high clinical relevance. Typically, the precise effect of an external action on the ensemble is unknown, since the microscopic description of the oscillators and their interactions are not available. We show that, desynchronization in case of a large degree of uncertainty about important features of the system is nevertheless possible; it can be achieved by virtue of a feedback loop with an additional adaptation of parameters. The adaptation also ensures desynchronization of ensembles with non-stationary, time-varying parameters. We perform the stability analysis of the feedback-controlled system and demonstrate efficient destruction of synchrony for several models, including those of spiking and bursting neurons.

Decoding brain state transitions in the pedunculopontine nucleus: cooperative phasic and tonic mechanisms

PubMed Central

Petzold, Anne; Valencia, Miguel; Pál, Balázs; Mena-Segovia, Juan

2015-01-01

Cholinergic neurons of the pedunculopontine nucleus (PPN) are most active during the waking state. Their activation is deemed to cause a switch in the global brain activity from sleep to wakefulness, while their sustained discharge may contribute to upholding the waking state and enhancing arousal. Similarly, non-cholinergic PPN neurons are responsive to brain state transitions and their activation may influence some of the same targets of cholinergic neurons, suggesting that they operate in coordination. Yet, it is not clear how the discharge of distinct classes of PPN neurons organize during brain states. Here, we monitored the in vivo network activity of PPN neurons in the anesthetized rat across two distinct levels of cortical dynamics and their transitions. We identified a highly structured configuration in PPN network activity during slow-wave activity that was replaced by decorrelated activity during the activated state (AS). During the transition, neurons were predominantly excited (phasically or tonically), but some were inhibited. Identified cholinergic neurons displayed phasic and short latency responses to sensory stimulation, whereas the majority of non-cholinergic showed tonic responses and remained at high discharge rates beyond the state transition. In vitro recordings demonstrate that cholinergic neurons exhibit fast adaptation that prevents them from discharging at high rates over prolonged time periods. Our data shows that PPN neurons have distinct but complementary roles during brain state transitions, where cholinergic neurons provide a fast and transient response to sensory events that drive state transitions, whereas non-cholinergic neurons maintain an elevated firing rate during global activation. PMID:26582977

A causal role for V5/MT neurons coding motion-disparity conjunctions in resolving perceptual ambiguity.

PubMed

Krug, Kristine; Cicmil, Nela; Parker, Andrew J; Cumming, Bruce G

2013-08-05

Judgments about the perceptual appearance of visual objects require the combination of multiple parameters, like location, direction, color, speed, and depth. Our understanding of perceptual judgments has been greatly informed by studies of ambiguous figures, which take on different appearances depending upon the brain state of the observer. Here we probe the neural mechanisms hypothesized as responsible for judging the apparent direction of rotation of ambiguous structure from motion (SFM) stimuli. Resolving the rotation direction of SFM cylinders requires the conjoint decoding of direction of motion and binocular depth signals [1, 2]. Within cortical visual area V5/MT of two macaque monkeys, we applied electrical stimulation at sites with consistent multiunit tuning to combinations of binocular depth and direction of motion, while the monkey made perceptual decisions about the rotation of SFM stimuli. For both ambiguous and unambiguous SFM figures, rotation judgments shifted as if we had added a specific conjunction of disparity and motion signals to the stimulus elements. This is the first causal demonstration that the activity of neurons in V5/MT contributes directly to the perception of SFM stimuli and by implication to decoding the specific conjunction of disparity and motion, the two different visual cues whose combination drives the perceptual judgment. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data

PubMed Central

O'Donnell, Cian; alves, J. Tiago Gonç; Whiteley, Nick; Portera-Cailliau, Carlos; Sejnowski, Terrence J.

2017-01-01

Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded (∼2Neurons). Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate (the number of neurons synchronously active) and the probability that each individual neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex about 65 ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and, surprisingly, found that it first increases, and then decreases during development. This statistical model opens new options for interrogating neural population data and can bolster the use of modern large-scale in vivo Ca2+ and voltage imaging tools. PMID:27870612

Neuronal Correlates of Functional Coupling between Reach- and Grasp-Related Components of Muscle Activity

PubMed Central

Geed, Shashwati; McCurdy, Martha L.; van Kan, Peter L. E.

2017-01-01

Coordinated reach-to-grasp movements require precise spatiotemporal synchrony between proximal forelimb muscles (shoulder, elbow) that transport the hand toward a target during reach, and distal muscles (wrist, digit) that simultaneously preshape and orient the hand for grasp. The precise mechanisms through which the redundant neuromuscular circuitry coordinates reach with grasp, however, remain unclear. Recently, Geed and Van Kan (2016) demonstrated, using exploratory factor analysis (EFA), that limited numbers of global, template-like transport/preshape- and grasp-related muscle components underlie the complexity and variability of intramuscular electromyograms (EMGs) of up to 21 distal and proximal muscles recorded while monkeys performed reach-to-grasp tasks. Importantly, transport/preshape- and grasp-related muscle components showed invariant spatiotemporal coupling, which provides a potential mechanism for coordinating forelimb muscles during reach-to-grasp movements. In the present study, we tested whether ensemble discharges of forelimb neurons in the cerebellar nucleus interpositus (NI) and its target, the magnocellular red nucleus (RNm), a source of rubrospinal fibers, function as neuronal correlates of the transport/preshape- and grasp-related muscle components we identified. EFA applied to single-unit discharges of populations of NI and RNm neurons recorded while the same monkeys that were used previously performed the same reach-to-grasp tasks, revealed neuronal components in the ensemble discharges of both NI and RNm neuronal populations with characteristics broadly similar to muscle components. Subsets of NI and RNm neuronal components were strongly and significantly crosscorrelated with subsets of muscle components, suggesting that similar functional units of reach-to-grasp behavior are expressed by NI and RNm neuronal populations and forelimb muscles. Importantly, like transport/preshape- and grasp-related muscle components, their NI and RNm neuronal correlates showed invariant spatiotemporal coupling. Clinical and lesion studies have reported disruption of coupling between reach and grasp following cerebellar damage; the present results expand on those studies by identifying a neuronal mechanism that may underlie cerebellar contributions to spatiotemporal coordination of distal and proximal limb muscles during reaching to grasp. We conclude that finding similar functional units of behavior expressed at multiple levels of information processing along interposito-rubrospinal pathways and forelimb muscles supports the hypothesis that functionally related populations of NI and RNm neurons act synergistically in the control of complex coordinated motor behaviors. PMID:28270752

Intra-day signal instabilities affect decoding performance in an intracortical neural interface system.

PubMed

Perge, János A; Homer, Mark L; Malik, Wasim Q; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P; Hochberg, Leigh R

2013-06-01

Motor neural interface systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). 84% of the recorded units showed a statistically significant change in apparent firing rate (3.8 ± 8.71 Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and 74% of the units showed a significant change in spike amplitude (3.7 ± 6.5 µV or 5.5% of mean spike amplitude). 40% of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional 'bias' in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in 56% of all performance assessments in participant cursor control (n = 2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions. We anticipate that signal acquisition and decoding methods that can adapt to the reported instabilities will further improve the performance of intracortically-based NISs.

Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila

PubMed Central

Aso, Yoshinori; Sitaraman, Divya; Ichinose, Toshiharu; Kaun, Karla R; Vogt, Katrin; Belliart-Guérin, Ghislain; Plaçais, Pierre-Yves; Robie, Alice A; Yamagata, Nobuhiro; Schnaitmann, Christopher; Rowell, William J; Johnston, Rebecca M; Ngo, Teri-T B; Chen, Nan; Korff, Wyatt; Nitabach, Michael N; Heberlein, Ulrike; Preat, Thomas; Branson, Kristin M; Tanimoto, Hiromu; Rubin, Gerald M

2014-01-01

Animals discriminate stimuli, learn their predictive value and use this knowledge to modify their behavior. In Drosophila, the mushroom body (MB) plays a key role in these processes. Sensory stimuli are sparsely represented by ∼2000 Kenyon cells, which converge onto 34 output neurons (MBONs) of 21 types. We studied the role of MBONs in several associative learning tasks and in sleep regulation, revealing the extent to which information flow is segregated into distinct channels and suggesting possible roles for the multi-layered MBON network. We also show that optogenetic activation of MBONs can, depending on cell type, induce repulsion or attraction in flies. The behavioral effects of MBON perturbation are combinatorial, suggesting that the MBON ensemble collectively represents valence. We propose that local, stimulus-specific dopaminergic modulation selectively alters the balance within the MBON network for those stimuli. Our results suggest that valence encoded by the MBON ensemble biases memory-based action selection. DOI: http://dx.doi.org/10.7554/eLife.04580.001 PMID:25535794

Activity-dependent switch of GABAergic inhibition into glutamatergic excitation in astrocyte-neuron networks

PubMed Central

Perea, Gertrudis; Gómez, Ricardo; Mederos, Sara; Covelo, Ana; Ballesteros, Jesús J; Schlosser, Laura; Hernández-Vivanco, Alicia; Martín-Fernández, Mario; Quintana, Ruth; Rayan, Abdelrahman; Díez, Adolfo; Fuenzalida, Marco; Agarwal, Amit; Bergles, Dwight E; Bettler, Bernhard; Manahan-Vaughan, Denise; Martín, Eduardo D; Kirchhoff, Frank; Araque, Alfonso

2016-01-01

Interneurons are critical for proper neural network function and can activate Ca2+ signaling in astrocytes. However, the impact of the interneuron-astrocyte signaling into neuronal network operation remains unknown. Using the simplest hippocampal Astrocyte-Neuron network, i.e., GABAergic interneuron, pyramidal neuron, single CA3-CA1 glutamatergic synapse, and astrocytes, we found that interneuron-astrocyte signaling dynamically affected excitatory neurotransmission in an activity- and time-dependent manner, and determined the sign (inhibition vs potentiation) of the GABA-mediated effects. While synaptic inhibition was mediated by GABAA receptors, potentiation involved astrocyte GABAB receptors, astrocytic glutamate release, and presynaptic metabotropic glutamate receptors. Using conditional astrocyte-specific GABAB receptor (Gabbr1) knockout mice, we confirmed the glial source of the interneuron-induced potentiation, and demonstrated the involvement of astrocytes in hippocampal theta and gamma oscillations in vivo. Therefore, astrocytes decode interneuron activity and transform inhibitory into excitatory signals, contributing to the emergence of novel network properties resulting from the interneuron-astrocyte interplay. DOI: http://dx.doi.org/10.7554/eLife.20362.001 PMID:28012274

The random energy model in a magnetic field and joint source channel coding

NASA Astrophysics Data System (ADS)

Merhav, Neri

2008-09-01

We demonstrate that there is an intimate relationship between the magnetic properties of Derrida’s random energy model (REM) of spin glasses and the problem of joint source-channel coding in Information Theory. In particular, typical patterns of erroneously decoded messages in the coding problem have “magnetization” properties that are analogous to those of the REM in certain phases, where the non-uniformity of the distribution of the source in the coding problem plays the role of an external magnetic field applied to the REM. We also relate the ensemble performance (random coding exponents) of joint source-channel codes to the free energy of the REM in its different phases.

Playing charades in the fMRI: are mirror and/or mentalizing areas involved in gestural communication?

PubMed

Schippers, Marleen B; Gazzola, Valeria; Goebel, Rainer; Keysers, Christian

2009-08-27

Communication is an important aspect of human life, allowing us to powerfully coordinate our behaviour with that of others. Boiled down to its mere essentials, communication entails transferring a mental content from one brain to another. Spoken language obviously plays an important role in communication between human individuals. Manual gestures however often aid the semantic interpretation of the spoken message, and gestures may have played a central role in the earlier evolution of communication. Here we used the social game of charades to investigate the neural basis of gestural communication by having participants produce and interpret meaningful gestures while their brain activity was measured using functional magnetic resonance imaging. While participants decoded observed gestures, the putative mirror neuron system (pMNS: premotor, parietal and posterior mid-temporal cortex), associated with motor simulation, and the temporo-parietal junction (TPJ), associated with mentalizing and agency attribution, were significantly recruited. Of these areas only the pMNS was recruited during the production of gestures. This suggests that gestural communication relies on a combination of simulation and, during decoding, mentalizing/agency attribution brain areas. Comparing the decoding of gestures with a condition in which participants viewed the same gestures with an instruction not to interpret the gestures showed that although parts of the pMNS responded more strongly during active decoding, most of the pMNS and the TPJ did not show such significant task effects. This suggests that the mere observation of gestures recruits most of the system involved in voluntary interpretation.

Closing the Loop for Memory Prostheses: Detecting the Role of Hippocampal Neural Ensembles Using Nonlinear Models

PubMed Central

Hampson, Robert E.; Song, Dong; Chan, Rosa H.M.; Sweatt, Andrew J.; Riley, Mitchell R.; Goonawardena, Anushka V.; Marmarelis, Vasilis Z.; Gerhardt, Greg A.; Berger, Theodore W.; Deadwyler, Sam A.

2012-01-01

A major factor involved in providing closed loop feedback for control of neural function is to understand how neural ensembles encode online information critical to the final behavioral endpoint. This issue was directly assessed in rats performing a short-term delay memory task in which successful encoding of task information is dependent upon specific spatiotemporal firing patterns recorded from ensembles of CA3 and CA1 hippocampal neurons. Such patterns, extracted by a specially designed nonlinear multi-input multi-output (MIMO) nonlinear mathematical model, were used to predict successful performance online via a closed loop paradigm which regulated trial difficulty (time of retention) as a function of the “strength” of stimulus encoding. The significance of the MIMO model as a neural prosthesis has been demonstrated by substituting trains of electrical stimulation pulses to mimic these same ensemble firing patterns. This feature was used repeatedly to vary “normal” encoding as a means of understanding how neural ensembles can be “tuned” to mimic the inherent process of selecting codes of different strength and functional specificity. The capacity to enhance and tune hippocampal encoding via MIMO model detection and insertion of critical ensemble firing patterns shown here provides the basis for possible extension to other disrupted brain circuitry. PMID:22498704

Noninvasive EEG correlates of overground and stair walking.

PubMed

Brantley, Justin A; Luu, Trieu Phat; Ozdemir, Recep; Zhu, Fangshi; Winslow, Anna T; Huang, Helen; Contreras-Vidal, Jose L

2016-08-01

Automated walking intention detection remains a challenge in lower-limb neuroprosthetic systems. Here, we assess the feasibility of extracting motor intent from scalp electroencephalography (EEG). First, we evaluated the corticomuscular coherence between central EEG electrodes (C1, Cz, C2) and muscles of the shank and thigh during walking on level ground and stairs. Second, we trained decoders to predict the linear envelope of the surface electromyogram (EMG). We observed significant EEG-led corticomuscular coupling between electrodes and sEMG (tibialis anterior) in the high delta (3-4 Hz) and low theta (4-5 Hz) frequency bands during level walking, indicating efferent signaling from the cortex to peripheral motor neurons. The coherence was increased between EEG and vastus lateralis and tibialis anterior in the delta band (<; 2 Hz) during stair ascent, indicating a task specific modulation in corticomuscular coupling. However, EMG was the leading signal for biceps femoris and gastrocnemius coherence during stair ascent, possibly representing afferent feedback loops from periphery to the motor cortex. Decoder validation showed that EEG signals contained information about the sEMG patterns during over ground walking, however, the accuracy of the predicted sEMG patterns decreased during the stair condition. Overall, these initial findings support the feasibility of integrating sEMG and EEG into a hybrid decoder for volitional control of lower limb neuroprostheses.

The Behavioral Relevance of Cortical Neural Ensemble Responses Emerges Suddenly

PubMed Central

Sadacca, Brian F.; Mukherjee, Narendra; Vladusich, Tony; Li, Jennifer X.

2016-01-01

Whereas many laboratory-studied decisions involve a highly trained animal identifying an ambiguous stimulus, many naturalistic decisions do not. Consumption decisions, for instance, involve determining whether to eject or consume an already identified stimulus in the mouth and are decisions that can be made without training. By standard analyses, rodent cortical single-neuron taste responses come to predict such consumption decisions across the 500 ms preceding the consumption or rejection itself; decision-related firing emerges well after stimulus identification. Analyzing single-trial ensemble activity using hidden Markov models, we show these decision-related cortical responses to be part of a reliable sequence of states (each defined by the firing rates within the ensemble) separated by brief state-to-state transitions, the latencies of which vary widely between trials. When we aligned data to the onset of the (late-appearing) state that dominates during the time period in which single-neuron firing is correlated to taste palatability, the apparent ramp in stimulus-aligned choice-related firing was shown to be a much more precipitous coherent jump. This jump in choice-related firing resembled a step function more than it did the output of a standard (ramping) decision-making model, and provided a robust prediction of decision latency in single trials. Together, these results demonstrate that activity related to naturalistic consumption decisions emerges nearly instantaneously in cortical ensembles. SIGNIFICANCE STATEMENT This paper provides a description of how the brain makes evaluative decisions. The majority of work on the neurobiology of decision making deals with “what is it?” decisions; out of this work has emerged a model whereby neurons accumulate information about the stimulus in the form of slowly increasing firing rates and reach a decision when those firing rates reach a threshold. Here, we study a different kind of more naturalistic decision—a decision to evaluate “what shall I do with it?” after the identity of a taste in the mouth has been identified—and show that this decision is not made through the gradual increasing of stimulus-related firing, but rather that this decision appears to be made in a sudden moment of “insight.” PMID:26791199

Synchronization and coordination of sequences in two neural ensembles

NASA Astrophysics Data System (ADS)

Venaille, Antoine; Varona, Pablo; Rabinovich, Mikhail I.

2005-06-01

There are many types of neural networks involved in the sequential motor behavior of animals. For high species, the control and coordination of the network dynamics is a function of the higher levels of the central nervous system, in particular the cerebellum. However, in many cases, especially for invertebrates, such coordination is the result of direct synaptic connections between small circuits. We show here that even the chaotic sequential activity of small model networks can be coordinated by electrotonic synapses connecting one or several pairs of neurons that belong to two different networks. As an example, we analyzed the coordination and synchronization of the sequential activity of two statocyst model networks of the marine mollusk Clione. The statocysts are gravity sensory organs that play a key role in postural control of the animal and the generation of a complex hunting motor program. Each statocyst network was modeled by a small ensemble of neurons with Lotka-Volterra type dynamics and nonsymmetric inhibitory interactions. We studied how two such networks were synchronized by electrical coupling in the presence of an external signal which lead to winnerless competition among the neurons. We found that as a function of the number and the strength of connections between the two networks, it is possible to coordinate and synchronize the sequences that each network generates with its own chaotic dynamics. In spite of the chaoticity, the coordination of the signals is established through an activation sequence lock for those neurons that are active at a particular instant of time.

Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex

PubMed Central

Singer, Wolf; Maass, Wolfgang

2009-01-01

It is currently not known how distributed neuronal responses in early visual areas carry stimulus-related information. We made multielectrode recordings from cat primary visual cortex and applied methods from machine learning in order to analyze the temporal evolution of stimulus-related information in the spiking activity of large ensembles of around 100 neurons. We used sequences of up to three different visual stimuli (letters of the alphabet) presented for 100 ms and with intervals of 100 ms or larger. Most of the information about visual stimuli extractable by sophisticated methods of machine learning, i.e., support vector machines with nonlinear kernel functions, was also extractable by simple linear classification such as can be achieved by individual neurons. New stimuli did not erase information about previous stimuli. The responses to the most recent stimulus contained about equal amounts of information about both this and the preceding stimulus. This information was encoded both in the discharge rates (response amplitudes) of the ensemble of neurons and, when using short time constants for integration (e.g., 20 ms), in the precise timing of individual spikes (≤∼20 ms), and persisted for several 100 ms beyond the offset of stimuli. The results indicate that the network from which we recorded is endowed with fading memory and is capable of performing online computations utilizing information about temporally sequential stimuli. This result challenges models assuming frame-by-frame analyses of sequential inputs. PMID:20027205

Multineuronal vectorization is more efficient than time-segmental vectorization for information extraction from neuronal activities in the inferior temporal cortex.

PubMed

Kaneko, Hidekazu; Tamura, Hiroshi; Tate, Shunta; Kawashima, Takahiro; Suzuki, Shinya S; Fujita, Ichiro

2010-08-01

In order for patients with disabilities to control assistive devices with their own neural activity, multineuronal spike trains must be efficiently decoded because only limited computational resources can be used to generate prosthetic control signals in portable real-time applications. In this study, we compare the abilities of two vectorizing procedures (multineuronal and time-segmental) to extract information from spike trains during the same total neuron-seconds. In the multineuronal vectorizing procedure, we defined a response vector whose components represented the spike counts of one to five neurons. In the time-segmental vectorizing procedure, a response vector consisted of components representing a neuron's spike counts for one to five time-segment(s) of a response period of 1 s. Spike trains were recorded from neurons in the inferior temporal cortex of monkeys presented with visual stimuli. We examined whether the amount of information of the visual stimuli carried by these neurons differed between the two vectorizing procedures. The amount of information calculated with the multineuronal vectorizing procedure, but not the time-segmental vectorizing procedure, significantly increased with the dimensions of the response vector. We conclude that the multineuronal vectorizing procedure is superior to the time-segmental vectorizing procedure in efficiently extracting information from neuronal signals. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Double-Labeled Metabolic Maps of Memory.

ERIC Educational Resources Information Center

John, E. R.; And Others

1986-01-01

Reviews a study which sought to obtain a quantitative metabolic map of the neurons mediating a specific memory. Research results support notions of cooperative processes in which nonrandom behavior of high ensembles of neural elements mediates the integration and processing of information and the retrieval of memory. (ML)

Memory Allocation: Mechanisms and Function.

PubMed

Josselyn, Sheena A; Frankland, Paul W

2018-04-25

Memories for events are thought to be represented in sparse, distributed neuronal ensembles (or engrams). In this article, we review how neurons are chosen to become part of a particular engram, via a process of neuronal allocation. Experiments in rodents indicate that eligible neurons compete for allocation to a given engram, with more excitable neurons winning this competition. Moreover, fluctuations in neuronal excitability determine how engrams interact, promoting either memory integration (via coallocation to overlapping engrams) or separation (via disallocation to nonoverlapping engrams). In parallel with rodent studies, recent findings in humans verify the importance of this memory integration process for linking memories that occur close in time or share related content. A deeper understanding of allocation promises to provide insights into the logic underlying how knowledge is normally organized in the brain and the disorders in which this process has gone awry. Expected final online publication date for the Annual Review of Neuroscience Volume 41 is July 8, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Linking Neurons to Network Function and Behavior by Two-Photon Holographic Optogenetics and Volumetric Imaging.

PubMed

Dal Maschio, Marco; Donovan, Joseph C; Helmbrecht, Thomas O; Baier, Herwig

2017-05-17

We introduce a flexible method for high-resolution interrogation of circuit function, which combines simultaneous 3D two-photon stimulation of multiple targeted neurons, volumetric functional imaging, and quantitative behavioral tracking. This integrated approach was applied to dissect how an ensemble of premotor neurons in the larval zebrafish brain drives a basic motor program, the bending of the tail. We developed an iterative photostimulation strategy to identify minimal subsets of channelrhodopsin (ChR2)-expressing neurons that are sufficient to initiate tail movements. At the same time, the induced network activity was recorded by multiplane GCaMP6 imaging across the brain. From this dataset, we computationally identified activity patterns associated with distinct components of the elicited behavior and characterized the contributions of individual neurons. Using photoactivatable GFP (paGFP), we extended our protocol to visualize single functionally identified neurons and reconstruct their morphologies. Together, this toolkit enables linking behavior to circuit activity with unprecedented resolution. Copyright © 2017 Elsevier Inc. All rights reserved.

Photovoltaic Retinal Prosthesis with High Pixel Density

PubMed Central

Mathieson, Keith; Loudin, James; Goetz, Georges; Huie, Philip; Wang, Lele; Kamins, Theodore I.; Galambos, Ludwig; Smith, Richard; Harris, James S.; Sher, Alexander; Palanker, Daniel

2012-01-01

Retinal degenerative diseases lead to blindness due to loss of the “image capturing” photoreceptors, while neurons in the “image processing” inner retinal layers are relatively well preserved. Electronic retinal prostheses seek to restore sight by electrically stimulating surviving neurons. Most implants are powered through inductive coils, requiring complex surgical methods to implant the coil-decoder-cable-array systems, which deliver energy to stimulating electrodes via intraocular cables. We present a photovoltaic subretinal prosthesis, in which silicon photodiodes in each pixel receive power and data directly through pulsed near-infrared illumination and electrically stimulate neurons. Stimulation was produced in normal and degenerate rat retinas, with pulse durations from 0.5 to 4 ms, and threshold peak irradiances from 0.2 to 10 mW/mm2, two orders of magnitude below the ocular safety limit. Neural responses were elicited by illuminating a single 70 μm bipolar pixel, demonstrating the possibility of a fully-integrated photovoltaic retinal prosthesis with high pixel density. PMID:23049619

Decoding a neural circuit controlling global animal state in C. elegans

PubMed Central

Laurent, Patrick; Soltesz, Zoltan; Nelson, Geoffrey M; Chen, Changchun; Arellano-Carbajal, Fausto; Levy, Emmanuel; de Bono, Mario

2015-01-01

Brains organize behavior and physiology to optimize the response to threats or opportunities. We dissect how 21% O2, an indicator of surface exposure, reprograms C. elegans' global state, inducing sustained locomotory arousal and altering expression of neuropeptides, metabolic enzymes, and other non-neural genes. The URX O2-sensing neurons drive arousal at 21% O2 by tonically activating the RMG interneurons. Stimulating RMG is sufficient to switch behavioral state. Ablating the ASH, ADL, or ASK sensory neurons connected to RMG by gap junctions does not disrupt arousal. However, disrupting cation currents in these neurons curtails RMG neurosecretion and arousal. RMG signals high O2 by peptidergic secretion. Neuropeptide reporters reveal neural circuit state, as neurosecretion stimulates neuropeptide expression. Neural imaging in unrestrained animals shows that URX and RMG encode O2 concentration rather than behavior, while the activity of downstream interneurons such as AVB and AIY reflect both O2 levels and the behavior being executed. DOI: http://dx.doi.org/10.7554/eLife.04241.001 PMID:25760081

Decoding a Decision Process in the Neuronal Population of Dorsal Premotor Cortex.

PubMed

Rossi-Pool, Román; Zainos, Antonio; Alvarez, Manuel; Zizumbo, Jerónimo; Vergara, José; Romo, Ranulfo

2017-12-20

When trained monkeys discriminate the temporal structure of two sequential vibrotactile stimuli, dorsal premotor cortex (DPC) showed high heterogeneity among its neuronal responses. Notably, DPC neurons coded stimulus patterns as broader categories and signaled them during working memory, comparison, and postponed decision periods. Here, we show that such population activity can be condensed into two major coding components: one that persistently represented in working memory both the first stimulus identity and the postponed informed choice and another that transiently coded the initial sensory information and the result of the comparison between the two stimuli. Additionally, we identified relevant signals that coded the timing of task events. These temporal and task-parameter readouts were shown to be strongly linked to the monkeys' behavior when contrasted to those obtained in a non-demanding cognitive control task and during error trials. These signals, hidden in the heterogeneity, were prominently represented by the DPC population response. Copyright © 2017 Elsevier Inc. All rights reserved.

Brain-Wide Maps of "Fos" Expression during Fear Learning and Recall

ERIC Educational Resources Information Center

Cho, Jin-Hyung; Rendall, Sam D.; Gray, Jesse M.

2017-01-01

"Fos" induction during learning labels neuronal ensembles in the hippocampus that encode a specific physical environment, revealing a memory trace. In the cortex and other regions, the extent to which "Fos" induction during learning reveals specific sensory representations is unknown. Here we generate high-quality brain-wide…

Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex

PubMed Central

Kaufman, Matthew T; Churchland, Mark M; Ryu, Stephen I; Shenoy, Krishna V

2015-01-01

When choosing actions, we can act decisively, vacillate, or suffer momentary indecision. Studying how individual decisions unfold requires moment-by-moment readouts of brain state. Here we provide such a view from dorsal premotor and primary motor cortex. Two monkeys performed a novel decision task while we recorded from many neurons simultaneously. We found that a decoder trained using ‘forced choices’ (one target viable) was highly reliable when applied to ‘free choices’. However, during free choices internal events formed three categories. Typically, neural activity was consistent with rapid, unwavering choices. Sometimes, though, we observed presumed ‘changes of mind’: the neural state initially reflected one choice before changing to reflect the final choice. Finally, we observed momentary ‘indecision’: delay forming any clear motor plan. Further, moments of neural indecision accompanied moments of behavioral indecision. Together, these results reveal the rich and diverse set of internal events long suspected to occur during free choice. DOI: http://dx.doi.org/10.7554/eLife.04677.001 PMID:25942352

A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.

PubMed

Dethier, Julie; Nuyujukian, Paul; Eliasmith, Chris; Stewart, Terry; Elassaad, Shauki A; Shenoy, Krishna V; Boahen, Kwabena

2011-01-01

Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm's velocity and mapped on to the SNN using the Neural Engineering Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.

Mapping and Deciphering Neural Codes of NMDA Receptor-Dependent Fear Memory Engrams in the Hippocampus

PubMed Central

Tsien, Joe Z.

2013-01-01

Mapping and decoding brain activity patterns underlying learning and memory represents both great interest and immense challenge. At present, very little is known regarding many of the very basic questions regarding the neural codes of memory: are fear memories retrieved during the freezing state or non-freezing state of the animals? How do individual memory traces give arise to a holistic, real-time associative memory engram? How are memory codes regulated by synaptic plasticity? Here, by applying high-density electrode arrays and dimensionality-reduction decoding algorithms, we investigate hippocampal CA1 activity patterns of trace fear conditioning memory code in inducible NMDA receptor knockout mice and their control littermates. Our analyses showed that the conditioned tone (CS) and unconditioned foot-shock (US) can evoke hippocampal ensemble responses in control and mutant mice. Yet, temporal formats and contents of CA1 fear memory engrams differ significantly between the genotypes. The mutant mice with disabled NMDA receptor plasticity failed to generate CS-to-US or US-to-CS associative memory traces. Moreover, the mutant CA1 region lacked memory traces for “what at when” information that predicts the timing relationship between the conditioned tone and the foot shock. The degraded associative fear memory engram is further manifested in its lack of intertwined and alternating temporal association between CS and US memory traces that are characteristic to the holistic memory recall in the wild-type animals. Therefore, our study has decoded real-time memory contents, timing relationship between CS and US, and temporal organizing patterns of fear memory engrams and demonstrated how hippocampal memory codes are regulated by NMDA receptor synaptic plasticity. PMID:24302990

Decoding spatial and temporal features of neuronal cAMP/PKA signaling with FRET biosensors.

PubMed

Castro, Liliana R V; Guiot, Elvire; Polito, Marina; Paupardin-Tritsch, Daniéle; Vincent, Pierre

2014-02-01

Cyclic adenosine monophosphate (cAMP) and the cyclic-AMP-dependent protein kinase (PKA) regulate a plethora of cellular functions in virtually all eukaryotic cells. In neurons, the cAMP/PKA signaling cascade controls a number of biological properties such as axonal growth, pathfinding, efficacy of synaptic transmission, regulation of excitability, or long term changes. Genetically encoded optical biosensors for cAMP or PKA are considerably improving our understanding of these processes by providing a real-time measurement in living neurons. In this review, we describe the recent progress made in the creation of biosensors for cAMP or PKA activity. These biosensors revealed profound differences in the amplitude of the cAMP signal evoked by neuromodulators between various neuronal preparations. These responses can be resolved at the level of individual neurons, also revealing differences related to the neuronal type. At the sub-cellular level, biosensors reported different signal dynamics in domains like dendrites, cell body, nucleus, and axon. Combining this imaging approach with pharmacology or genetic models points at phosphodiesterases and phosphatases as critical regulatory proteins. Biosensor imaging will certainly emerge as a forefront tool to decipher the subtle mechanics of intracellular signaling. This will certainly help us to understand the mechanism of action of current drugs and foster the development of novel molecules for neuropsychiatric diseases. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Direct demonstration of persistent Na+ channel activity in dendritic processes of mammalian cortical neurones

PubMed Central

Magistretti, Jacopo; Ragsdale, David S; Alonso, Angel

1999-01-01

Single Na+ channel activity was recorded in patch-clamp, cell-attached experiments performed on dendritic processes of acutely isolated principal neurones from rat entorhinal-cortex layer II. The distances of the recording sites from the soma ranged from ≈20 to ≈100 μm.Step depolarisations from holding potentials of −120 to −100 mV to test potentials of −60 to +10 mV elicited Na+ channel openings in all of the recorded patches (n= 16).In 10 patches, besides transient Na+ channel openings clustered within the first few milliseconds of the depolarising pulses, prolonged and/or late Na+ channel openings were also regularly observed. This ‘persistent’ Na+ channel activity produced net inward, persistent currents in ensemble-average traces, and remained stable over the entire duration of the experiments (≈9 to 30 min).Two of these patches contained <= 3 channels. In these cases, persistent Na+ channel openings could be attributed to the activity of one single channel.The voltage dependence of persistent-current amplitude in ensemble-average traces closely resembled that of whole-cell, persistent Na+ current expressed by the same neurones, and displayed the same characteristic low threshold of activation.Dendritic, persistent Na+ channel openings had relatively high single-channel conductance (≈20 pS), similar to what is observed for somatic, persistent Na+ channels.We conclude that a stable, persistent Na+ channel activity is expressed by proximal dendrites of entorhinal-cortex layer II principal neurones, and can contribute a significant low-threshold, persistent Na+ current to the dendritic processing of excitatory synaptic inputs. PMID:10601494

Neural population-level memory traces in the mouse hippocampus.

PubMed

Chen, Guifen; Wang, L Phillip; Tsien, Joe Z

2009-12-16

One of the fundamental goals in neurosciences is to elucidate the formation and retrieval of brain's associative memory traces in real-time. Here, we describe real-time neural ensemble transient dynamics in the mouse hippocampal CA1 region and demonstrate their relationships with behavioral performances during both learning and recall. We employed the classic trace fear conditioning paradigm involving a neutral tone followed by a mild foot-shock 20 seconds later. Our large-scale recording and decoding methods revealed that conditioned tone responses and tone-shock association patterns were not present in CA1 during the first pairing, but emerged quickly after multiple pairings. These encoding patterns showed increased immediate-replay, correlating tightly with increased immediate-freezing during learning. Moreover, during contextual recall, these patterns reappeared in tandem six-to-fourteen times per minute, again correlating tightly with behavioral recall. Upon traced tone recall, while various fear memories were retrieved, the shock traces exhibited a unique recall-peak around the 20-second trace interval, further signifying the memory of time for the expected shock. Therefore, our study has revealed various real-time associative memory traces during learning and recall in CA1, and demonstrates that real-time memory traces can be decoded on a moment-to-moment basis over any single trial.

Interfacing to the brain’s motor decisions

PubMed Central

2017-01-01

It has been long known that neural activity, recorded with electrophysiological methods, contains rich information about a subject’s motor intentions, sensory experiences, allocation of attention, action planning, and even abstract thoughts. All these functions have been the subject of neurophysiological investigations, with the goal of understanding how neuronal activity represents behavioral parameters, sensory inputs, and cognitive functions. The field of brain-machine interfaces (BMIs) strives for a somewhat different goal: it endeavors to extract information from neural modulations to create a communication link between the brain and external devices. Although many remarkable successes have been already achieved in the BMI field, questions remain regarding the possibility of decoding high-order neural representations, such as decision making. Could BMIs be employed to decode the neural representations of decisions underlying goal-directed actions? In this review we lay out a framework that describes the computations underlying goal-directed actions as a multistep process performed by multiple cortical and subcortical areas. We then discuss how BMIs could connect to different decision-making steps and decode the neural processing ongoing before movements are initiated. Such decision-making BMIs could operate as a system with prediction that offers many advantages, such as shorter reaction time, better error processing, and improved unsupervised learning. To present the current state of the art, we review several recent BMIs incorporating decision-making components. PMID:28003406

Intensity invariance properties of auditory neurons compared to the statistics of relevant natural signals in grasshoppers.

PubMed

Clemens, Jan; Weschke, Gerroth; Vogel, Astrid; Ronacher, Bernhard

2010-04-01

The temporal pattern of amplitude modulations (AM) is often used to recognize acoustic objects. To identify objects reliably, intensity invariant representations have to be formed. We approached this problem within the auditory pathway of grasshoppers. We presented AM patterns modulated at different time scales and intensities. Metric space analysis of neuronal responses allowed us to determine how well, how invariantly, and at which time scales AM frequency is encoded. We find that in some neurons spike-count cues contribute substantially (20-60%) to the decoding of AM frequency at a single intensity. However, such cues are not robust when intensity varies. The general intensity invariance of the system is poor. However, there exists a range of AM frequencies around 83 Hz where intensity invariance of local interneurons is relatively high. In this range, natural communication signals exhibit much variation between species, suggesting an important behavioral role for this frequency band. We hypothesize, just as has been proposed for human speech, that the communication signals might have evolved to match the processing properties of the receivers. This contrasts with optimal coding theory, which postulates that neuronal systems are adapted to the statistics of the relevant signals.

The Molecular Basis of Memory

PubMed Central

2012-01-01

We propose a tripartite biochemical mechanism for memory. Three physiologic components are involved, namely, the neuron (individual and circuit), the surrounding neural extracellular matrix, and the various trace metals distributed within the matrix. The binding of a metal cation affects a corresponding nanostructure (shrinking, twisting, expansion) and dielectric sensibility of the chelating node (address) within the matrix lattice, sensed by the neuron. The neural extracellular matrix serves as an electro-elastic lattice, wherein neurons manipulate multiple trace metals (n > 10) to encode, store, and decode coginive information. The proposed mechanism explains brains low energy requirements and high rates of storage capacity described in multiples of Avogadro number (NA = 6 × 1023). Supportive evidence correlates memory loss to trace metal toxicity or deficiency, or breakdown in the delivery/transport of metals to the matrix, or its degradation. Inherited diseases revolving around dysfunctional trace metal metabolism and memory dysfunction, include Alzheimer's disease (Al, Zn, Fe), Wilson’s disease (Cu), thalassemia (Fe), and autism (metallothionein). The tripartite mechanism points to the electro-elastic interactions of neurons with trace metals distributed within the neural extracellular matrix, as the molecular underpinning of “synaptic plasticity” affecting short-term memory, long-term memory, and forgetting. PMID:23050060

Fast targeted gene transfection and optogenetic modification of single neurons using femtosecond laser irradiation

PubMed Central

Antkowiak, Maciej; Torres-Mapa, Maria Leilani; Witts, Emily C.; Miles, Gareth B.; Dholakia, Kishan; Gunn-Moore, Frank J.

2013-01-01

A prevailing problem in neuroscience is the fast and targeted delivery of DNA into selected neurons. The development of an appropriate methodology would enable the transfection of multiple genes into the same cell or different genes into different neighboring cells as well as rapid cell selective functionalization of neurons. Here, we show that optimized femtosecond optical transfection fulfills these requirements. We also demonstrate successful optical transfection of channelrhodopsin-2 in single selected neurons. We extend the functionality of this technique for wider uptake by neuroscientists by using fast three-dimensional laser beam steering enabling an image-guided “point-and-transfect” user-friendly transfection of selected cells. A sub-second transfection timescale per cell makes this method more rapid by at least two orders of magnitude when compared to alternative single-cell transfection techniques. This novel technology provides the ability to carry out large-scale cell selective genetic studies on neuronal ensembles and perform rapid genetic programming of neural circuits. PMID:24257461

Fast targeted gene transfection and optogenetic modification of single neurons using femtosecond laser irradiation.

PubMed

Antkowiak, Maciej; Torres-Mapa, Maria Leilani; Witts, Emily C; Miles, Gareth B; Dholakia, Kishan; Gunn-Moore, Frank J

2013-11-21

A prevailing problem in neuroscience is the fast and targeted delivery of DNA into selected neurons. The development of an appropriate methodology would enable the transfection of multiple genes into the same cell or different genes into different neighboring cells as well as rapid cell selective functionalization of neurons. Here, we show that optimized femtosecond optical transfection fulfills these requirements. We also demonstrate successful optical transfection of channelrhodopsin-2 in single selected neurons. We extend the functionality of this technique for wider uptake by neuroscientists by using fast three-dimensional laser beam steering enabling an image-guided "point-and-transfect" user-friendly transfection of selected cells. A sub-second transfection timescale per cell makes this method more rapid by at least two orders of magnitude when compared to alternative single-cell transfection techniques. This novel technology provides the ability to carry out large-scale cell selective genetic studies on neuronal ensembles and perform rapid genetic programming of neural circuits.

Experiments in clustered neuronal networks: A paradigm for complex modular dynamics

NASA Astrophysics Data System (ADS)

Teller, Sara; Soriano, Jordi

2016-06-01

Uncovering the interplay activity-connectivity is one of the major challenges in neuroscience. To deepen in the understanding of how a neuronal circuit shapes network dynamics, neuronal cultures have emerged as remarkable systems given their accessibility and easy manipulation. An attractive configuration of these in vitro systems consists in an ensemble of interconnected clusters of neurons. Using calcium fluorescence imaging to monitor spontaneous activity in these clustered neuronal networks, we were able to draw functional maps and reveal their topological features. We also observed that these networks exhibit a hierarchical modular dynamics, in which clusters fire in small groups that shape characteristic communities in the network. The structure and stability of these communities is sensitive to chemical or physical action, and therefore their analysis may serve as a proxy for network health. Indeed, the combination of all these approaches is helping to develop models to quantify damage upon network degradation, with promising applications for the study of neurological disorders in vitro.

Short-Term Depression, Temporal Summation, and Onset Inhibition Shape Interval Tuning in Midbrain Neurons

PubMed Central

Baker, Christa A.

2014-01-01

A variety of synaptic mechanisms can contribute to single-neuron selectivity for temporal intervals in sensory stimuli. However, it remains unknown how these mechanisms interact to establish single-neuron sensitivity to temporal patterns of sensory stimulation in vivo. Here we address this question in a circuit that allows us to control the precise temporal patterns of synaptic input to interval-tuned neurons in behaviorally relevant ways. We obtained in vivo intracellular recordings under multiple levels of current clamp from midbrain neurons in the mormyrid weakly electric fish Brienomyrus brachyistius during stimulation with electrosensory pulse trains. To reveal the excitatory and inhibitory inputs onto interval-tuned neurons, we then estimated the synaptic conductances underlying responses. We found short-term depression in excitatory and inhibitory pathways onto all interval-tuned neurons. Short-interval selectivity was associated with excitation that depressed less than inhibition at short intervals, as well as temporally summating excitation. Long-interval selectivity was associated with long-lasting onset inhibition. We investigated tuning after separately nullifying the contributions of temporal summation and depression, and found the greatest diversity of interval selectivity among neurons when both mechanisms were at play. Furthermore, eliminating the effects of depression decreased sensitivity to directional changes in interval. These findings demonstrate that variation in depression and summation of excitation and inhibition helps to establish tuning to behaviorally relevant intervals in communication signals, and that depression contributes to neural coding of interval sequences. This work reveals for the first time how the interplay between short-term plasticity and temporal summation mediates the decoding of temporal sequences in awake, behaving animals. PMID:25339741

Sensory Coding by Cerebellar Mossy Fibres through Inhibition-Driven Phase Resetting and Synchronisation

PubMed Central

Holtzman, Tahl; Jörntell, Henrik

2011-01-01

Temporal coding of spike-times using oscillatory mechanisms allied to spike-time dependent plasticity could represent a powerful mechanism for neuronal communication. However, it is unclear how temporal coding is constructed at the single neuronal level. Here we investigate a novel class of highly regular, metronome-like neurones in the rat brainstem which form a major source of cerebellar afferents. Stimulation of sensory inputs evoked brief periods of inhibition that interrupted the regular firing of these cells leading to phase-shifted spike-time advancements and delays. Alongside phase-shifting, metronome cells also behaved as band-pass filters during rhythmic sensory stimulation, with maximal spike-stimulus synchronisation at frequencies close to the idiosyncratic firing frequency of each neurone. Phase-shifting and band-pass filtering serve to temporally align ensembles of metronome cells, leading to sustained volleys of near-coincident spike-times, thereby transmitting synchronised sensory information to downstream targets in the cerebellar cortex. PMID:22046297

Stochastic inference with spiking neurons in the high-conductance state

NASA Astrophysics Data System (ADS)

Petrovici, Mihai A.; Bill, Johannes; Bytschok, Ilja; Schemmel, Johannes; Meier, Karlheinz

2016-10-01

The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we provide an analytical derivation of the neural activation function that holds for a large parameter space, including the high-conductance state. On this basis, we show how an ensemble of leaky integrate-and-fire neurons with conductance-based synapses embedded in a spiking environment can attain the correct firing statistics for sampling from a well-defined target distribution. For recurrent networks, we examine convergence toward stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This points to a new computational role of high-conductance states and establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level.

Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data

PubMed Central

Pnevmatikakis, Eftychios A.; Soudry, Daniel; Gao, Yuanjun; Machado, Timothy A.; Merel, Josh; Pfau, David; Reardon, Thomas; Mu, Yu; Lacefield, Clay; Yang, Weijian; Ahrens, Misha; Bruno, Randy; Jessell, Thomas M.; Peterka, Darcy S.; Yuste, Rafael; Paninski, Liam

2016-01-01

SUMMARY We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multineuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data. PMID:26774160

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

PubMed

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-12-24

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

Coding and Plasticity in the Mammalian Thermosensory System.

PubMed

Yarmolinsky, David A; Peng, Yueqing; Pogorzala, Leah A; Rutlin, Michael; Hoon, Mark A; Zuker, Charles S

2016-12-07

Perception of the thermal environment begins with the activation of peripheral thermosensory neurons innervating the body surface. To understand how temperature is represented in vivo, we used genetically encoded calcium indicators to measure temperature-evoked responses in hundreds of neurons across the trigeminal ganglion. Our results show how warm, hot, and cold stimuli are represented by distinct population responses, uncover unique functional classes of thermosensory neurons mediating heat and cold sensing, and reveal the molecular logic for peripheral warmth sensing. Next, we examined how the peripheral somatosensory system is functionally reorganized to produce altered perception of the thermal environment after injury. We identify fundamental transformations in sensory coding, including the silencing and recruitment of large ensembles of neurons, providing a cellular basis for perceptual changes in temperature sensing, including heat hypersensitivity, persistence of heat perception, cold hyperalgesia, and cold analgesia. Copyright © 2016 Elsevier Inc. All rights reserved.

Slow dynamics and regularization phenomena in ensembles of chaotic neurons

NASA Astrophysics Data System (ADS)

Rabinovich, M. I.; Varona, P.; Torres, J. J.; Huerta, R.; Abarbanel, H. D. I.

1999-02-01

We have explored the role of calcium concentration dynamics in the generation of chaos and in the regularization of the bursting oscillations using a minimal neural circuit of two coupled model neurons. In regions of the control parameter space where the slowest component, namely the calcium concentration in the endoplasmic reticulum, weakly depends on the other variables, this model is analogous to three dimensional systems as found in [1] or [2]. These are minimal models that describe the fundamental characteristics of the chaotic spiking-bursting behavior observed in real neurons. We have investigated different regimes of cooperative behavior in large assemblies of such units using lattice of non-identical Hindmarsh-Rose neurons electrically coupled with parameters chosen randomly inside the chaotic region. We study the regularization mechanisms in large assemblies and the development of several spatio-temporal patterns as a function of the interconnectivity among nearest neighbors.

VTA neurons coordinate with the hippocampal reactivation of spatial experience

PubMed Central

Gomperts, Stephen N; Kloosterman, Fabian; Wilson, Matthew A

2015-01-01

Spatial learning requires the hippocampus, and the replay of spatial sequences during hippocampal sharp wave-ripple (SPW-R) events of quiet wakefulness and sleep is believed to play a crucial role. To test whether the coordination of VTA reward prediction error signals with these replayed spatial sequences could contribute to this process, we recorded from neuronal ensembles of the hippocampus and VTA as rats performed appetitive spatial tasks and subsequently slept. We found that many reward responsive (RR) VTA neurons coordinated with quiet wakefulness-associated hippocampal SPW-R events that replayed recent experience. In contrast, coordination between RR neurons and SPW-R events in subsequent slow wave sleep was diminished. Together, these results indicate distinct contributions of VTA reinforcement activity associated with hippocampal spatial replay to the processing of wake and SWS-associated spatial memory. DOI: http://dx.doi.org/10.7554/eLife.05360.001 PMID:26465113

Connecting a Connectome to Behavior: An Ensemble of Neuroanatomical Models of C. elegans Klinotaxis

PubMed Central

Izquierdo, Eduardo J.; Beer, Randall D.

2013-01-01

Increased efforts in the assembly and analysis of connectome data are providing new insights into the principles underlying the connectivity of neural circuits. However, despite these considerable advances in connectomics, neuroanatomical data must be integrated with neurophysiological and behavioral data in order to obtain a complete picture of neural function. Due to its nearly complete wiring diagram and large behavioral repertoire, the nematode worm Caenorhaditis elegans is an ideal organism in which to explore in detail this link between neural connectivity and behavior. In this paper, we develop a neuroanatomically-grounded model of salt klinotaxis, a form of chemotaxis in which changes in orientation are directed towards the source through gradual continual adjustments. We identify a minimal klinotaxis circuit by systematically searching the C. elegans connectome for pathways linking chemosensory neurons to neck motor neurons, and prune the resulting network based on both experimental considerations and several simplifying assumptions. We then use an evolutionary algorithm to find possible values for the unknown electrophsyiological parameters in the network such that the behavioral performance of the entire model is optimized to match that of the animal. Multiple runs of the evolutionary algorithm produce an ensemble of such models. We analyze in some detail the mechanisms by which one of the best evolved circuits operates and characterize the similarities and differences between this mechanism and other solutions in the ensemble. Finally, we propose a series of experiments to determine which of these alternatives the worm may be using. PMID:23408877

The application of the multi-alternative approach in active neural network models

NASA Astrophysics Data System (ADS)

Podvalny, S.; Vasiljev, E.

2017-02-01

The article refers to the construction of intelligent systems based artificial neuron networks are used. We discuss the basic properties of the non-compliance of artificial neuron networks and their biological prototypes. It is shown here that the main reason for these discrepancies is the structural immutability of the neuron network models in the learning process, that is, their passivity. Based on the modern understanding of the biological nervous system as a structured ensemble of nerve cells, it is proposed to abandon the attempts to simulate its work at the level of the elementary neurons functioning processes and proceed to the reproduction of the information structure of data storage and processing on the basis of the general enough evolutionary principles of multialternativity, i.e. the multi-level structural model, diversity and modularity. The implementation method of these principles is offered, using the faceted memory organization in the neuron network with the rearranging active structure. An example of the implementation of the active facet-type neuron network in the intellectual decision-making system in the conditions of critical events development in the electrical distribution system.

Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling.

PubMed

de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A; Ayali, Amir; Anava, Sarit; Sánchez-Ávila, Carmen; Boccaletti, Stefano

2015-06-01

Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth. © 2014 International Society for Advancement of Cytometry.

A distinctive subpopulation of medial septal slow-firing neurons promote hippocampal activation and theta oscillations

PubMed Central

Lin, Shih-Chieh; Nicolelis, Miguel A. L.

2011-01-01

The medial septum-vertical limb of the diagonal band of Broca (MSvDB) is important for normal hippocampal functions and theta oscillations. Although many previous studies have focused on understanding how MSVDB neurons fire rhythmic bursts to pace hippocampal theta oscillations, a significant portion of MSVDB neurons are slow-firing and thus do not pace theta oscillations. The function of these MSVDB neurons, especially their role in modulating hippocampal activity, remains unknown. We recorded MSVDB neuronal ensembles in behaving rats, and identified a distinct physiologically homogeneous subpopulation of slow-firing neurons (overall firing <4 Hz) that shared three features: 1) much higher firing rate during rapid eye movement sleep than during slow-wave (SW) sleep; 2) temporary activation associated with transient arousals during SW sleep; 3) brief responses (latency 15∼30 ms) to auditory stimuli. Analysis of the fine temporal relationship of their spiking and theta oscillations showed that unlike the theta-pacing neurons, the firing of these “pro-arousal” neurons follows theta oscillations. However, their activity precedes short-term increases in hippocampal oscillation power in the theta and gamma range lasting for a few seconds. Together, these results suggest that these pro-arousal slow-firing MSvDB neurons may function collectively to promote hippocampal activation. PMID:21865435

Spike sorting of synchronous spikes from local neuron ensembles

PubMed Central

Pröpper, Robert; Alle, Henrik; Meier, Philipp; Geiger, Jörg R. P.; Obermayer, Klaus; Munk, Matthias H. J.

2015-01-01

Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons. PMID:26289473

Intra-day signal instabilities affect decoding performance in an intracortical neural interface system

PubMed Central

Perge, János A.; Homer, Mark L.; Malik, Wasim Q.; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P.; Hochberg, Leigh R.

2013-01-01

Objective Motor Neural Interface Systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. Approach To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). Main results Eighty-four percent of the recorded units showed a statistically significant change in apparent firing rate (3.8±8.71Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and seventy-four percent of the units showed a significant change in spike amplitude (3.7±6.5μV or 5.5% of mean spike amplitude). Forty percent of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional “bias” in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in fifty-six percent of all performance assessments in participant cursor control (n=2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions. Significance We anticipate that signal acquisition and decoding methods that can adapt to the reported instabilities will further improve the performance of intracortically-based NISs. PMID:23574741

Numerical analysis of the chimera states in the multilayered network model

NASA Astrophysics Data System (ADS)

Goremyko, Mikhail V.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Ghosh, Dibakar; Bera, Bidesh K.; Dana, Syamal K.; Hramov, Alexander E.

2017-03-01

We numerically study the interaction between the ensembles of the Hindmarsh-Rose (HR) neuron systems, arranged in the multilayer network model. We have shown that the fully identical layers, demonstrated individually different chimera due to the initial mismatch, come to the identical chimera state with the increase of inter-layer coupling. Within the multilayer model we also consider the case, when the one layer demonstrates chimera state, while another layer exhibits coherent or incoherent dynamics. It has been shown that the interactions chimera-coherent state and chimera-incoherent state leads to the both excitation of chimera as from the ensemble of fully coherent or incoherent oscillators, and suppression of initially stable chimera state

The Influence of Neuronal Density and Maturation on Network Activity of Hippocampal Cell Cultures: A Methodological Study

PubMed Central

Menegon, Andrea; Ferrigno, Giancarlo; Pedrocchi, Alessandra

2013-01-01

It is known that cell density influences the maturation process of in vitro neuronal networks. Neuronal cultures plated with different cell densities differ in number of synapses per neuron and thus in single neuron synaptic transmission, which results in a density-dependent neuronal network activity. Although many authors provided detailed information about the effects of cell density on neuronal culture activity, a dedicated report of density and age influence on neuronal hippocampal culture activity has not yet been reported. Therefore, this work aims at providing reference data to researchers that set up an experimental study on hippocampal neuronal cultures, helping in planning and decoding the experiments. In this work, we analysed the effects of both neuronal density and culture age on functional attributes of maturing hippocampal cultures. We characterized the electrophysiological activity of neuronal cultures seeded at three different cell densities, recording their spontaneous electrical activity over maturation by means of MicroElectrode Arrays (MEAs). We had gather data from 86 independent hippocampal cultures to achieve solid statistic results, considering the high culture-to-culture variability. Network activity was evaluated in terms of simple spiking, burst and network burst features. We observed that electrical descriptors were characterized by a functional peak during maturation, followed by a stable phase (for sparse and medium density cultures) or by a decrease phase (for high dense neuronal cultures). Moreover, 900 cells/mm2 cultures showed characteristics suitable for long lasting experiments (e.g. chronic effect of drug treatments) while 1800 cells/mm2 cultures should be preferred for experiments that require intense electrical activity (e.g. to evaluate the effect of inhibitory molecules). Finally, cell cultures at 3600 cells/mm2 are more appropriate for experiments in which time saving is relevant (e.g. drug screenings). These results are intended to be a reference for the planning of in vitro neurophysiological and neuropharmacological experiments with MEAs. PMID:24386305

The influence of neuronal density and maturation on network activity of hippocampal cell cultures: a methodological study.

PubMed

Biffi, Emilia; Regalia, Giulia; Menegon, Andrea; Ferrigno, Giancarlo; Pedrocchi, Alessandra

2013-01-01

It is known that cell density influences the maturation process of in vitro neuronal networks. Neuronal cultures plated with different cell densities differ in number of synapses per neuron and thus in single neuron synaptic transmission, which results in a density-dependent neuronal network activity. Although many authors provided detailed information about the effects of cell density on neuronal culture activity, a dedicated report of density and age influence on neuronal hippocampal culture activity has not yet been reported. Therefore, this work aims at providing reference data to researchers that set up an experimental study on hippocampal neuronal cultures, helping in planning and decoding the experiments. In this work, we analysed the effects of both neuronal density and culture age on functional attributes of maturing hippocampal cultures. We characterized the electrophysiological activity of neuronal cultures seeded at three different cell densities, recording their spontaneous electrical activity over maturation by means of MicroElectrode Arrays (MEAs). We had gather data from 86 independent hippocampal cultures to achieve solid statistic results, considering the high culture-to-culture variability. Network activity was evaluated in terms of simple spiking, burst and network burst features. We observed that electrical descriptors were characterized by a functional peak during maturation, followed by a stable phase (for sparse and medium density cultures) or by a decrease phase (for high dense neuronal cultures). Moreover, 900 cells/mm(2) cultures showed characteristics suitable for long lasting experiments (e.g. chronic effect of drug treatments) while 1800 cells/mm(2) cultures should be preferred for experiments that require intense electrical activity (e.g. to evaluate the effect of inhibitory molecules). Finally, cell cultures at 3600 cells/mm(2) are more appropriate for experiments in which time saving is relevant (e.g. drug screenings). These results are intended to be a reference for the planning of in vitro neurophysiological and neuropharmacological experiments with MEAs.

Ripple-Triggered Stimulation of the Locus Coeruleus during Post-Learning Sleep Disrupts Ripple/Spindle Coupling and Impairs Memory Consolidation

ERIC Educational Resources Information Center

Novitskaya, Yulia; Sara, Susan J.; Logothetis, Nikos K.; Eschenko, Oxana

2016-01-01

Experience-induced replay of neuronal ensembles occurs during hippocampal high-frequency oscillations, or ripples. Post-learning increase in ripple rate is predictive of memory recall, while ripple disruption impairs learning. Ripples may thus present a fundamental component of a neurophysiological mechanism of memory consolidation. In addition to…

A temperature rise reduces trial-to-trial variability of locust auditory neuron responses.

PubMed

Eberhard, Monika J B; Schleimer, Jan-Hendrik; Schreiber, Susanne; Ronacher, Bernhard

2015-09-01

The neurophysiology of ectothermic animals, such as insects, is affected by environmental temperature, as their body temperature fluctuates with ambient conditions. Changes in temperature alter properties of neurons and, consequently, have an impact on the processing of information. Nevertheless, nervous system function is often maintained over a broad temperature range, exhibiting a surprising robustness to variations in temperature. A special problem arises for acoustically communicating insects, as in these animals mate recognition and mate localization typically rely on the decoding of fast amplitude modulations in calling and courtship songs. In the auditory periphery, however, temporal resolution is constrained by intrinsic neuronal noise. Such noise predominantly arises from the stochasticity of ion channel gating and potentially impairs the processing of sensory signals. On the basis of intracellular recordings of locust auditory neurons, we show that intrinsic neuronal variability on the level of spikes is reduced with increasing temperature. We use a detailed mathematical model including stochastic ion channel gating to shed light on the underlying biophysical mechanisms in auditory receptor neurons: because of a redistribution of channel-induced current noise toward higher frequencies and specifics of the temperature dependence of the membrane impedance, membrane potential noise is indeed reduced at higher temperatures. This finding holds under generic conditions and physiologically plausible assumptions on the temperature dependence of the channels' kinetics and peak conductances. We demonstrate that the identified mechanism also can explain the experimentally observed reduction of spike timing variability at higher temperatures. Copyright © 2015 the American Physiological Society.

A temperature rise reduces trial-to-trial variability of locust auditory neuron responses

PubMed Central

Schleimer, Jan-Hendrik; Schreiber, Susanne; Ronacher, Bernhard

2015-01-01

The neurophysiology of ectothermic animals, such as insects, is affected by environmental temperature, as their body temperature fluctuates with ambient conditions. Changes in temperature alter properties of neurons and, consequently, have an impact on the processing of information. Nevertheless, nervous system function is often maintained over a broad temperature range, exhibiting a surprising robustness to variations in temperature. A special problem arises for acoustically communicating insects, as in these animals mate recognition and mate localization typically rely on the decoding of fast amplitude modulations in calling and courtship songs. In the auditory periphery, however, temporal resolution is constrained by intrinsic neuronal noise. Such noise predominantly arises from the stochasticity of ion channel gating and potentially impairs the processing of sensory signals. On the basis of intracellular recordings of locust auditory neurons, we show that intrinsic neuronal variability on the level of spikes is reduced with increasing temperature. We use a detailed mathematical model including stochastic ion channel gating to shed light on the underlying biophysical mechanisms in auditory receptor neurons: because of a redistribution of channel-induced current noise toward higher frequencies and specifics of the temperature dependence of the membrane impedance, membrane potential noise is indeed reduced at higher temperatures. This finding holds under generic conditions and physiologically plausible assumptions on the temperature dependence of the channels' kinetics and peak conductances. We demonstrate that the identified mechanism also can explain the experimentally observed reduction of spike timing variability at higher temperatures. PMID:26041833

Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

PubMed Central

Novellino, A.; D'Angelo, P.; Cozzi, L.; Chiappalone, M.; Sanguineti, V.; Martinoia, S.

2007-01-01

One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason “embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses. PMID:18350128

Distribution of glutamatergic, GABAergic, and glycinergic neurons in the auditory pathways of macaque monkeys.

PubMed

Ito, T; Inoue, K; Takada, M

2015-12-03

Macaque monkeys use complex communication calls and are regarded as a model for studying the coding and decoding of complex sound in the auditory system. However, little is known about the distribution of excitatory and inhibitory neurons in the auditory system of macaque monkeys. In this study, we examined the overall distribution of cell bodies that expressed mRNAs for VGLUT1, and VGLUT2 (markers for glutamatergic neurons), GAD67 (a marker for GABAergic neurons), and GLYT2 (a marker for glycinergic neurons) in the auditory system of the Japanese macaque. In addition, we performed immunohistochemistry for VGLUT1, VGLUT2, and GAD67 in order to compare the distribution of proteins and mRNAs. We found that most of the excitatory neurons in the auditory brainstem expressed VGLUT2. In contrast, the expression of VGLUT1 mRNA was restricted to the auditory cortex (AC), periolivary nuclei, and cochlear nuclei (CN). The co-expression of GAD67 and GLYT2 mRNAs was common in the ventral nucleus of the lateral lemniscus (VNLL), CN, and superior olivary complex except for the medial nucleus of the trapezoid body, which expressed GLYT2 alone. In contrast, the dorsal nucleus of the lateral lemniscus, inferior colliculus, thalamus, and AC expressed GAD67 alone. The absence of co-expression of VGLUT1 and VGLUT2 in the medial geniculate, medial superior olive, and VNLL suggests that synaptic responses in the target neurons of these nuclei may be different between rodents and macaque monkeys. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Decoding the language of epigenetics during neural development is key for understanding development as well as developmental neurotoxicity.

PubMed

Yeo, Michele; Patisaul, Heather; Liedtke, Wolfgang

2013-11-01

Neural development is a delicate process that can be disrupted by pollution that exerts detrimental impact on neural signaling. This commentary highlights recent discoveries in the arena of research at the interface of environmental toxicology and developmental neuroscience relating to toxicity mechanisms of bisphenol A (BPA), a ubiquitous chemical used in manufacturing of plastics and epoxy resins that is known to bind to and interfere with estrogen receptors, estrogen-receptor-related receptors and other receptors for gonadal steroids. It was recently observed that BPA disrupts the perinatal chloride shift, a key neurodevelopmental mechanism that brings down neuronal chloride from ~100 mM to ~20 mM within weeks. The chloride shift happens in all central nervous systems of vertebrates around parturition. High neuronal chloride supports neuron precursors' migrations, low neuronal chloride is the prerequisite for inhibitory action of neurotransmitters GABA and glycine, and thus an absolute requisite for normal functioning of the mature CNS. One critical contributor to the neuronal chloride shift is the concomitant upregulation of expression of the chloride-extruding transporter molecule, KCC2. We highlight recent findings including our discovery that BPA disrupts the chloride shift in a sex-specific manner by recruiting epigenetics mechanisms. These could be relevant for childhood neuropsychiatric disorders as well as for liability to develop chronic neuropsychiatric diseases later in life.

Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making

PubMed Central

Seamans, Jeremy K.; Durstewitz, Daniel

2011-01-01

A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states. PMID:21625577

PMv Neuronal Firing May Be Driven by a Movement Command Trajectory within Multidimensional Gaussian Fields.

PubMed

Agarwal, Rahul; Thakor, Nitish V; Sarma, Sridevi V; Massaquoi, Steve G

2015-06-24

The premotor cortex (PM) is known to be a site of visuo-somatosensory integration for the production of movement. We sought to better understand the ventral PM (PMv) by modeling its signal encoding in greater detail. Neuronal firing data was obtained from 110 PMv neurons in two male rhesus macaques executing four reach-grasp-manipulate tasks. We found that in the large majority of neurons (∼90%) the firing patterns across the four tasks could be explained by assuming that a high-dimensional position/configuration trajectory-like signal evolving ∼250 ms before movement was encoded within a multidimensional Gaussian field (MGF). Our findings are consistent with the possibility that PMv neurons process a visually specified reference command for the intended arm/hand position trajectory with respect to a proprioceptively or visually sensed initial configuration. The estimated MGF were (hyper) disc-like, such that each neuron's firing modulated strongly only with commands that evolved along a single direction within position/configuration space. Thus, many neurons appeared to be tuned to slices of this input signal space that as a collection appeared to well cover the space. The MGF encoding models appear to be consistent with the arm-referent, bell-shaped, visual target tuning curves and target selectivity patterns observed in PMV visual-motor neurons. These findings suggest that PMv may implement a lookup table-like mechanism that helps translate intended movement trajectory into time-varying patterns of activation in motor cortex and spinal cord. MGFs provide an improved nonlinear framework for potentially decoding visually specified, intended multijoint arm/hand trajectories well in advance of movement. Copyright © 2015 the authors 0270-6474/15/359508-18$15.00/0.

Neuronal Assemblies Evidence Distributed Interactions within a Tactile Discrimination Task in Rats

PubMed Central

Deolindo, Camila S.; Kunicki, Ana C. B.; da Silva, Maria I.; Lima Brasil, Fabrício; Moioli, Renan C.

2018-01-01

Accumulating evidence suggests that neural interactions are distributed and relate to animal behavior, but many open questions remain. The neural assembly hypothesis, formulated by Hebb, states that synchronously active single neurons may transiently organize into functional neural circuits—neuronal assemblies (NAs)—and that would constitute the fundamental unit of information processing in the brain. However, the formation, vanishing, and temporal evolution of NAs are not fully understood. In particular, characterizing NAs in multiple brain regions over the course of behavioral tasks is relevant to assess the highly distributed nature of brain processing. In the context of NA characterization, active tactile discrimination tasks with rats are elucidative because they engage several cortical areas in the processing of information that are otherwise masked in passive or anesthetized scenarios. In this work, we investigate the dynamic formation of NAs within and among four different cortical regions in long-range fronto-parieto-occipital networks (primary somatosensory, primary visual, prefrontal, and posterior parietal cortices), simultaneously recorded from seven rats engaged in an active tactile discrimination task. Our results first confirm that task-related neuronal firing rate dynamics in all four regions is significantly modulated. Notably, a support vector machine decoder reveals that neural populations contain more information about the tactile stimulus than the majority of single neurons alone. Then, over the course of the task, we identify the emergence and vanishing of NAs whose participating neurons are shown to contain more information about animal behavior than randomly chosen neurons. Taken together, our results further support the role of multiple and distributed neurons as the functional unit of information processing in the brain (NA hypothesis) and their link to active animal behavior. PMID:29375324

Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

PubMed Central

Pohlmeyer, Eric A.; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W.; Sanchez, Justin C.

2014-01-01

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

Entorhinal cortex receptive fields are modulated by spatial attention, even without movement

PubMed Central

König, Peter; König, Seth; Buffalo, Elizabeth A

2018-01-01

Grid cells in the entorhinal cortex allow for the precise decoding of position in space. Along with potentially playing an important role in navigation, grid cells have recently been hypothesized to make a general contribution to mental operations. A prerequisite for this hypothesis is that grid cell activity does not critically depend on physical movement. Here, we show that movement of covert attention, without any physical movement, also elicits spatial receptive fields with a triangular tiling of space. In monkeys trained to maintain central fixation while covertly attending to a stimulus moving in the periphery we identified a significant population (20/141, 14% neurons at a FDR <5%) of entorhinal cells with spatially structured receptive fields. This contrasts with recordings obtained in the hippocampus, where grid-like representations were not observed. Our results provide evidence that neurons in macaque entorhinal cortex do not rely on physical movement. PMID:29537964

Fractional Gaussian noise-enhanced information capacity of a nonlinear neuron model with binary signal input

NASA Astrophysics Data System (ADS)

Gao, Feng-Yin; Kang, Yan-Mei; Chen, Xi; Chen, Guanrong

2018-05-01

This paper reveals the effect of fractional Gaussian noise with Hurst exponent H ∈(1 /2 ,1 ) on the information capacity of a general nonlinear neuron model with binary signal input. The fGn and its corresponding fractional Brownian motion exhibit long-range, strong-dependent increments. It extends standard Brownian motion to many types of fractional processes found in nature, such as the synaptic noise. In the paper, for the subthreshold binary signal, sufficient conditions are given based on the "forbidden interval" theorem to guarantee the occurrence of stochastic resonance, while for the suprathreshold binary signal, the simulated results show that additive fGn with Hurst exponent H ∈(1 /2 ,1 ) could increase the mutual information or bits count. The investigation indicated that the synaptic noise with the characters of long-range dependence and self-similarity might be the driving factor for the efficient encoding and decoding of the nervous system.

Decoding spike timing: the differential reverse correlation method

PubMed Central

Tkačik, Gašper; Magnasco, Marcelo O.

2009-01-01

It is widely acknowledged that detailed timing of action potentials is used to encode information, for example in auditory pathways; however the computational tools required to analyze encoding through timing are still in their infancy. We present a simple example of encoding, based on a recent model of time-frequency analysis, in which units fire action potentials when a certain condition is met, but the timing of the action potential depends also on other features of the stimulus. We show that, as a result, spike-triggered averages are smoothed so much they do not represent the true features of the encoding. Inspired by this example, we present a simple method, differential reverse correlations, that can separate an analysis of what causes a neuron to spike, and what controls its timing. We analyze with this method the leaky integrate-and-fire neuron and show the method accurately reconstructs the model's kernel. PMID:18597928

Non-overlapping Neural Networks in Hydra vulgaris.

PubMed

Dupre, Christophe; Yuste, Rafael

2017-04-24

To understand the emergent properties of neural circuits, it would be ideal to record the activity of every neuron in a behaving animal and decode how it relates to behavior. We have achieved this with the cnidarian Hydra vulgaris, using calcium imaging of genetically engineered animals to measure the activity of essentially all of its neurons. Although the nervous system of Hydra is traditionally described as a simple nerve net, we surprisingly find instead a series of functional networks that are anatomically non-overlapping and are associated with specific behaviors. Three major functional networks extend through the entire animal and are activated selectively during longitudinal contractions, elongations in response to light, and radial contractions, whereas an additional network is located near the hypostome and is active during nodding. These results demonstrate the functional sophistication of apparently simple nerve nets, and the potential of Hydra and other basal metazoans as a model system for neural circuit studies. Published by Elsevier Ltd.

A method for decoding the neurophysiological spike-response transform.

PubMed

Stern, Estee; García-Crescioni, Keyla; Miller, Mark W; Peskin, Charles S; Brezina, Vladimir

2009-11-15

Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.

Decoding and reconstructing color from responses in human visual cortex.

PubMed

Brouwer, Gijs Joost; Heeger, David J

2009-11-04

How is color represented by spatially distributed patterns of activity in visual cortex? Functional magnetic resonance imaging responses to several stimulus colors were analyzed with multivariate techniques: conventional pattern classification, a forward model of idealized color tuning, and principal component analysis (PCA). Stimulus color was accurately decoded from activity in V1, V2, V3, V4, and VO1 but not LO1, LO2, V3A/B, or MT+. The conventional classifier and forward model yielded similar accuracies, but the forward model (unlike the classifier) also reliably reconstructed novel stimulus colors not used to train (specify parameters of) the model. The mean responses, averaged across voxels in each visual area, were not reliably distinguishable for the different stimulus colors. Hence, each stimulus color was associated with a unique spatially distributed pattern of activity, presumably reflecting the color selectivity of cortical neurons. Using PCA, a color space was derived from the covariation, across voxels, in the responses to different colors. In V4 and VO1, the first two principal component scores (main source of variation) of the responses revealed a progression through perceptual color space, with perceptually similar colors evoking the most similar responses. This was not the case for any of the other visual cortical areas, including V1, although decoding was most accurate in V1. This dissociation implies a transformation from the color representation in V1 to reflect perceptual color space in V4 and VO1.

NMDA receptor gating of information flow through the striatum in vivo.

PubMed

Pomata, Pablo E; Belluscio, Mariano A; Riquelme, Luis A; Murer, M Gustavo

2008-12-10

A role of NMDA receptors in corticostriatal synaptic plasticity is widely acknowledged. However, the conditions that allow NMDA receptor activation in the striatum in vivo remain obscure. Here we show that NMDA receptors contribute to sustain the membrane potential of striatal medium spiny projection neurons close to threshold during spontaneous UP states in vivo. Moreover, we found that the blockade of striatal NMDA receptors reduces markedly the spontaneous firing of ensembles of medium spiny neurons during slow waves in urethane-anesthetized rats. We speculate that recurrent activation of NMDA receptors during UP states allows off-line information flow through the striatum and system level consolidation during habit formation.

Toward a Neurocentric View of Learning.

PubMed

Titley, Heather K; Brunel, Nicolas; Hansel, Christian

2017-07-05

Synaptic plasticity (e.g., long-term potentiation [LTP]) is considered the cellular correlate of learning. Recent optogenetic studies on memory engram formation assign a critical role in learning to suprathreshold activation of neurons and their integration into active engrams ("engram cells"). Here we review evidence that ensemble integration may result from LTP but also from cell-autonomous changes in membrane excitability. We propose that synaptic plasticity determines synaptic connectivity maps, whereas intrinsic plasticity-possibly separated in time-amplifies neuronal responsiveness and acutely drives engram integration. Our proposal marks a move away from an exclusively synaptocentric toward a non-exclusive, neurocentric view of learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Decoding natural images from evoked brain activities using encoding models with invertible mapping.

PubMed

Li, Chao; Xu, Junhai; Liu, Baolin

2018-05-21

Recent studies have built encoding models in the early visual cortex, and reliable mappings have been made between the low-level visual features of stimuli and brain activities. However, these mappings are irreversible, so that the features cannot be directly decoded. To solve this problem, we designed a sparse framework-based encoding model that predicted brain activities from a complete feature representation. Moreover, according to the distribution and activation rules of neurons in the primary visual cortex (V1), three key transformations were introduced into the basic feature to improve the model performance. In this setting, the mapping was simple enough that it could be inverted using a closed-form formula. Using this mapping, we designed a hybrid identification method based on the support vector machine (SVM), and tested it on a published functional magnetic resonance imaging (fMRI) dataset. The experiments confirmed the rationality of our encoding model, and the identification accuracies for 2 subjects increased from 92% and 72% to 98% and 92% with the chance level only 0.8%. Copyright © 2018 Elsevier Ltd. All rights reserved.

Translocation of CaMKII to dendritic microtubules supports the plasticity of local synapses

PubMed Central

Lemieux, Mado; Labrecque, Simon; Tardif, Christian; Labrie-Dion, Étienne; LeBel, Éric

2012-01-01

The processing of excitatory synaptic inputs involves compartmentalized dendritic Ca2+ oscillations. The downstream signaling evoked by these local Ca2+ transients and their impact on local synaptic development and remodeling are unknown. Ca2+/calmodulin-dependent protein kinase II (CaMKII) is an important decoder of Ca2+ signals and mediator of synaptic plasticity. In addition to its known accumulation at spines, we observed with live imaging the dynamic recruitment of CaMKII to dendritic subdomains adjacent to activated synapses in cultured hippocampal neurons. This localized and transient enrichment of CaMKII to dendritic sites coincided spatially and temporally with dendritic Ca2+ transients. We show that it involved an interaction with microtubular elements, required activation of the kinase, and led to localized dendritic CaMKII autophosphorylation. This process was accompanied by the adjacent remodeling of spines and synaptic AMPA receptor insertion. Replacement of endogenous CaMKII with a mutant that cannot translocate within dendrites lessened this activity-dependent synaptic plasticity. Thus, CaMKII could decode compartmental dendritic Ca2+ transients to support remodeling of local synapses. PMID:22965911

Multiunit Activity-Based Real-Time Limb-State Estimation from Dorsal Root Ganglion Recordings

PubMed Central

Han, Sungmin; Chu, Jun-Uk; Kim, Hyungmin; Park, Jong Woong; Youn, Inchan

2017-01-01

Proprioceptive afferent activities could be useful for providing sensory feedback signals for closed-loop control during functional electrical stimulation (FES). However, most previous studies have used the single-unit activity of individual neurons to extract sensory information from proprioceptive afferents. This study proposes a new decoding method to estimate ankle and knee joint angles using multiunit activity data. Proprioceptive afferent signals were recorded from a dorsal root ganglion with a single-shank microelectrode during passive movements of the ankle and knee joints, and joint angles were measured as kinematic data. The mean absolute value (MAV) was extracted from the multiunit activity data, and a dynamically driven recurrent neural network (DDRNN) was used to estimate ankle and knee joint angles. The multiunit activity-based MAV feature was sufficiently informative to estimate limb states, and the DDRNN showed a better decoding performance than conventional linear estimators. In addition, processing time delay satisfied real-time constraints. These results demonstrated that the proposed method could be applicable for providing real-time sensory feedback signals in closed-loop FES systems. PMID:28276474

Dorsal motor nucleus of the vagus neurons: a multivariate taxonomy.

PubMed

Jarvinen, M K; Powley, T L

1999-01-18

The dorsal motor nucleus of the vagus (DMNX) contains neurons with different projections and discrete functions, but little success has been achieved in distinguishing the cells cytoarchitectonically. The present experiment employed multivariate analytical techniques to evaluate DMNX neuronal morphology. Male Sprague-Dawley rats (n = 77) were perfused, and the brainstems were stained en bloc with a Golgi-Cox protocol. DMNX neurons in each of three planes (coronal, sagittal, and horizontal; total sample = 607) were digitized. Three-dimensional features quantified included dendritic length, number of segments, spine density, number of primary dendrites, dendritic orientation, and soma form factor. Cluster analyses of six independent samples of 100+ neurons and of three composite replicate pools of 200+ neurons consistently identified similar sets of four distinct neuronal profiles. One profile (spinous, limited dendrites, small somata) appears to correspond to the interneuron population of the DMNX. In contrast, the other three distinctive profiles (e.g., one is multipolar, with large dendritic fields and large somata) are different types of preganglionic neurons. Each of the four types of neurons is found throughout the DMNX, suggesting that the individual columnar subnuclei and other postulated vagal motorneuron pools are composed of all types of neurons. Within individual motor pools, ensembles of the different neuronal types must cooperatively organize different functions and project to different effectors within a target organ. By extension, specializations of the preganglionic motor pools are more likely to result from their afferent inputs, peripheral target tissues, neurochemistry, or physiological features rather than from any unique morphological profiles.

Efficient digital implementation of a conductance-based globus pallidus neuron and the dynamics analysis

NASA Astrophysics Data System (ADS)

Yang, Shuangming; Wei, Xile; Deng, Bin; Liu, Chen; Li, Huiyan; Wang, Jiang

2018-03-01

Balance between biological plausibility of dynamical activities and computational efficiency is one of challenging problems in computational neuroscience and neural system engineering. This paper proposes a set of efficient methods for the hardware realization of the conductance-based neuron model with relevant dynamics, targeting reproducing the biological behaviors with low-cost implementation on digital programmable platform, which can be applied in wide range of conductance-based neuron models. Modified GP neuron models for efficient hardware implementation are presented to reproduce reliable pallidal dynamics, which decode the information of basal ganglia and regulate the movement disorder related voluntary activities. Implementation results on a field-programmable gate array (FPGA) demonstrate that the proposed techniques and models can reduce the resource cost significantly and reproduce the biological dynamics accurately. Besides, the biological behaviors with weak network coupling are explored on the proposed platform, and theoretical analysis is also made for the investigation of biological characteristics of the structured pallidal oscillator and network. The implementation techniques provide an essential step towards the large-scale neural network to explore the dynamical mechanisms in real time. Furthermore, the proposed methodology enables the FPGA-based system a powerful platform for the investigation on neurodegenerative diseases and real-time control of bio-inspired neuro-robotics.

Parallel processing in the honeybee olfactory pathway: structure, function, and evolution.

PubMed

Rössler, Wolfgang; Brill, Martin F

2013-11-01

Animals face highly complex and dynamic olfactory stimuli in their natural environments, which require fast and reliable olfactory processing. Parallel processing is a common principle of sensory systems supporting this task, for example in visual and auditory systems, but its role in olfaction remained unclear. Studies in the honeybee focused on a dual olfactory pathway. Two sets of projection neurons connect glomeruli in two antennal-lobe hemilobes via lateral and medial tracts in opposite sequence with the mushroom bodies and lateral horn. Comparative studies suggest that this dual-tract circuit represents a unique adaptation in Hymenoptera. Imaging studies indicate that glomeruli in both hemilobes receive redundant sensory input. Recent simultaneous multi-unit recordings from projection neurons of both tracts revealed widely overlapping response profiles strongly indicating parallel olfactory processing. Whereas lateral-tract neurons respond fast with broad (generalistic) profiles, medial-tract neurons are odorant specific and respond slower. In analogy to "what-" and "where" subsystems in visual pathways, this suggests two parallel olfactory subsystems providing "what-" (quality) and "when" (temporal) information. Temporal response properties may support across-tract coincidence coding in higher centers. Parallel olfactory processing likely enhances perception of complex odorant mixtures to decode the diverse and dynamic olfactory world of a social insect.

Cortical processing of dynamic sound envelope transitions.

PubMed

Zhou, Yi; Wang, Xiaoqin

2010-12-08

Slow envelope fluctuations in the range of 2-20 Hz provide important segmental cues for processing communication sounds. For a successful segmentation, a neural processor must capture envelope features associated with the rise and fall of signal energy, a process that is often challenged by the interference of background noise. This study investigated the neural representations of slowly varying envelopes in quiet and in background noise in the primary auditory cortex (A1) of awake marmoset monkeys. We characterized envelope features based on the local average and rate of change of sound level in envelope waveforms and identified envelope features to which neurons were selective by reverse correlation. Our results showed that envelope feature selectivity of A1 neurons was correlated with the degree of nonmonotonicity in their static rate-level functions. Nonmonotonic neurons exhibited greater feature selectivity than monotonic neurons in quiet and in background noise. The diverse envelope feature selectivity decreased spike-timing correlation among A1 neurons in response to the same envelope waveforms. As a result, the variability, but not the average, of the ensemble responses of A1 neurons represented more faithfully the dynamic transitions in low-frequency sound envelopes both in quiet and in background noise.

Modeles numeriques de la stimulation optique de neurones assistee par nanoparticules plasmoniques

NASA Astrophysics Data System (ADS)

Le Hir, Nicolas

La stimulation de neurones par laser emerge depuis plusieurs annees comme une alternative aux techniques plus traditionnelles de stimulation artificielle. Contrairement a celles-ci, la stimulation lumineuse ne necessite pas d'interagir directement avec le tissu organique, comme c'est le cas pour une stimulation par electrodes, et ne necessite pas de manipulation genetique comme c'est le cas pour les methodes optogenetiques. Plus recemment, la stimulation lumineuse de neurones assistee par nanoparticules a emerge comme un complement a la stimulation simplement lumineuse. L'utilisation de nanoparticules complementaires permet d'augmenter la precision spatiale du procede et de diminuer la fluence necessaire pour observer le phenomene. Ceci vient des proprietes d'interaction entre les nanoparticules et le faisceau laser, comme par exemple les proprietes d'absorption des nanoparticules. Deux phenomenes princpaux sont observes. Dans certains cas, il s'agit d'une depolarisation de la membrane, ou d'un potentiel d'action. Dans d'autres experiences, un influx de calcium vers l'interieur du neurone est detecte par une augmentation de la fluorescence d'une proteine sensible a la concentration calcique. Certaines stimulations sont globales, c'est a dire qu'une perturbation se propage a l'ensemble du neurone : c'est le cas d'un potentiel d'action. D'autres sont, au contraire, locales et ne se propagent pas a l'ensemble de la cellule. Si une stimulation lumineuse globale est rendue possible par des techniques relativement bien maitrisees a l'heure actuelle, comme l'optogenetique, une stimulation uniquement locale est plus difficile a realiser. Or, il semblerait que les methodes de stimulation lumineuse assistees par nanoparticules puissent, dans certaines conditions, offrir cette possibilite. Cela serait d'une grande aide pour conduire de nouvelles etudes sur le fonctionnement des neurones, en offrant de nouvelles possibilites experimentales en complement des possibilites actuelles. Cependant, le mecanisme physique a l'origine de la stimulation lumineuse de neurones, ainsi que celui a l'orgine de la stimulation lumineuse assistee par nanoparticules, n'est a ce jour pas totalement compris. Des hypotheses ont ete formulees concernant ce mecanisme : il pourrait etre photothermique, photomecanique, ou encore photochimique. Il se pourrait egalement que plusieurs mecanismes soient a l'oeuvre conjointement, etant donne la variete des observations. La litterature ne converge pas a ce sujet et l'existence d'un mecanisme commun aux differentes situations n'a pas ete demontree.

Spatio-Temporal Patterning in Primary Motor Cortex at Movement Onset.

PubMed

Best, Matthew D; Suminski, Aaron J; Takahashi, Kazutaka; Brown, Kevin A; Hatsopoulos, Nicholas G

2017-02-01

Voluntary movement initiation involves the engagement of large populations of motor cortical neurons around movement onset. Despite knowledge of the temporal dynamics that lead to movement, the spatial structure of these dynamics across the cortical surface remains unknown. In data from 4 rhesus macaques, we show that the timing of attenuation of beta frequency local field potential oscillations, a correlate of locally activated cortex, forms a spatial gradient across primary motor cortex (MI). We show that these spatio-temporal dynamics are recapitulated in the engagement order of ensembles of MI neurons. We demonstrate that these patterns are unique to movement onset and suggest that movement initiation requires a precise spatio-temporal sequential activation of neurons in MI. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

For things needing your attention: the role of neocortical gamma in sensory perception.

PubMed

Pritchett, Dominique L; Siegle, Joshua H; Deister, Christopher A; Moore, Christopher I

2015-04-01

Two general classes of hypotheses for the role for gamma oscillations in sensation are those that predict gamma facilitates signal amplification through local synchronization of a distinct ensemble, and those that predict gamma modulates fine temporal relationships between neurons to represent information. Correlative evidence has been offered for and against these hypotheses. A recent study in which gamma was optogenetically entrained by driving fast-spiking interneurons showed enhanced sensory detection of harder-to-perceive stimuli, those that benefit most from attention, in agreement with the amplification hypotheses. These findings are supported by similar studies employing less specific optogenetic patterns or single neuron stimulation, but contrast with findings based on direct optogenetic stimulation of pyramidal neurons. Key next steps for this topic are described. Copyright © 2015 Elsevier Ltd. All rights reserved.

Linking dynamic patterns of neural activity in orbitofrontal cortex with decision making.

PubMed

Rich, Erin L; Stoll, Frederic M; Rudebeck, Peter H

2018-04-01

Humans and animals demonstrate extraordinary flexibility in choice behavior, particularly when deciding based on subjective preferences. We evaluate options on different scales, deliberate, and often change our minds. Little is known about the neural mechanisms that underlie these dynamic aspects of decision-making, although neural activity in orbitofrontal cortex (OFC) likely plays a central role. Recent evidence from studies in macaques shows that attention modulates value responses in OFC, and that ensembles of OFC neurons dynamically signal different options during choices. When contexts change, these ensembles flexibly remap to encode the new task. Determining how these dynamic patterns emerge and relate to choices will inform models of decision-making and OFC function. Copyright © 2017 Elsevier Ltd. All rights reserved.

Stimuli Reduce the Dimensionality of Cortical Activity

PubMed Central

Mazzucato, Luca; Fontanini, Alfredo; La Camera, Giancarlo

2016-01-01

The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models. PMID:26924968

Stimuli Reduce the Dimensionality of Cortical Activity.

PubMed

Mazzucato, Luca; Fontanini, Alfredo; La Camera, Giancarlo

2016-01-01

The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models.

Disentangling polydispersity in the PCNA−p15PAF complex, a disordered, transient and multivalent macromolecular assembly

PubMed Central

Cordeiro, Tiago N.; Chen, Po-chia; De Biasio, Alfredo; Sibille, Nathalie; Blanco, Francisco J.; Hub, Jochen S.; Crehuet, Ramon

2017-01-01

Abstract The intrinsically disordered p15PAF regulates DNA replication and repair when interacting with the Proliferating Cell Nuclear Antigen (PCNA) sliding clamp. As many interactions between disordered proteins and globular partners involved in signaling and regulation, the complex between p15PAF and trimeric PCNA is of low affinity, forming a transient complex that is difficult to characterize at a structural level due to its inherent polydispersity. We have determined the structure, conformational fluctuations, and relative population of the five species that coexist in solution by combining small-angle X-ray scattering (SAXS) with molecular modelling. By using explicit ensemble descriptions for the individual species, built using integrative approaches and molecular dynamics (MD) simulations, we collectively interpreted multiple SAXS profiles as population-weighted thermodynamic mixtures. The analysis demonstrates that the N-terminus of p15PAF penetrates the PCNA ring and emerges on the back face. This observation substantiates the role of p15PAF as a drag regulating PCNA processivity during DNA repair. Our study reveals the power of ensemble-based approaches to decode structural, dynamic, and thermodynamic information from SAXS data. This strategy paves the way for deciphering the structural bases of flexible, transient and multivalent macromolecular assemblies involved in pivotal biological processes. PMID:28180305

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits

PubMed Central

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-01-01

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits. DOI: http://dx.doi.org/10.7554/eLife.10056.001 PMID:26705334

Use of a simplified method of optical recording to identify foci of maximal neuron activity in the somatosensory cortex of white rats.

PubMed

Inyushin, M Y; Volnova, A B; Lenkov, D N

2001-01-01

Eight mongrel white male rats were studied under urethane anesthesia, and neuron activity evoked by mechanical and/or electrical stimulation of the contralateral whiskers was recorded in the primary somatosensory cortex. Recordings were made using a digital USB chamber attached to the printer port of a Pentium 200MMX computer running standard programs. Optical images were obtained in the barrel-field zone using a differential signal, i.e., the difference signal for cortex images in control and experimental animals. The results obtained here showed that subtraction of averaged sequences of frames yielded images consisting of spots reflecting the probable position of activated groups of neurons. The most effective stimulation consisted of natural low-frequency stimulation of the whiskers. The method can be used for preliminary mapping of cortical zones, as it provides for rapid and reproducible testing of the activity of neuron ensembles over large areas of the cortex.

Risk of punishment influences discrete and coordinated encoding of reward-guided actions by prefrontal cortex and VTA neurons

PubMed Central

Park, Junchol

2017-01-01

Actions motivated by rewards are often associated with risk of punishment. Little is known about the neural representation of punishment risk during reward-seeking behavior. We modeled this circumstance in rats by designing a task where actions were consistently rewarded but probabilistically punished. Spike activity and local field potentials were recorded during task performance simultaneously from VTA and mPFC, two reciprocally connected regions implicated in reward-seeking and aversive behaviors. At the single unit level, we found that ensembles of putative dopamine and non-dopamine VTA neurons and mPFC neurons encode the relationship between action and punishment. At the network level, we found that coherent theta oscillations synchronize VTA and mPFC in a bottom-up direction, effectively phase-modulating the neuronal spike activity in the two regions during punishment-free actions. This synchrony declined as a function of punishment probability, suggesting that during reward-seeking actions, risk of punishment diminishes VTA-driven neural synchrony between the two regions. PMID:29058673

Stereotyped responses of Drosophila peptidergic neuronal ensemble depend on downstream neuromodulators

PubMed Central

Mena, Wilson; Diegelmann, Sören; Wegener, Christian; Ewer, John

2016-01-01

Neuropeptides play a key role in the regulation of behaviors and physiological responses including alertness, social recognition, and hunger, yet, their mechanism of action is poorly understood. Here, we focus on the endocrine control ecdysis behavior, which is used by arthropods to shed their cuticle at the end of every molt. Ecdysis is triggered by ETH (Ecdysis triggering hormone), and we show that the response of peptidergic neurons that produce CCAP (crustacean cardioactive peptide), which are key targets of ETH and control the onset of ecdysis behavior, depends fundamentally on the actions of neuropeptides produced by other direct targets of ETH and released in a broad paracrine manner within the CNS; by autocrine influences from the CCAP neurons themselves; and by inhibitory actions mediated by GABA. Our findings provide insights into how this critical insect behavior is controlled and general principles for understanding how neuropeptides organize neuronal activity and behaviors. DOI: http://dx.doi.org/10.7554/eLife.19686.001 PMID:27976997

Are memory traces localized or distributed?

PubMed

Thompson, R F

1991-01-01

Evidence supports the view that "memory traces" are formed in the hippocampus and in the cerebellum in classical conditioning of discrete behavioral responses (e.g. eyeblink conditioning). In the hippocampus, learning results in long-lasting increases in excitability of pyramidal neurons that appear to be localized to these neurons (i.e. changes in membrane properties and receptor function). However, these learning-altered pyramidal neurons are distributed widely throughout CA3 and CA1. Although it plays a key role in certain aspects of classical conditioning, the hippocampus is not necessary for learning and memory of the basic conditioned responses. The cerebellum and its associated brain stem circuitry, on the other hand, does appear to be essential (necessary and sufficient) for learning and memory of the conditioned response. Evidence to date is most consistent with a localized trace in the interpositus nucleus and multiple localized traces in cerebellar cortex, each involving relatively large ensembles of neurons. Perhaps "procedural" memory traces are relatively localized and "declarative" traces more widely distributed.

Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice.

PubMed

Li, Ying; Mathis, Alexander; Grewe, Benjamin F; Osterhout, Jessica A; Ahanonu, Biafra; Schnitzer, Mark J; Murthy, Venkatesh N; Dulac, Catherine

2017-11-16

The medial amygdala (MeA) plays a critical role in processing species- and sex-specific signals that trigger social and defensive behaviors. However, the principles by which this deep brain structure encodes social information is poorly understood. We used a miniature microscope to image the Ca 2+ dynamics of large neural ensembles in awake behaving mice and tracked the responses of MeA neurons over several months. These recordings revealed spatially intermingled subsets of MeA neurons with distinct temporal dynamics. The encoding of social information in the MeA differed between males and females and relied on information from both individual cells and neuronal populations. By performing long-term Ca 2+ imaging across different social contexts, we found that sexual experience triggers lasting and sex-specific changes in MeA activity, which, in males, involve signaling by oxytocin. These findings reveal basic principles underlying the brain's representation of social information and its modulation by intrinsic and extrinsic factors. Copyright © 2017 Elsevier Inc. All rights reserved.

Distinct Mechanisms for Synchronization and Temporal Patterning of Odor-Encoding Neural Assemblies

NASA Astrophysics Data System (ADS)

MacLeod, Katrina; Laurent, Gilles

1996-11-01

Stimulus-evoked oscillatory synchronization of neural assemblies and temporal patterns of neuronal activity have been observed in many sensory systems, such as the visual and auditory cortices of mammals or the olfactory system of insects. In the locust olfactory system, single odor puffs cause the immediate formation of odor-specific neural assemblies, defined both by their transient synchronized firing and their progressive transformation over the course of a response. The application of an antagonist of ionotropic γ-aminobutyric acid (GABA) receptors to the first olfactory relay neuropil selectively blocked the fast inhibitory synapse between local and projection neurons. This manipulation abolished the synchronization of the odor-coding neural ensembles but did not affect each neuron's temporal response patterns to odors, even when these patterns contained periods of inhibition. Fast GABA-mediated inhibition, therefore, appears to underlie neuronal synchronization but not response tuning in this olfactory system. The selective desynchronization of stimulus-evoked oscillating neural assemblies in vivo is now possible, enabling direct functional tests of their significance for sensation and perception.

Error Control Coding Techniques for Space and Satellite Communications

NASA Technical Reports Server (NTRS)

Lin, Shu

2000-01-01

This paper presents a concatenated turbo coding system in which a Reed-Solomom outer code is concatenated with a binary turbo inner code. In the proposed system, the outer code decoder and the inner turbo code decoder interact to achieve both good bit error and frame error performances. The outer code decoder helps the inner turbo code decoder to terminate its decoding iteration while the inner turbo code decoder provides soft-output information to the outer code decoder to carry out a reliability-based soft-decision decoding. In the case that the outer code decoding fails, the outer code decoder instructs the inner code decoder to continue its decoding iterations until the outer code decoding is successful or a preset maximum number of decoding iterations is reached. This interaction between outer and inner code decoders reduces decoding delay. Also presented in the paper are an effective criterion for stopping the iteration process of the inner code decoder and a new reliability-based decoding algorithm for nonbinary codes.

An Interactive Concatenated Turbo Coding System

NASA Technical Reports Server (NTRS)

Liu, Ye; Tang, Heng; Lin, Shu; Fossorier, Marc

1999-01-01

This paper presents a concatenated turbo coding system in which a Reed-Solomon outer code is concatenated with a binary turbo inner code. In the proposed system, the outer code decoder and the inner turbo code decoder interact to achieve both good bit error and frame error performances. The outer code decoder helps the inner turbo code decoder to terminate its decoding iteration while the inner turbo code decoder provides soft-output information to the outer code decoder to carry out a reliability-based soft- decision decoding. In the case that the outer code decoding fails, the outer code decoder instructs the inner code decoder to continue its decoding iterations until the outer code decoding is successful or a preset maximum number of decoding iterations is reached. This interaction between outer and inner code decoders reduces decoding delay. Also presented in the paper are an effective criterion for stopping the iteration process of the inner code decoder and a new reliability-based decoding algorithm for nonbinary codes.

An ultra-sparse code underliesthe generation of neural sequences in a songbird

NASA Astrophysics Data System (ADS)

Hahnloser, Richard H. R.; Kozhevnikov, Alexay A.; Fee, Michale S.

2002-09-01

Sequences of motor activity are encoded in many vertebrate brains by complex spatio-temporal patterns of neural activity; however, the neural circuit mechanisms underlying the generation of these pre-motor patterns are poorly understood. In songbirds, one prominent site of pre-motor activity is the forebrain robust nucleus of the archistriatum (RA), which generates stereotyped sequences of spike bursts during song and recapitulates these sequences during sleep. We show that the stereotyped sequences in RA are driven from nucleus HVC (high vocal centre), the principal pre-motor input to RA. Recordings of identified HVC neurons in sleeping and singing birds show that individual HVC neurons projecting onto RA neurons produce bursts sparsely, at a single, precise time during the RA sequence. These HVC neurons burst sequentially with respect to one another. We suggest that at each time in the RA sequence, the ensemble of active RA neurons is driven by a subpopulation of RA-projecting HVC neurons that is active only at that time. As a population, these HVC neurons may form an explicit representation of time in the sequence. Such a sparse representation, a temporal analogue of the `grandmother cell' concept for object recognition, eliminates the problem of temporal interference during sequence generation and learning attributed to more distributed representations.

Thalamocortical Connections Drive Intracortical Activation of Functional Columns in the Mislaminated Reeler Somatosensory Cortex

PubMed Central

Wagener, Robin J.; Witte, Mirko; Guy, Julien; Mingo-Moreno, Nieves; Kügler, Sebastian; Staiger, Jochen F.

2016-01-01

Neuronal wiring is key to proper neural information processing. Tactile information from the rodent's whiskers reaches the cortex via distinct anatomical pathways. The lemniscal pathway relays whisking and touch information from the ventral posteromedial thalamic nucleus to layer IV of the primary somatosensory “barrel” cortex. The disorganized neocortex of the reeler mouse is a model system that should severely compromise the ingrowth of thalamocortical axons (TCAs) into the cortex. Moreover, it could disrupt intracortical wiring. We found that neuronal intermingling within the reeler barrel cortex substantially exceeded previous descriptions, leading to the loss of layers. However, viral tracing revealed that TCAs still specifically targeted transgenically labeled spiny layer IV neurons. Slice electrophysiology and optogenetics proved that these connections represent functional synapses. In addition, we assessed intracortical activation via immediate-early-gene expression resulting from a behavioral exploration task. The cellular composition of activated neuronal ensembles suggests extensive similarities in intracolumnar information processing in the wild-type and reeler brains. We conclude that extensive ectopic positioning of neuronal partners can be compensated for by cell-autonomous mechanisms that allow for the establishment of proper connectivity. Thus, genetic neuronal fate seems to be of greater importance for correct cortical wiring than radial neuronal position. PMID:26564256

Time-Warp–Invariant Neuronal Processing

PubMed Central

Gütig, Robert; Sompolinsky, Haim

2009-01-01

Fluctuations in the temporal durations of sensory signals constitute a major source of variability within natural stimulus ensembles. The neuronal mechanisms through which sensory systems can stabilize perception against such fluctuations are largely unknown. An intriguing instantiation of such robustness occurs in human speech perception, which relies critically on temporal acoustic cues that are embedded in signals with highly variable duration. Across different instances of natural speech, auditory cues can undergo temporal warping that ranges from 2-fold compression to 2-fold dilation without significant perceptual impairment. Here, we report that time-warp–invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons. We propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task. Applying this general biophysical mechanism to the example of speech processing, we propose a neuronal network model for time-warp–invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task. Our results demonstrate the important functional role of synaptic conductances in spike-based neuronal information processing and learning. The biophysics of temporal integration at neuronal membranes can endow sensory pathways with powerful time-warp–invariant computational capabilities. PMID:19582146

Decoding ALS: From Genes to Mechanism

PubMed Central

Taylor, J. Paul; Brown, Robert H.; Cleveland, Don W.

2017-01-01

Preface Amyotrophic lateral sclerosis (ALS) is a progressive and uniformly fatal neurodegenerative disease. A plethora of genetic factors underlying ALS have now been identified that drive motor neuron degeneration, increase susceptibility to the disease, or influence the rate of progression. Emerging themes include dysfunction in RNA metabolism and protein homeostasis, with specific defects in nucleocytoplasmic trafficking, induction of endoplasmic reticulum stress, and impaired dynamics of ribonucleoprotein bodies such as RNA granules that assemble through the process of liquid-liquid phase separation. Extraordinary recent progress in understanding the biology of ALS provides new grounds for optimism that meaningful therapies for ALS will be identified. PMID:27830784

Factor-Analysis Methods for Higher-Performance Neural Prostheses

PubMed Central

Santhanam, Gopal; Yu, Byron M.; Gilja, Vikash; Ryu, Stephen I.; Afshar, Afsheen; Sahani, Maneesh; Shenoy, Krishna V.

2009-01-01

Neural prostheses aim to provide treatment options for individuals with nervous-system disease or injury. It is necessary, however, to increase the performance of such systems before they can be clinically viable for patients with motor dysfunction. One performance limitation is the presence of correlated trial-to-trial variability that can cause neural responses to wax and wane in concert as the subject is, for example, more attentive or more fatigued. If a system does not properly account for this variability, it may mistakenly interpret such variability as an entirely different intention by the subject. We report here the design and characterization of factor-analysis (FA)–based decoding algorithms that can contend with this confound. We characterize the decoders (classifiers) on experimental data where monkeys performed both a real reach task and a prosthetic cursor task while we recorded from 96 electrodes implanted in dorsal premotor cortex. The decoder attempts to infer the underlying factors that comodulate the neurons' responses and can use this information to substantially lower error rates (one of eight reach endpoint predictions) by ≲75% (e.g., ∼20% total prediction error using traditional independent Poisson models reduced to ∼5%). We also examine additional key aspects of these new algorithms: the effect of neural integration window length on performance, an extension of the algorithms to use Poisson statistics, and the effect of training set size on the decoding accuracy of test data. We found that FA-based methods are most effective for integration windows >150 ms, although still advantageous at shorter timescales, that Gaussian-based algorithms performed better than the analogous Poisson-based algorithms and that the FA algorithm is robust even with a limited amount of training data. We propose that FA-based methods are effective in modeling correlated trial-to-trial neural variability and can be used to substantially increase overall prosthetic system performance. PMID:19297518

Tactile orientation perception: an ideal observer analysis of human psychophysical performance in relation to macaque area 3b receptive fields

PubMed Central

Peters, Ryan M.; Staibano, Phillip

2015-01-01

The ability to resolve the orientation of edges is crucial to daily tactile and sensorimotor function, yet the means by which edge perception occurs is not well understood. Primate cortical area 3b neurons have diverse receptive field (RF) spatial structures that may participate in edge orientation perception. We evaluated five candidate RF models for macaque area 3b neurons, previously recorded while an oriented bar contacted the monkey's fingertip. We used a Bayesian classifier to assign each neuron a best-fit RF structure. We generated predictions for human performance by implementing an ideal observer that optimally decoded stimulus-evoked spike counts in the model neurons. The ideal observer predicted a saturating reduction in bar orientation discrimination threshold with increasing bar length. We tested 24 humans on an automated, precision-controlled bar orientation discrimination task and observed performance consistent with that predicted. We next queried the ideal observer to discover the RF structure and number of cortical neurons that best matched each participant's performance. Human perception was matched with a median of 24 model neurons firing throughout a 1-s period. The 10 lowest-performing participants were fit with RFs lacking inhibitory sidebands, whereas 12 of the 14 higher-performing participants were fit with RFs containing inhibitory sidebands. Participants whose discrimination improved as bar length increased to 10 mm were fit with longer RFs; those who performed well on the 2-mm bar, with narrower RFs. These results suggest plausible RF features and computational strategies underlying tactile spatial perception and may have implications for perceptual learning. PMID:26354318

Efficient transformation of an auditory population code in a small sensory system.

PubMed

Clemens, Jan; Kutzki, Olaf; Ronacher, Bernhard; Schreiber, Susanne; Wohlgemuth, Sandra

2011-08-16

Optimal coding principles are implemented in many large sensory systems. They include the systematic transformation of external stimuli into a sparse and decorrelated neuronal representation, enabling a flexible readout of stimulus properties. Are these principles also applicable to size-constrained systems, which have to rely on a limited number of neurons and may only have to fulfill specific and restricted tasks? We studied this question in an insect system--the early auditory pathway of grasshoppers. Grasshoppers use genetically fixed songs to recognize mates. The first steps of neural processing of songs take place in a small three-layer feed-forward network comprising only a few dozen neurons. We analyzed the transformation of the neural code within this network. Indeed, grasshoppers create a decorrelated and sparse representation, in accordance with optimal coding theory. Whereas the neuronal input layer is best read out as a summed population, a labeled-line population code for temporal features of the song is established after only two processing steps. At this stage, information about song identity is maximal for a population decoder that preserves neuronal identity. We conclude that optimal coding principles do apply to the early auditory system of the grasshopper, despite its size constraints. The inputs, however, are not encoded in a systematic, map-like fashion as in many larger sensory systems. Already at its periphery, part of the grasshopper auditory system seems to focus on behaviorally relevant features, and is in this property more reminiscent of higher sensory areas in vertebrates.

A Neuronal Network Model for Pitch Selectivity and Representation

PubMed Central

Huang, Chengcheng; Rinzel, John

2016-01-01

Pitch is a perceptual correlate of periodicity. Sounds with distinct spectra can elicit the same pitch. Despite the importance of pitch perception, understanding the cellular mechanism of pitch perception is still a major challenge and a mechanistic model of pitch is lacking. A multi-stage neuronal network model is developed for pitch frequency estimation using biophysically-based, high-resolution coincidence detector neurons. The neuronal units respond only to highly coincident input among convergent auditory nerve fibers across frequency channels. Their selectivity for only very fast rising slopes of convergent input enables these slope-detectors to distinguish the most prominent coincidences in multi-peaked input time courses. Pitch can then be estimated from the first-order interspike intervals of the slope-detectors. The regular firing pattern of the slope-detector neurons are similar for sounds sharing the same pitch despite the distinct timbres. The decoded pitch strengths also correlate well with the salience of pitch perception as reported by human listeners. Therefore, our model can serve as a neural representation for pitch. Our model performs successfully in estimating the pitch of missing fundamental complexes and reproducing the pitch variation with respect to the frequency shift of inharmonic complexes. It also accounts for the phase sensitivity of pitch perception in the cases of Schroeder phase, alternating phase and random phase relationships. Moreover, our model can also be applied to stochastic sound stimuli, iterated-ripple-noise, and account for their multiple pitch perceptions. PMID:27378900

A Neuronal Network Model for Pitch Selectivity and Representation.

PubMed

Huang, Chengcheng; Rinzel, John

2016-01-01

Pitch is a perceptual correlate of periodicity. Sounds with distinct spectra can elicit the same pitch. Despite the importance of pitch perception, understanding the cellular mechanism of pitch perception is still a major challenge and a mechanistic model of pitch is lacking. A multi-stage neuronal network model is developed for pitch frequency estimation using biophysically-based, high-resolution coincidence detector neurons. The neuronal units respond only to highly coincident input among convergent auditory nerve fibers across frequency channels. Their selectivity for only very fast rising slopes of convergent input enables these slope-detectors to distinguish the most prominent coincidences in multi-peaked input time courses. Pitch can then be estimated from the first-order interspike intervals of the slope-detectors. The regular firing pattern of the slope-detector neurons are similar for sounds sharing the same pitch despite the distinct timbres. The decoded pitch strengths also correlate well with the salience of pitch perception as reported by human listeners. Therefore, our model can serve as a neural representation for pitch. Our model performs successfully in estimating the pitch of missing fundamental complexes and reproducing the pitch variation with respect to the frequency shift of inharmonic complexes. It also accounts for the phase sensitivity of pitch perception in the cases of Schroeder phase, alternating phase and random phase relationships. Moreover, our model can also be applied to stochastic sound stimuli, iterated-ripple-noise, and account for their multiple pitch perceptions.

Neurons responsive to face-view in the primate ventrolateral prefrontal cortex.

PubMed

Romanski, L M; Diehl, M M

2011-08-25

Studies have indicated that temporal and prefrontal brain regions process face and vocal information. Face-selective and vocalization-responsive neurons have been demonstrated in the ventrolateral prefrontal cortex (VLPFC) and some prefrontal cells preferentially respond to combinations of face and corresponding vocalizations. These studies suggest VLPFC in nonhuman primates may play a role in communication that is similar to the role of inferior frontal regions in human language processing. If VLPFC is involved in communication, information about a speaker's face including identity, face-view, gaze, and emotional expression might be encoded by prefrontal neurons. In the following study, we examined the effect of face-view in ventrolateral prefrontal neurons by testing cells with auditory, visual, and a set of human and monkey faces rotated through 0°, 30°, 60°, 90°, and -30°. Prefrontal neurons responded selectively to either the identity of the face presented (human or monkey) or to the specific view of the face/head, or to both identity and face-view. Neurons which were affected by the identity of the face most often showed an increase in firing in the second part of the stimulus period. Neurons that were selective for face-view typically preferred forward face-view stimuli (0° and 30° rotation). The neurons which were selective for forward face-view were also auditory responsive compared to other neurons which responded to other views or were unselective which were not auditory responsive. Our analysis showed that the human forward face (0°) was decoded better and also contained the most information relative to other face-views. Our findings confirm a role for VLPFC in the processing and integration of face and vocalization information and add to the growing body of evidence that the primate ventrolateral prefrontal cortex plays a prominent role in social communication and is an important model in understanding the cellular mechanisms of communication. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

Neurons responsive to face-view in the Primate Ventrolateral Prefrontal Cortex

PubMed Central

Romanski, Lizabeth M.; Diehl, Maria M.

2011-01-01

Studies have indicated that temporal and prefrontal brain regions process face and vocal information. Face-selective and vocalization-responsive neurons have been demonstrated in the ventrolateral prefrontal cortex (VLPFC) and some prefrontal cells preferentially respond to combinations of face and corresponding vocalizations. These studies suggest VLPFC in non-human primates may play a role in communication that is similar to the role of inferior frontal regions in human language processing. If VLPFC is involved in communication, information about a speaker's face including identity, face-view, gaze and emotional expression might be encoded by prefrontal neurons. In the following study, we examined the effect of face-view in ventrolateral prefrontal neurons by testing cells with auditory, visual, and a set of human and monkey faces rotated through 0°, 30°, 60°, 90°, and −30°. Prefrontal neurons responded selectively to either the identity of the face presented (human or monkey) or to the specific view of the face/head, or to both identity and face-view. Neurons which were affected by the identity of the face most often showed an increase in firing in the second part of the stimulus period. Neurons that were selective for face-view typically preferred forward face-view stimuli (0° and 30° rotation). The neurons which were selective for forward face-view were also auditory responsive compared to other neurons which responded to other views or were unselective which were not auditory responsive. Our analysis showed that the human forward face (0°) was decoded better and also contained the most information relative to other face-views. Our findings confirm a role for VLPFC in the processing and integration of face and vocalization information and add to the growing body of evidence that the primate ventrolateral prefrontal cortex plays a prominent role in social communication and is an important model in understanding the cellular mechanisms of communication. PMID:21605632

Environmentally induced amplitude death and firing provocation in large-scale networks of neuronal systems

NASA Astrophysics Data System (ADS)

Pankratova, Evgeniya V.; Kalyakulina, Alena I.

2016-12-01

We study the dynamics of multielement neuronal systems taking into account both the direct interaction between the cells via linear coupling and nondiffusive cell-to-cell communication via common environment. For the cells exhibiting individual bursting behavior, we have revealed the dependence of the network activity on its scale. Particularly, we show that small-scale networks demonstrate the inability to maintain complicated oscillations: for a small number of elements in an ensemble, the phenomenon of amplitude death is observed. The existence of threshold network scales and mechanisms causing firing in artificial and real multielement neural networks, as well as their significance for biological applications, are discussed.

Sequential dynamics in the motif of excitatory coupled elements

NASA Astrophysics Data System (ADS)

Korotkov, Alexander G.; Kazakov, Alexey O.; Osipov, Grigory V.

2015-11-01

In this article a new model of motif (small ensemble) of neuron-like elements is proposed. It is built with the use of the generalized Lotka-Volterra model with excitatory couplings. The main motivation for this work comes from the problems of neuroscience where excitatory couplings are proved to be the predominant type of interaction between neurons of the brain. In this paper it is shown that there are two modes depending on the type of coupling between the elements: the mode with a stable heteroclinic cycle and the mode with a stable limit cycle. Our second goal is to examine the chaotic dynamics of the generalized three-dimensional Lotka-Volterra model.

Neuronal pattern separation in the olfactory bulb improves odor discrimination learning

PubMed Central

Lagier, Samuel; Begnaud, Frédéric; Rodriguez, Ivan; Carleton, Alan

2015-01-01

Neuronal pattern separation is thought to enable the brain to disambiguate sensory stimuli with overlapping features thereby extracting valuable information. In the olfactory system, it remains unknown whether pattern separation acts as a driving force for sensory discrimination and the learning thereof. Here we show that overlapping odor-evoked input patterns to the mouse olfactory bulb (OB) are dynamically reformatted in the network at the timescale of a single breath, giving rise to separated patterns of activity in ensemble of output neurons (mitral/tufted cells; M/T). Strikingly, the extent of pattern separation in M/T assemblies predicts behavioral discrimination performance during the learning phase. Furthermore, exciting or inhibiting GABAergic OB interneurons, using optogenetics or pharmacogenetics, altered pattern separation and thereby odor discrimination learning in a bidirectional way. In conclusion, we propose that the OB network can act as a pattern separator facilitating olfactory stimuli distinction, a process that is sculpted by synaptic inhibition. PMID:26301325

Neuronal pattern separation in the olfactory bulb improves odor discrimination learning.

PubMed

Gschwend, Olivier; Abraham, Nixon M; Lagier, Samuel; Begnaud, Frédéric; Rodriguez, Ivan; Carleton, Alan

2015-10-01

Neuronal pattern separation is thought to enable the brain to disambiguate sensory stimuli with overlapping features, thereby extracting valuable information. In the olfactory system, it remains unknown whether pattern separation acts as a driving force for sensory discrimination and the learning thereof. We found that overlapping odor-evoked input patterns to the mouse olfactory bulb (OB) were dynamically reformatted in the network on the timescale of a single breath, giving rise to separated patterns of activity in an ensemble of output neurons, mitral/tufted (M/T) cells. Notably, the extent of pattern separation in M/T assemblies predicted behavioral discrimination performance during the learning phase. Furthermore, exciting or inhibiting GABAergic OB interneurons, using optogenetics or pharmacogenetics, altered pattern separation and thereby odor discrimination learning in a bidirectional way. In conclusion, we propose that the OB network can act as a pattern separator facilitating olfactory stimulus distinction, a process that is sculpted by synaptic inhibition.

Pyramidal Cell-Interneuron Interactions Underlie Hippocampal Ripple Oscillations

PubMed Central

Stark, Eran; Roux, Lisa; Eichler, Ronny; Senzai, Yuta; Royer, Sebastien; Buzsáki, György

2015-01-01

SUMMARY High-frequency ripple oscillations, observed most prominently in the hippocampal CA1 pyramidal layer, are associated with memory consolidation. The cellular and network mechanisms underlying the generation, frequency control, and spatial coherence of the rhythm are poorly understood. Using multisite optogenetic manipulations in freely behaving rodents, we found that depolarization of a small group of nearby pyramidal cells was sufficient to induce high-frequency oscillations, whereas closed-loop silencing of pyramidal cells or activation of parvalbumin-(PV) or somatostatin-immunoreactive interneurons aborted spontaneously occurring ripples. Focal pharmacological blockade of GABAA receptors abolished ripples. Localized PV inter-neuron activation paced ensemble spiking, and simultaneous induction of high-frequency oscillations at multiple locations resulted in a temporally coherent pattern mediated by phase-locked inter-neuron spiking. These results constrain competing models of ripple generation and indicate that temporally precise local interactions between excitatory and inhibitory neurons support ripple generation in the intact hippocampus. PMID:25033186

Multisite two-photon imaging of neurons on multielectrode arrays

NASA Astrophysics Data System (ADS)

Potter, Steve M.; Lukina, Natalia; Longmuir, Kenneth J.; Wu, Yan

2001-04-01

We wish to understand how neural systems store, recall, and process information. We are using cultured networks of cortical neurons grown on microelectrode arrays as a model system for studying the emergent properties of ensembles of living neurons. We have developed a 2-way communication interface between the cultured network and a computer- generated animal, the Neurally Controlled Animat. Neural activity is used to control the behavior of the Animat, and 2- photon time-lapse imaging is carried out in order to observe the morphological changes that might underlie changes in neural processing. The 2-photon microscope is ideal for repeated imaging over hours or days, with submicron resolution and little photodamage. We have designed a computer-controlled microscope stage that allows imaging several locations in sequence, in order to collect more image data. For the latest progress, see: http://www.caltech.edu/~pinelab/PotterGroup.htm.

An ensemble of regulatory elements controls Runx3 spatiotemporal expression in subsets of dorsal root ganglia proprioceptive neurons.

PubMed

Appel, Elena; Weissmann, Sarit; Salzberg, Yehuda; Orlovsky, Kira; Negreanu, Varda; Tsoory, Michael; Raanan, Calanit; Feldmesser, Ester; Bernstein, Yael; Wolstein, Orit; Levanon, Ditsa; Groner, Yoram

2016-12-01

The Runx3 transcription factor is essential for development and diversification of the dorsal root ganglia (DRGs) TrkC sensory neurons. In Runx3-deficient mice, developing TrkC neurons fail to extend central and peripheral afferents, leading to cell death and disruption of the stretch reflex circuit, resulting in severe limb ataxia. Despite its central role, the mechanisms underlying the spatiotemporal expression specificities of Runx3 in TrkC neurons were largely unknown. Here we first defined the genomic transcription unit encompassing regulatory elements (REs) that mediate the tissue-specific expression of Runx3. Using transgenic mice expressing BAC reporters spanning the Runx3 locus, we discovered three REs-dubbed R1, R2, and R3-that cross-talk with promoter-2 (P2) to drive TrkC neuron-specific Runx3 transcription. Deletion of single or multiple elements either in the BAC transgenics or by CRISPR/Cas9-mediated endogenous ablation established the REs' ability to promote and/or repress Runx3 expression in developing sensory neurons. Our analysis reveals that an intricate combinatorial interplay among the three REs governs Runx3 expression in distinct subtypes of TrkC neurons while concomitantly extinguishing its expression in non-TrkC neurons. These findings provide insights into the mechanism regulating cell type-specific expression and subtype diversification of TrkC neurons in developing DRGs. © 2016 Appel et al.; Published by Cold Spring Harbor Laboratory Press.

Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field.

PubMed

Moerel, Michelle; De Martino, Federico; Kemper, Valentin G; Schmitter, Sebastian; Vu, An T; Uğurbil, Kâmil; Formisano, Elia; Yacoub, Essa

2018-01-01

Following rapid technological advances, ultra-high field functional MRI (fMRI) enables exploring correlates of neuronal population activity at an increasing spatial resolution. However, as the fMRI blood-oxygenation-level-dependent (BOLD) contrast is a vascular signal, the spatial specificity of fMRI data is ultimately determined by the characteristics of the underlying vasculature. At 7T, fMRI measurement parameters determine the relative contribution of the macro- and microvasculature to the acquired signal. Here we investigate how these parameters affect relevant high-end fMRI analyses such as encoding, decoding, and submillimeter mapping of voxel preferences in the human auditory cortex. Specifically, we compare a T 2 * weighted fMRI dataset, obtained with 2D gradient echo (GE) EPI, to a predominantly T 2 weighted dataset obtained with 3D GRASE. We first investigated the decoding accuracy based on two encoding models that represented different hypotheses about auditory cortical processing. This encoding/decoding analysis profited from the large spatial coverage and sensitivity of the T 2 * weighted acquisitions, as evidenced by a significantly higher prediction accuracy in the GE-EPI dataset compared to the 3D GRASE dataset for both encoding models. The main disadvantage of the T 2 * weighted GE-EPI dataset for encoding/decoding analyses was that the prediction accuracy exhibited cortical depth dependent vascular biases. However, we propose that the comparison of prediction accuracy across the different encoding models may be used as a post processing technique to salvage the spatial interpretability of the GE-EPI cortical depth-dependent prediction accuracy. Second, we explored the mapping of voxel preferences. Large-scale maps of frequency preference (i.e., tonotopy) were similar across datasets, yet the GE-EPI dataset was preferable due to its larger spatial coverage and sensitivity. However, submillimeter tonotopy maps revealed biases in assigned frequency preference and selectivity for the GE-EPI dataset, but not for the 3D GRASE dataset. Thus, a T 2 weighted acquisition is recommended if high specificity in tonotopic maps is required. In conclusion, different fMRI acquisitions were better suited for different analyses. It is therefore critical that any sequence parameter optimization considers the eventual intended fMRI analyses and the nature of the neuroscience questions being asked. Copyright © 2017 Elsevier Inc. All rights reserved.

Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function.

PubMed

Yu, Zhe; McKnight, Timothy E; Ericson, M Nance; Melechko, Anatoli V; Simpson, Michael L; Morrison, Barclay

2012-05-01

Neural chips, which are capable of simultaneous multisite neural recording and stimulation, have been used to detect and modulate neural activity for almost thirty years. As neural interfaces, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface may potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single-cell level and even inside the cell. The authors demonstrate the utility of a neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes. The new device can be used to stimulate and/or monitor signals from brain tissue in vitro and for monitoring dynamic information of neuroplasticity both intracellularly and at the single cell level including neuroelectrical and neurochemical activities. Copyright © 2012 Elsevier Inc. All rights reserved.

Single trial discrimination of individual finger movements on one hand: A combined MEG and EEG study☆

PubMed Central

Quandt, F.; Reichert, C.; Hinrichs, H.; Heinze, H.J.; Knight, R.T.; Rieger, J.W.

2012-01-01

It is crucial to understand what brain signals can be decoded from single trials with different recording techniques for the development of Brain-Machine Interfaces. A specific challenge for non-invasive recording methods are activations confined to small spatial areas on the cortex such as the finger representation of one hand. Here we study the information content of single trial brain activity in non-invasive MEG and EEG recordings elicited by finger movements of one hand. We investigate the feasibility of decoding which of four fingers of one hand performed a slight button press. With MEG we demonstrate reliable discrimination of single button presses performed with the thumb, the index, the middle or the little finger (average over all subjects and fingers 57%, best subject 70%, empirical guessing level: 25.1%). EEG decoding performance was less robust (average over all subjects and fingers 43%, best subject 54%, empirical guessing level 25.1%). Spatiotemporal patterns of amplitude variations in the time series provided best information for discriminating finger movements. Non-phase-locked changes of mu and beta oscillations were less predictive. Movement related high gamma oscillations were observed in average induced oscillation amplitudes in the MEG but did not provide sufficient information about the finger's identity in single trials. Importantly, pre-movement neuronal activity provided information about the preparation of the movement of a specific finger. Our study demonstrates the potential of non-invasive MEG to provide informative features for individual finger control in a Brain-Machine Interface neuroprosthesis. PMID:22155040

Collective phase response curves for heterogeneous coupled oscillators

NASA Astrophysics Data System (ADS)

Hannay, Kevin M.; Booth, Victoria; Forger, Daniel B.

2015-08-01

Phase response curves (PRCs) have become an indispensable tool in understanding the entrainment and synchronization of biological oscillators. However, biological oscillators are often found in large coupled heterogeneous systems and the variable of physiological importance is the collective rhythm resulting from an aggregation of the individual oscillations. To study this phenomena we consider phase resetting of the collective rhythm for large ensembles of globally coupled Sakaguchi-Kuramoto oscillators. Making use of Ott-Antonsen theory we derive an asymptotically valid analytic formula for the collective PRC. A result of this analysis is a characteristic scaling for the change in the amplitude and entrainment points for the collective PRC compared to the individual oscillator PRC. We support the analytical findings with numerical evidence and demonstrate the applicability of the theory to large ensembles of coupled neuronal oscillators.

Dynamic Grouping of Hippocampal Neural Activity During Cognitive Control of Two Spatial Frames

PubMed Central

Kelemen, Eduard; Fenton, André A.

2010-01-01

Cognitive control is the ability to coordinate multiple streams of information to prevent confusion and select appropriate behavioral responses, especially when presented with competing alternatives. Despite its theoretical and clinical significance, the neural mechanisms of cognitive control are poorly understood. Using a two-frame place avoidance task and partial hippocampal inactivation, we confirmed that intact hippocampal function is necessary for coordinating two streams of spatial information. Rats were placed on a continuously rotating arena and trained to organize their behavior according to two concurrently relevant spatial frames: one stationary, the other rotating. We then studied how information about locations in these two spatial frames is organized in the action potential discharge of ensembles of hippocampal cells. Both streams of information were represented in neuronal discharge—place cell activity was organized according to both spatial frames, but almost all cells preferentially represented locations in one of the two spatial frames. At any given time, most coactive cells tended to represent locations in the same spatial frame, reducing the risk of interference between the two information streams. An ensemble's preference to represent locations in one or the other spatial frame alternated within a session, but at each moment, location in the more behaviorally relevant spatial frame was more likely to be represented. This discharge organized into transient groups of coactive neurons that fired together within 25 ms to represent locations in the same spatial frame. These findings show that dynamic grouping, the transient coactivation of neural subpopulations that represent the same stream of information, can coordinate representations of concurrent information streams and avoid confusion, demonstrating neural-ensemble correlates of cognitive control in hippocampus. PMID:20585373

Enhanced decoding for the Galileo low-gain antenna mission: Viterbi redecoding with four decoding stages

NASA Technical Reports Server (NTRS)

Dolinar, S.; Belongie, M.

1995-01-01

The Galileo low-gain antenna mission will be supported by a coding system that uses a (14,1/4) inner convolutional code concatenated with Reed-Solomon codes of four different redundancies. Decoding for this code is designed to proceed in four distinct stages of Viterbi decoding followed by Reed-Solomon decoding. In each successive stage, the Reed-Solomon decoder only tries to decode the highest redundancy codewords not yet decoded in previous stages, and the Viterbi decoder redecodes its data utilizing the known symbols from previously decoded Reed-Solomon codewords. A previous article analyzed a two-stage decoding option that was not selected by Galileo. The present article analyzes the four-stage decoding scheme and derives the near-optimum set of redundancies selected for use by Galileo. The performance improvements relative to one- and two-stage decoding systems are evaluated.

Sparse orthogonal population representation of spatial context in the retrosplenial cortex.

PubMed

Mao, Dun; Kandler, Steffen; McNaughton, Bruce L; Bonin, Vincent

2017-08-15

Sparse orthogonal coding is a key feature of hippocampal neural activity, which is believed to increase episodic memory capacity and to assist in navigation. Some retrosplenial cortex (RSC) neurons convey distributed spatial and navigational signals, but place-field representations such as observed in the hippocampus have not been reported. Combining cellular Ca 2+ imaging in RSC of mice with a head-fixed locomotion assay, we identified a population of RSC neurons, located predominantly in superficial layers, whose ensemble activity closely resembles that of hippocampal CA1 place cells during the same task. Like CA1 place cells, these RSC neurons fire in sequences during movement, and show narrowly tuned firing fields that form a sparse, orthogonal code correlated with location. RSC 'place' cell activity is robust to environmental manipulations, showing partial remapping similar to that observed in CA1. This population code for spatial context may assist the RSC in its role in memory and/or navigation.Neurons in the retrosplenial cortex (RSC) encode spatial and navigational signals. Here the authors use calcium imaging to show that, similar to the hippocampus, RSC neurons also encode place cell-like activity in a sparse orthogonal representation, partially anchored to the allocentric cues on the linear track.

Suprathreshold stochastic resonance in neural processing tuned by correlation.

PubMed

Durrant, Simon; Kang, Yanmei; Stocks, Nigel; Feng, Jianfeng

2011-07-01

Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.

Suprathreshold stochastic resonance in neural processing tuned by correlation

NASA Astrophysics Data System (ADS)

Durrant, Simon; Kang, Yanmei; Stocks, Nigel; Feng, Jianfeng

2011-07-01

Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.

Searching for collective behavior in a large network of sensory neurons.

PubMed

Tkačik, Gašper; Marre, Olivier; Amodei, Dario; Schneidman, Elad; Bialek, William; Berry, Michael J

2014-01-01

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.

Searching for Collective Behavior in a Large Network of Sensory Neurons

PubMed Central

Tkačik, Gašper; Marre, Olivier; Amodei, Dario; Schneidman, Elad; Bialek, William; Berry, Michael J.

2014-01-01

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction. PMID:24391485

The visual white matter: The application of diffusion MRI and fiber tractography to vision science

PubMed Central

Rokem, Ariel; Takemura, Hiromasa; Bock, Andrew S.; Scherf, K. Suzanne; Behrmann, Marlene; Wandell, Brian A.; Fine, Ione; Bridge, Holly; Pestilli, Franco

2017-01-01

Visual neuroscience has traditionally focused much of its attention on understanding the response properties of single neurons or neuronal ensembles. The visual white matter and the long-range neuronal connections it supports are fundamental in establishing such neuronal response properties and visual function. This review article provides an introduction to measurements and methods to study the human visual white matter using diffusion MRI. These methods allow us to measure the microstructural and macrostructural properties of the white matter in living human individuals; they allow us to trace long-range connections between neurons in different parts of the visual system and to measure the biophysical properties of these connections. We also review a range of findings from recent studies on connections between different visual field maps, the effects of visual impairment on the white matter, and the properties underlying networks that process visual information supporting visual face recognition. Finally, we discuss a few promising directions for future studies. These include new methods for analysis of MRI data, open datasets that are becoming available to study brain connectivity and white matter properties, and open source software for the analysis of these data. PMID:28196374

Hearing in noisy environments: noise invariance and contrast gain control

PubMed Central

Willmore, Ben D B; Cooke, James E; King, Andrew J

2014-01-01

Contrast gain control has recently been identified as a fundamental property of the auditory system. Electrophysiological recordings in ferrets have shown that neurons continuously adjust their gain (their sensitivity to change in sound level) in response to the contrast of sounds that are heard. At the level of the auditory cortex, these gain changes partly compensate for changes in sound contrast. This means that sounds which are structurally similar, but have different contrasts, have similar neuronal representations in the auditory cortex. As a result, the cortical representation is relatively invariant to stimulus contrast and robust to the presence of noise in the stimulus. In the inferior colliculus (an important subcortical auditory structure), gain changes are less reliably compensatory, suggesting that contrast- and noise-invariant representations are constructed gradually as one ascends the auditory pathway. In addition to noise invariance, contrast gain control provides a variety of computational advantages over static neuronal representations; it makes efficient use of neuronal dynamic range, may contribute to redundancy-reducing, sparse codes for sound and allows for simpler decoding of population responses. The circuits underlying auditory contrast gain control are still under investigation. As in the visual system, these circuits may be modulated by factors other than stimulus contrast, forming a potential neural substrate for mediating the effects of attention as well as interactions between the senses. PMID:24907308

Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex

NASA Astrophysics Data System (ADS)

Ohki, Kenichi; Chung, Sooyoung; Ch'ng, Yeang H.; Kara, Prakash; Reid, R. Clay

2005-02-01

Neurons in the cerebral cortex are organized into anatomical columns, with ensembles of cells arranged from the surface to the white matter. Within a column, neurons often share functional properties, such as selectivity for stimulus orientation; columns with distinct properties, such as different preferred orientations, tile the cortical surface in orderly patterns. This functional architecture was discovered with the relatively sparse sampling of microelectrode recordings. Optical imaging of membrane voltage or metabolic activity elucidated the overall geometry of functional maps, but is averaged over many cells (resolution >100µm). Consequently, the purity of functional domains and the precision of the borders between them could not be resolved. Here, we labelled thousands of neurons of the visual cortex with a calcium-sensitive indicator in vivo. We then imaged the activity of neuronal populations at single-cell resolution with two-photon microscopy up to a depth of 400µm. In rat primary visual cortex, neurons had robust orientation selectivity but there was no discernible local structure; neighbouring neurons often responded to different orientations. In area 18 of cat visual cortex, functional maps were organized at a fine scale. Neurons with opposite preferences for stimulus direction were segregated with extraordinary spatial precision in three dimensions, with columnar borders one to two cells wide. These results indicate that cortical maps can be built with single-cell precision.

Decorrelation of Neural-Network Activity by Inhibitory Feedback

PubMed Central

Einevoll, Gaute T.; Diesmann, Markus

2012-01-01

Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between excitatory (E) and inhibitory (I) neurons, but a consequence of a particular structure of correlations among the three possible pairings (EE, EI, II). PMID:23133368

Estimating Single-Trial Responses in EEG

NASA Technical Reports Server (NTRS)

Shah, A. S.; Knuth, K. H.; Truccolo, W. A.; Mehta, A. D.; Fu, K. G.; Johnston, T. A.; Ding, M.; Bressler, S. L.; Schroeder, C. E.; Clancy, Daniel (Technical Monitor)

2002-01-01

Accurate characterization of single-trial field potential responses is critical from a number of perspectives. For example, it allows differentiation of an evoked response from ongoing EEG. We previously developed the multiple component Event Related Potential (mcERP) algorithm to improve resolution of the single-trial evoked response. The mcERP model states that multiple components, each specified by a stereotypic waveform varying in latency and amplitude from trial to trial, comprise the evoked response. Application of the mcERP algorithm to simulated data with three independent, synthetic components has shown that the model is capable of separating these components and estimating their variability. Application of the model to single trial, visual evoked potentials recorded simultaneously from all V1 laminae in an awake, fixating macaque yielded local and far-field components. Certain local components estimated by the model were distributed in both granular and supragranular laminae. This suggests a linear coupling between the responses of thalamo-recipient neuronal ensembles and subsequent responses of supragranular neuronal ensembles, as predicted by the feedforward anatomy of V1. Our results indicate that the mcERP algorithm provides a valid estimation of single-trial responses. This will enable analyses that depend on trial-to-trial variations and those that require separation of the evoked response from background EEG rhythms

Scalable SCPPM Decoder

NASA Technical Reports Server (NTRS)

Quir, Kevin J.; Gin, Jonathan W.; Nguyen, Danh H.; Nguyen, Huy; Nakashima, Michael A.; Moision, Bruce E.

2012-01-01

A decoder was developed that decodes a serial concatenated pulse position modulation (SCPPM) encoded information sequence. The decoder takes as input a sequence of four bit log-likelihood ratios (LLR) for each PPM slot in a codeword via a XAUI 10-Gb/s quad optical fiber interface. If the decoder is unavailable, it passes the LLRs on to the next decoder via a XAUI 10-Gb/s quad optical fiber interface. Otherwise, it decodes the sequence and outputs information bits through a 1-GB/s Ethernet UDP/IP (User Datagram Protocol/Internet Protocol) interface. The throughput for a single decoder unit is 150-Mb/s at an average of four decoding iterations; by connecting a number of decoder units in series, a decoding rate equal to that of the aggregate rate is achieved. The unit is controlled through a 1-GB/s Ethernet UDP/IP interface. This ground station decoder was developed to demonstrate a deep space optical communication link capability, and is unique in the scalable design to achieve real-time SCPP decoding at the aggregate data rate.

Sparse bursts optimize information transmission in a multiplexed neural code.

PubMed

Naud, Richard; Sprekeler, Henning

2018-06-22

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets. Copyright © 2018 the Author(s). Published by PNAS.

Reversible large-scale modification of cortical networks during neuroprosthetic control.

PubMed

Ganguly, Karunesh; Dimitrov, Dragan F; Wallis, Jonathan D; Carmena, Jose M

2011-05-01

Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.

Learning complex temporal patterns with resource-dependent spike timing-dependent plasticity.

PubMed

Hunzinger, Jason F; Chan, Victor H; Froemke, Robert C

2012-07-01

Studies of spike timing-dependent plasticity (STDP) have revealed that long-term changes in the strength of a synapse may be modulated substantially by temporal relationships between multiple presynaptic and postsynaptic spikes. Whereas long-term potentiation (LTP) and long-term depression (LTD) of synaptic strength have been modeled as distinct or separate functional mechanisms, here, we propose a new shared resource model. A functional consequence of our model is fast, stable, and diverse unsupervised learning of temporal multispike patterns with a biologically consistent spiking neural network. Due to interdependencies between LTP and LTD, dendritic delays, and proactive homeostatic aspects of the model, neurons are equipped to learn to decode temporally coded information within spike bursts. Moreover, neurons learn spike timing with few exposures in substantial noise and jitter. Surprisingly, despite having only one parameter, the model also accurately predicts in vitro observations of STDP in more complex multispike trains, as well as rate-dependent effects. We discuss candidate commonalities in natural long-term plasticity mechanisms.

Anisotropic connectivity implements motion-based prediction in a spiking neural network.

PubMed

Kaplan, Bernhard A; Lansner, Anders; Masson, Guillaume S; Perrinet, Laurent U

2013-01-01

Predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the world. Although it is becoming generally accepted, it is not clear on which level spiking neural networks may implement predictive coding and what function their connectivity may have. We present a network model of conductance-based integrate-and-fire neurons inspired by the architecture of retinotopic cortical areas that assumes predictive coding is implemented through network connectivity, namely in the connection delays and in selectiveness for the tuning properties of source and target cells. We show that the applied connection pattern leads to motion-based prediction in an experiment tracking a moving dot. In contrast to our proposed model, a network with random or isotropic connectivity fails to predict the path when the moving dot disappears. Furthermore, we show that a simple linear decoding approach is sufficient to transform neuronal spiking activity into a probabilistic estimate for reading out the target trajectory.

Video time encoding machines.

PubMed

Lazar, Aurel A; Pnevmatikakis, Eftychios A

2011-03-01

We investigate architectures for time encoding and time decoding of visual stimuli such as natural and synthetic video streams (movies, animation). The architecture for time encoding is akin to models of the early visual system. It consists of a bank of filters in cascade with single-input multi-output neural circuits. Neuron firing is based on either a threshold-and-fire or an integrate-and-fire spiking mechanism with feedback. We show that analog information is represented by the neural circuits as projections on a set of band-limited functions determined by the spike sequence. Under Nyquist-type and frame conditions, the encoded signal can be recovered from these projections with arbitrary precision. For the video time encoding machine architecture, we demonstrate that band-limited video streams of finite energy can be faithfully recovered from the spike trains and provide a stable algorithm for perfect recovery. The key condition for recovery calls for the number of neurons in the population to be above a threshold value.

Video Time Encoding Machines

PubMed Central

Lazar, Aurel A.; Pnevmatikakis, Eftychios A.

2013-01-01

We investigate architectures for time encoding and time decoding of visual stimuli such as natural and synthetic video streams (movies, animation). The architecture for time encoding is akin to models of the early visual system. It consists of a bank of filters in cascade with single-input multi-output neural circuits. Neuron firing is based on either a threshold-and-fire or an integrate-and-fire spiking mechanism with feedback. We show that analog information is represented by the neural circuits as projections on a set of band-limited functions determined by the spike sequence. Under Nyquist-type and frame conditions, the encoded signal can be recovered from these projections with arbitrary precision. For the video time encoding machine architecture, we demonstrate that band-limited video streams of finite energy can be faithfully recovered from the spike trains and provide a stable algorithm for perfect recovery. The key condition for recovery calls for the number of neurons in the population to be above a threshold value. PMID:21296708

Statistical coding and decoding of heartbeat intervals.

PubMed

Lucena, Fausto; Barros, Allan Kardec; Príncipe, José C; Ohnishi, Noboru

2011-01-01

The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

Restoration of Hindlimb Movements after Complete Spinal Cord Injury Using Brain-Controlled Functional Electrical Stimulation.

PubMed

Knudsen, Eric B; Moxon, Karen A

2017-01-01

Single neuron and local field potential signals recorded in the primary motor cortex have been repeatedly demonstrated as viable control signals for multi-degree-of-freedom actuators. Although the primary source of these signals has been fore/upper limb motor regions, recent evidence suggests that neural adaptation underlying neuroprosthetic control is generalizable across cortex, including hindlimb sensorimotor cortex. Here, adult rats underwent a longitudinal study that included a hindlimb pedal press task in response to cues for specific durations, followed by brain machine interface (BMI) tasks in healthy rats, after rats received a complete spinal transection and after the BMI signal controls epidural stimulation (BMI-FES). Over the course of the transition from learned behavior to BMI task, fewer neurons were responsive after the cue, the proportion of neurons selective for press duration increased and these neurons carried more information. After a complete, mid-thoracic spinal lesion that completely severed both ascending and descending connections to the lower limbs, there was a reduction in task-responsive neurons followed by a reacquisition of task selectivity in recorded populations. This occurred due to a change in pattern of neuronal responses not simple changes in firing rate. Finally, during BMI-FES, additional information about the intended press duration was produced. This information was not dependent on the stimulation, which was the same for short and long duration presses during the early phase of stimulation, but instead was likely due to sensory feedback to sensorimotor cortex in response to movement along the trunk during the restored pedal press. This post-cue signal could be used as an error signal in a continuous decoder providing information about the position of the limb to optimally control a neuroprosthetic device.

Temporal characteristics of gustatory responses in rat parabrachial neurons vary by stimulus and chemosensitive neuron type.

PubMed

Geran, Laura; Travers, Susan

2013-01-01

It has been demonstrated that temporal features of spike trains can increase the amount of information available for gustatory processing. However, the nature of these temporal characteristics and their relationship to different taste qualities and neuron types are not well-defined. The present study analyzed the time course of taste responses from parabrachial (PBN) neurons elicited by multiple applications of "sweet" (sucrose), "salty" (NaCl), "sour" (citric acid), and "bitter" (quinine and cycloheximide) stimuli in an acute preparation. Time course varied significantly by taste stimulus and best-stimulus classification. Across neurons, the ensemble code for the three electrolytes was similar initially but quinine diverged from NaCl and acid during the second 500 ms of stimulation and all four qualities became distinct just after 1s. This temporal evolution was reflected in significantly broader tuning during the initial response. Metric space analyses of quality discrimination by individual neurons showed that increases in information (H) afforded by temporal factors was usually explained by differences in rate envelope, which had a greater impact during the initial 2s (22.5% increase in H) compared to the later response (9.5%). Moreover, timing had a differential impact according to cell type, with between-quality discrimination in neurons activated maximally by NaCl or citric acid most affected. Timing was also found to dramatically improve within-quality discrimination (80% increase in H) in neurons that responded optimally to bitter stimuli (B-best). Spikes from B-best neurons were also more likely to occur in bursts. These findings suggest that among PBN taste neurons, time-dependent increases in mutual information can arise from stimulus- and neuron-specific differences in response envelope during the initial dynamic period. A stable rate code predominates in later epochs.

Temporal Characteristics of Gustatory Responses in Rat Parabrachial Neurons Vary by Stimulus and Chemosensitive Neuron Type

PubMed Central

Geran, Laura; Travers, Susan

2013-01-01

It has been demonstrated that temporal features of spike trains can increase the amount of information available for gustatory processing. However, the nature of these temporal characteristics and their relationship to different taste qualities and neuron types are not well-defined. The present study analyzed the time course of taste responses from parabrachial (PBN) neurons elicited by multiple applications of “sweet” (sucrose), “salty” (NaCl), “sour” (citric acid), and “bitter” (quinine and cycloheximide) stimuli in an acute preparation. Time course varied significantly by taste stimulus and best-stimulus classification. Across neurons, the ensemble code for the three electrolytes was similar initially but quinine diverged from NaCl and acid during the second 500ms of stimulation and all four qualities became distinct just after 1s. This temporal evolution was reflected in significantly broader tuning during the initial response. Metric space analyses of quality discrimination by individual neurons showed that increases in information (H) afforded by temporal factors was usually explained by differences in rate envelope, which had a greater impact during the initial 2s (22.5% increase in H) compared to the later response (9.5%). Moreover, timing had a differential impact according to cell type, with between-quality discrimination in neurons activated maximally by NaCl or citric acid most affected. Timing was also found to dramatically improve within-quality discrimination (80% increase in H) in neurons that responded optimally to bitter stimuli (B-best). Spikes from B-best neurons were also more likely to occur in bursts. These findings suggest that among PBN taste neurons, time-dependent increases in mutual information can arise from stimulus- and neuron-specific differences in response envelope during the initial dynamic period. A stable rate code predominates in later epochs. PMID:24124597

A novel parallel pipeline structure of VP9 decoder

NASA Astrophysics Data System (ADS)

Qin, Huabiao; Chen, Wu; Yi, Sijun; Tan, Yunfei; Yi, Huan

2018-04-01

To improve the efficiency of VP9 decoder, a novel parallel pipeline structure of VP9 decoder is presented in this paper. According to the decoding workflow, VP9 decoder can be divided into sub-modules which include entropy decoding, inverse quantization, inverse transform, intra prediction, inter prediction, deblocking and pixel adaptive compensation. By analyzing the computing time of each module, hotspot modules are located and the causes of low efficiency of VP9 decoder can be found. Then, a novel pipeline decoder structure is designed by using mixed parallel decoding methods of data division and function division. The experimental results show that this structure can greatly improve the decoding efficiency of VP9.

Singer product apertures-A coded aperture system with a fast decoding algorithm

NASA Astrophysics Data System (ADS)

Byard, Kevin; Shutler, Paul M. E.

2017-06-01

A new type of coded aperture configuration that enables fast decoding of the coded aperture shadowgram data is presented. Based on the products of incidence vectors generated from the Singer difference sets, we call these Singer product apertures. For a range of aperture dimensions, we compare experimentally the performance of three decoding methods: standard decoding, induction decoding and direct vector decoding. In all cases the induction and direct vector methods are several orders of magnitude faster than the standard method, with direct vector decoding being significantly faster than induction decoding. For apertures of the same dimensions the increase in speed offered by direct vector decoding over induction decoding is better for lower throughput apertures.

Sensitivity to Pigment-Dispersing Factor (PDF) Is Cell-Type Specific among PDF-Expressing Circadian Clock Neurons in the Madeira Cockroach.

PubMed

Gestrich, Julia; Giese, Maria; Shen, Wen; Zhang, Yi; Voss, Alexandra; Popov, Cyril; Stengl, Monika; Wei, HongYing

2018-02-01

Transplantation studies have pinpointed the circadian clock of the Madeira cockroach to the accessory medulla (AME) of the brain's optic lobes. The AME is innervated by approximately 240 adjacent neuropeptidergic neurons, including 12 pigment-dispersing factor (PDF)-expressing neurons anterior to the AME (aPDFMEs). Four of the aPDFMEs project contralaterally, controlling locomotor activity rhythms of the night-active cockroach. The present in vitro Ca 2+ imaging analysis focuses on contralaterally projecting AME neurons and their responses to PDF, GABA, and acetylcholine (ACh). First, rhodamine-dextran backfills from the contralateral optic stalk identified contralaterally projecting AME neurons, which were then dispersed in primary cell cultures. After characterization of PDF, GABA, and ACh responses, PDF immunocytochemistry identified ipsilaterally and contralaterally projecting PDFMEs. All PDF-sensitive clock neurons, PDF-immunoreactive clock neurons, and the majority of ipsilaterally and contralaterally projecting cells were excited by ACh. GABA inhibited all PDF-expressing clock neurons, and about half of other ipsilaterally projecting and most contralaterally projecting clock neurons. For the first time, we identified PDF autoreceptors in PDF-secreting cockroach circadian pacemakers. The medium-sized aPDFMEs and all other contralaterally projecting PDF-sensitive clock cells were inhibited by PDF. The ipsilaterally remaining small PDF-sensitive clock cells were activated by PDF. Only the largest aPDFME did not express PDF autoreceptors. We hypothesize that opposing PDF signaling generates 2 different ensembles of clock cells with antiphasic activity, regulating and maintaining a constant phase relationship between rest and activity cycles of the night-active cockroach.

Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ9-tetrahydrocannabinol administration

PubMed Central

Fetterhoff, Dustin; Opris, Ioan; Simpson, Sean L.; Deadwyler, Sam A.; Hampson, Robert E.; Kraft, Robert A.

2014-01-01

Background Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing. New method Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain–computer interfaces and nonlinear neuronal models. Results Neurons involved in memory processing (“Functional Cell Types” or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid-type 1 receptor partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons. Comparison with existing methods WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events. Conclusion z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain–computer interfaces. PMID:25086297

Differences in the Predictors of Reading Comprehension in First Graders from Low Socio-Economic Status Families with Either Good or Poor Decoding Skills

PubMed Central

Gentaz, Edouard; Sprenger-Charolles, Liliane; Theurel, Anne

2015-01-01

Based on the assumption that good decoding skills constitute a bootstrapping mechanism for reading comprehension, the present study investigated the relative contribution of the former skill to the latter compared to that of three other predictors of reading comprehension (listening comprehension, vocabulary and phonemic awareness) in 392 French-speaking first graders from low SES families. This large sample was split into three groups according to their level of decoding skills assessed by pseudoword reading. Using a cutoff of 1 SD above or below the mean of the entire population, there were 63 good decoders, 267 average decoders and 62 poor decoders. 58% of the variance in reading comprehension was explained by our four predictors, with decoding skills proving to be the best predictor (12.1%, 7.3% for listening comprehension, 4.6% for vocabulary and 3.3% for phonemic awareness). Interaction between group versus decoding skills, listening comprehension and phonemic awareness accounted for significant additional variance (3.6%, 1.1% and 1.0%, respectively). The effects on reading comprehension of decoding skills and phonemic awareness were higher in poor and average decoders than in good decoders whereas listening comprehension accounted for more variance in good and average decoders than in poor decoders. Furthermore, the percentage of children with impaired reading comprehension skills was higher in the group of poor decoders (55%) than in the two other groups (average decoders: 7%; good decoders: 0%) and only 6 children (1.5%) had impaired reading comprehension skills with unimpaired decoding skills, listening comprehension or vocabulary. These results challenge the outcomes of studies on “poor comprehenders” by showing that, at least in first grade, poor reading comprehension is strongly linked to the level of decoding skills. PMID:25793519

Differences in the predictors of reading comprehension in first graders from low socio-economic status families with either good or poor decoding skills.

PubMed

Gentaz, Edouard; Sprenger-Charolles, Liliane; Theurel, Anne

2015-01-01

Based on the assumption that good decoding skills constitute a bootstrapping mechanism for reading comprehension, the present study investigated the relative contribution of the former skill to the latter compared to that of three other predictors of reading comprehension (listening comprehension, vocabulary and phonemic awareness) in 392 French-speaking first graders from low SES families. This large sample was split into three groups according to their level of decoding skills assessed by pseudoword reading. Using a cutoff of 1 SD above or below the mean of the entire population, there were 63 good decoders, 267 average decoders and 62 poor decoders. 58% of the variance in reading comprehension was explained by our four predictors, with decoding skills proving to be the best predictor (12.1%, 7.3% for listening comprehension, 4.6% for vocabulary and 3.3% for phonemic awareness). Interaction between group versus decoding skills, listening comprehension and phonemic awareness accounted for significant additional variance (3.6%, 1.1% and 1.0%, respectively). The effects on reading comprehension of decoding skills and phonemic awareness were higher in poor and average decoders than in good decoders whereas listening comprehension accounted for more variance in good and average decoders than in poor decoders. Furthermore, the percentage of children with impaired reading comprehension skills was higher in the group of poor decoders (55%) than in the two other groups (average decoders: 7%; good decoders: 0%) and only 6 children (1.5%) had impaired reading comprehension skills with unimpaired decoding skills, listening comprehension or vocabulary. These results challenge the outcomes of studies on "poor comprehenders" by showing that, at least in first grade, poor reading comprehension is strongly linked to the level of decoding skills.

High Throughput Biological Analysis Using Multi-bit Magnetic Digital Planar Tags

NASA Astrophysics Data System (ADS)

Hong, B.; Jeong, J.-R.; Llandro, J.; Hayward, T. J.; Ionescu, A.; Trypiniotis, T.; Mitrelias, T.; Kopper, K. P.; Steinmuller, S. J.; Bland, J. A. C.

2008-06-01

We report a new magnetic labelling technology for high-throughput biomolecular identification and DNA sequencing. Planar multi-bit magnetic tags have been designed and fabricated, which comprise a magnetic barcode formed by an ensemble of micron-sized thin film Ni80Fe20 bars encapsulated in SU8. We show that by using a globally applied magnetic field and magneto-optical Kerr microscopy the magnetic elements in the multi-bit magnetic tags can be addressed individually and encoded/decoded remotely. The critical steps needed to show the feasibility of this technology are demonstrated, including fabrication, flow transport, remote writing and reading, and successful functionalization of the tags as verified by fluorescence detection. This approach is ideal for encoding information on tags in microfluidic flow or suspension, for such applications as labelling of chemical precursors during drug synthesis and combinatorial library-based high-throughput multiplexed bioassays.

Distributed representations in memory: Insights from functional brain imaging

PubMed Central

Rissman, Jesse; Wagner, Anthony D.

2015-01-01

Forging new memories for facts and events, holding critical details in mind on a moment-to-moment basis, and retrieving knowledge in the service of current goals all depend on a complex interplay between neural ensembles throughout the brain. Over the past decade, researchers have increasingly leveraged powerful analytical tools (e.g., multi-voxel pattern analysis) to decode the information represented within distributed fMRI activity patterns. In this review, we discuss how these methods can sensitively index neural representations of perceptual and semantic content, and how leverage on the engagement of distributed representations provides unique insights into distinct aspects of memory-guided behavior. We emphasize that, in addition to characterizing the contents of memories, analyses of distributed patterns shed light on the processes that influence how information is encoded, maintained, or retrieved, and thus inform memory theory. We conclude by highlighting open questions about memory that can be addressed through distributed pattern analyses. PMID:21943171

Improved discriminability of spatiotemporal neural patterns in rat motor cortical areas as directional choice learning progresses

PubMed Central

Mao, Hongwei; Yuan, Yuan; Si, Jennie

2015-01-01

Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively) areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2–3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats' behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task. PMID:25798093

WP1: transgenic opto-animals

NASA Astrophysics Data System (ADS)

UŻarowska, E.; Czajkowski, Rafał; Konopka, W.

2014-11-01

We aim to create a set of genetic tools where permanent opsin expression (ChR or NpHR) is precisely limited to the population of neurons that express immediate early gene c-fos during a specific temporal window of behavioral training. Since the c-fos gene is only expressed in neurons that form experience-dependent ensemble, this approach will result in specific labeling of a small subset of cells that create memory trace for the learned behavior. To this end we employ two alternative inducible gene expression systems: Tet Expression System and Cre/lox System. In both cases, the temporal window for opsin induction is controlled pharmacologically, by doxycycline or tamoxifen, respectively. Both systems will be used for creating lines of transgenic animals.

Transformation of the neural code for tactile detection from thalamus to cortex.

PubMed

Vázquez, Yuriria; Salinas, Emilio; Romo, Ranulfo

2013-07-09

To understand how sensory-driven neural activity gives rise to perception, it is essential to characterize how various relay stations in the brain encode stimulus presence. Neurons in the ventral posterior lateral (VPL) nucleus of the somatosensory thalamus and in primary somatosensory cortex (S1) respond to vibrotactile stimulation with relatively slow modulations (∼100 ms) of their firing rate. In addition, faster modulations (∼10 ms) time-locked to the stimulus waveform are observed in both areas, but their contribution to stimulus detection is unknown. Furthermore, it is unclear whether VPL and S1 neurons encode stimulus presence with similar accuracy and via the same response features. To address these questions, we recorded single neurons while trained monkeys judged the presence or absence of a vibrotactile stimulus of variable amplitude, and their activity was analyzed with a unique decoding method that is sensitive to the time scale of the firing rate fluctuations. We found that the maximum detection accuracy of single neurons is similar in VPL and S1. However, VPL relies more heavily on fast rate modulations than S1, and as a consequence, the neural code in S1 is more tolerant: its performance degrades less when the readout method or the time scale of integration is suboptimal. Therefore, S1 neurons implement a more robust code, one less sensitive to the temporal integration window used to infer stimulus presence downstream. The differences between VPL and S1 responses signaling the appearance of a stimulus suggest a transformation of the neural code from thalamus to cortex.

Architecture for time or transform domain decoding of reed-solomon codes

NASA Technical Reports Server (NTRS)

Hsu, In-Shek (Inventor); Truong, Trieu-Kie (Inventor); Deutsch, Leslie J. (Inventor); Shao, Howard M. (Inventor)

1989-01-01

Two pipeline (255,233) RS decoders, one a time domain decoder and the other a transform domain decoder, use the same first part to develop an errata locator polynomial .tau.(x), and an errata evaluator polynominal A(x). Both the time domain decoder and transform domain decoder have a modified GCD that uses an input multiplexer and an output demultiplexer to reduce the number of GCD cells required. The time domain decoder uses a Chien search and polynomial evaluator on the GCD outputs .tau.(x) and A(x), for the final decoding steps, while the transform domain decoder uses a transform error pattern algorithm operating on .tau.(x) and the initial syndrome computation S(x), followed by an inverse transform algorithm in sequence for the final decoding steps prior to adding the received RS coded message to produce a decoded output message.

FPGA implementation of low complexity LDPC iterative decoder

NASA Astrophysics Data System (ADS)

Verma, Shivani; Sharma, Sanjay

2016-07-01

Low-density parity-check (LDPC) codes, proposed by Gallager, emerged as a class of codes which can yield very good performance on the additive white Gaussian noise channel as well as on the binary symmetric channel. LDPC codes have gained lots of importance due to their capacity achieving property and excellent performance in the noisy channel. Belief propagation (BP) algorithm and its approximations, most notably min-sum, are popular iterative decoding algorithms used for LDPC and turbo codes. The trade-off between the hardware complexity and the decoding throughput is a critical factor in the implementation of the practical decoder. This article presents introduction to LDPC codes and its various decoding algorithms followed by realisation of LDPC decoder by using simplified message passing algorithm and partially parallel decoder architecture. Simplified message passing algorithm has been proposed for trade-off between low decoding complexity and decoder performance. It greatly reduces the routing and check node complexity of the decoder. Partially parallel decoder architecture possesses high speed and reduced complexity. The improved design of the decoder possesses a maximum symbol throughput of 92.95 Mbps and a maximum of 18 decoding iterations. The article presents implementation of 9216 bits, rate-1/2, (3, 6) LDPC decoder on Xilinx XC3D3400A device from Spartan-3A DSP family.

Temporal learning in the cerebellum: The microcircuit model

NASA Technical Reports Server (NTRS)

Miles, Coe F.; Rogers, David

1990-01-01

The cerebellum is that part of the brain which coordinates motor reflex behavior. To perform effectively, it must learn to generate specific motor commands at the proper times. We propose a fundamental circuit, called the MicroCircuit, which is the minimal ensemble of neurons both necessary and sufficient to learn timing. We describe how learning takes place in the MicroCircuit, which then explains the global behavior of the cerebellum as coordinated MicroCircuit behavior.

Information Flow through a Model of the C. elegans Klinotaxis Circuit

PubMed Central

Izquierdo, Eduardo J.; Williams, Paul L.; Beer, Randall D.

2015-01-01

Understanding how information about external stimuli is transformed into behavior is one of the central goals of neuroscience. Here we characterize the information flow through a complete sensorimotor circuit: from stimulus, to sensory neurons, to interneurons, to motor neurons, to muscles, to motion. Specifically, we apply a recently developed framework for quantifying information flow to a previously published ensemble of models of salt klinotaxis in the nematode worm Caenorhabditis elegans. Despite large variations in the neural parameters of individual circuits, we found that the overall information flow architecture circuit is remarkably consistent across the ensemble. This suggests structural connectivity is not necessarily predictive of effective connectivity. It also suggests information flow analysis captures general principles of operation for the klinotaxis circuit. In addition, information flow analysis reveals several key principles underlying how the models operate: (1) Interneuron class AIY is responsible for integrating information about positive and negative changes in concentration, and exhibits a strong left/right information asymmetry. (2) Gap junctions play a crucial role in the transfer of information responsible for the information symmetry observed in interneuron class AIZ. (3) Neck motor neuron class SMB implements an information gating mechanism that underlies the circuit’s state-dependent response. (4) The neck carries more information about small changes in concentration than about large ones, and more information about positive changes in concentration than about negative ones. Thus, not all directions of movement are equally informative for the worm. Each of these findings corresponds to hypotheses that could potentially be tested in the worm. Knowing the results of these experiments would greatly refine our understanding of the neural circuit underlying klinotaxis. PMID:26465883

Information Flow through a Model of the C. elegans Klinotaxis Circuit.

PubMed

Izquierdo, Eduardo J; Williams, Paul L; Beer, Randall D

2015-01-01

Understanding how information about external stimuli is transformed into behavior is one of the central goals of neuroscience. Here we characterize the information flow through a complete sensorimotor circuit: from stimulus, to sensory neurons, to interneurons, to motor neurons, to muscles, to motion. Specifically, we apply a recently developed framework for quantifying information flow to a previously published ensemble of models of salt klinotaxis in the nematode worm Caenorhabditis elegans. Despite large variations in the neural parameters of individual circuits, we found that the overall information flow architecture circuit is remarkably consistent across the ensemble. This suggests structural connectivity is not necessarily predictive of effective connectivity. It also suggests information flow analysis captures general principles of operation for the klinotaxis circuit. In addition, information flow analysis reveals several key principles underlying how the models operate: (1) Interneuron class AIY is responsible for integrating information about positive and negative changes in concentration, and exhibits a strong left/right information asymmetry. (2) Gap junctions play a crucial role in the transfer of information responsible for the information symmetry observed in interneuron class AIZ. (3) Neck motor neuron class SMB implements an information gating mechanism that underlies the circuit's state-dependent response. (4) The neck carries more information about small changes in concentration than about large ones, and more information about positive changes in concentration than about negative ones. Thus, not all directions of movement are equally informative for the worm. Each of these findings corresponds to hypotheses that could potentially be tested in the worm. Knowing the results of these experiments would greatly refine our understanding of the neural circuit underlying klinotaxis.

Transitions between sleep and feeding states in rat ventral striatum neurons

PubMed Central

Tellez, Luis A.; Perez, Isaac O.; Simon, Sidney A.

2012-01-01

Neurons in the nucleus accumbens (NAc) have been shown to participate in several behavioral states, including feeding and sleep. However, it is not known if the same neuron participates in both states and, if so, how similar are the responses. In addition, since the NAc contains several cell types, it is not known if each type participates in the transitions associated with feeding and sleep. Such knowledge is important for understanding the interaction between two different neural networks. For these reasons we recorded ensembles of NAc neurons while individual rats volitionally transitioned between the following states: awake and goal directed, feeding, quiet-awake, and sleeping. We found that during both feeding and sleep states, the same neurons could increase their activity (be activated) or decrease their activity (be inactivated) by feeding and/or during sleep, thus indicating that the vast majority of NAc neurons integrate sleep and feeding signals arising from spatially distinct neural networks. In contrast, a smaller population was modulated by only one of the states. For the majority of neurons in either state, we found that when one population was excited, the other was inhibited, suggesting that they act as a local circuit. Classification of neurons into putative interneurons [fast-spiking interneurons (pFSI) and choline acetyltransferase interneurons (pChAT)] and projection medium spiny neurons (pMSN) showed that all three types are modulated by transitions to and from feeding and sleep states. These results show, for the first time, that in the NAc, those putative inhibitory interneurons respond similarly to pMSN projection neurons and demonstrate interactions between NAc networks involved in sleep and feeding. PMID:22745464

Noise adaptation in integrate-and fire neurons.

PubMed

Rudd, M E; Brown, L G

1997-07-01

The statistical spiking response of an ensemble of identically prepared stochastic integrate-and-fire neurons to a rectangular input current plus gaussian white noise is analyzed. It is shown that, on average, integrate-and-fire neurons adapt to the root-mean-square noise level of their input. This phenomenon is referred to as noise adaptation. Noise adaptation is characterized by a decrease in the average neural firing rate and an accompanying decrease in the average value of the generator potential, both of which can be attributed to noise-induced resets of the generator potential mediated by the integrate-and-fire mechanism. A quantitative theory of noise adaptation in stochastic integrate-and-fire neurons is developed. It is shown that integrate-and-fire neurons, on average, produce transient spiking activity whenever there is an increase in the level of their input noise. This transient noise response is either reduced or eliminated over time, depending on the parameters of the model neuron. Analytical methods are used to prove that nonleaky integrate-and-fire neurons totally adapt to any constant input noise level, in the sense that their asymptotic spiking rates are independent of the magnitude of their input noise. For leaky integrate-and-fire neurons, the long-run noise adaptation is not total, but the response to noise is partially eliminated. Expressions for the probability density function of the generator potential and the first two moments of the potential distribution are derived for the particular case of a nonleaky neuron driven by gaussian white noise of mean zero and constant variance. The functional significance of noise adaptation for the performance of networks comprising integrate-and-fire neurons is discussed.

The design plan of a VLSI single chip (255, 223) Reed-Solomon decoder

NASA Technical Reports Server (NTRS)

Hsu, I. S.; Shao, H. M.; Deutsch, L. J.

1987-01-01

The very large-scale integration (VLSI) architecture of a single chip (255, 223) Reed-Solomon decoder for decoding both errors and erasures is described. A decoding failure detection capability is also included in this system so that the decoder will recognize a failure to decode instead of introducing additional errors. This could happen whenever the received word contains too many errors and erasures for the code to correct. The number of transistors needed to implement this decoder is estimated at about 75,000 if the delay for received message is not included. This is in contrast to the older transform decoding algorithm which needs about 100,000 transistors. However, the transform decoder is simpler in architecture than the time decoder. It is therefore possible to implement a single chip (255, 223) Reed-Solomon decoder with today's VLSI technology. An implementation strategy for the decoder system is presented. This represents the first step in a plan to take advantage of advanced coding techniques to realize a 2.0 dB coding gain for future space missions.

Multi-stage decoding for multi-level block modulation codes

NASA Technical Reports Server (NTRS)

Lin, Shu; Kasami, Tadao

1991-01-01

Various types of multistage decoding for multilevel block modulation codes, in which the decoding of a component code at each stage can be either soft decision or hard decision, maximum likelihood or bounded distance are discussed. Error performance for codes is analyzed for a memoryless additive channel based on various types of multi-stage decoding, and upper bounds on the probability of an incorrect decoding are derived. It was found that, if component codes of a multi-level modulation code and types of decoding at various stages are chosen properly, high spectral efficiency and large coding gain can be achieved with reduced decoding complexity. It was found that the difference in performance between the suboptimum multi-stage soft decision maximum likelihood decoding of a modulation code and the single stage optimum decoding of the overall code is very small, only a fraction of dB loss in SNR at the probability of an incorrect decoding for a block of 10(exp -6). Multi-stage decoding of multi-level modulation codes really offers a way to achieve the best of three worlds, bandwidth efficiency, coding gain, and decoding complexity.

The serial message-passing schedule for LDPC decoding algorithms

NASA Astrophysics Data System (ADS)

Liu, Mingshan; Liu, Shanshan; Zhou, Yuan; Jiang, Xue

2015-12-01

The conventional message-passing schedule for LDPC decoding algorithms is the so-called flooding schedule. It has the disadvantage that the updated messages cannot be used until next iteration, thus reducing the convergence speed . In this case, the Layered Decoding algorithm (LBP) based on serial message-passing schedule is proposed. In this paper the decoding principle of LBP algorithm is briefly introduced, and then proposed its two improved algorithms, the grouped serial decoding algorithm (Grouped LBP) and the semi-serial decoding algorithm .They can improve LBP algorithm's decoding speed while maintaining a good decoding performance.

Investigation of Neural Strategies of Visual Search

NASA Technical Reports Server (NTRS)

Krauzlis, Richard J.

2003-01-01

The goal of this project was to measure how neurons in the superior colliculus (SC) change their activity during a visual search task. Specifically, we proposed to measure how the activity of these neurons was altered by the discriminability of visual targets and to test how these changes might predict the changes in the subjects performance. The primary rationale for this study was that understanding how the information encoded by these neurons constrains overall search performance would foster the development of better models of human performance. Work performed during the period supported by this grant has achieved these aims. First, we have recorded from neurons in the superior colliculus (SC) during a visual search task in which the difficulty of the task and the performance of the subject was systematically varied. The results from these single-neuron physiology experiments shows that prior to eye movement onset, the difference in activity across the ensemble of neurons reaches a fixed threshold value, reflecting the operation of a winner-take-all mechanism. Second, we have developed a model of eye movement decisions based on the principle of winner-take-all . The model incorporates the idea that the overt saccade choice reflects only one of the multiple saccades prepared during visual discrimination, consistent with our physiological data. The value of the model is that, unlike previous models, it is able to account for both the latency and the percent correct of saccade choices.

Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity.

PubMed

Cazé, Romain D; Jarvis, Sarah; Foust, Amanda J; Schultz, Simon R

2017-09-01

Hearing, vision, touch: underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Nonlinear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of nonpreferred stimuli. Using a multi-subunit nonlinear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of loss of synapses or dendrites than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially nonselective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

Image transmission system using adaptive joint source and channel decoding

NASA Astrophysics Data System (ADS)

Liu, Weiliang; Daut, David G.

2005-03-01

In this paper, an adaptive joint source and channel decoding method is designed to accelerate the convergence of the iterative log-dimain sum-product decoding procedure of LDPC codes as well as to improve the reconstructed image quality. Error resilience modes are used in the JPEG2000 source codec, which makes it possible to provide useful source decoded information to the channel decoder. After each iteration, a tentative decoding is made and the channel decoded bits are then sent to the JPEG2000 decoder. Due to the error resilience modes, some bits are known to be either correct or in error. The positions of these bits are then fed back to the channel decoder. The log-likelihood ratios (LLR) of these bits are then modified by a weighting factor for the next iteration. By observing the statistics of the decoding procedure, the weighting factor is designed as a function of the channel condition. That is, for lower channel SNR, a larger factor is assigned, and vice versa. Results show that the proposed joint decoding methods can greatly reduce the number of iterations, and thereby reduce the decoding delay considerably. At the same time, this method always outperforms the non-source controlled decoding method up to 5dB in terms of PSNR for various reconstructed images.

A novel tetrode microdrive for simultaneous multi-neuron recording from different regions of primate brain.

PubMed

Santos, Lucas; Opris, Ioan; Fuqua, Joshua; Hampson, Robert E; Deadwyler, Sam A

2012-04-15

A unique custom-made tetrode microdrive for recording from large numbers of neurons in several areas of primate brain is described as a means for assessing simultaneous neural activity in cortical and subcortical structures in nonhuman primates (NHPs) performing behavioral tasks. The microdrive device utilizes tetrode technology with up to six ultra-thin microprobe guide tubes (0.1mm) that can be independently positioned, each containing reduced diameter tetrode and/or hexatrode microwires (0.02 mm) for recording and isolating single neuron activity. The microdrive device is mounted within the standard NHP cranial well and allows traversal of brain depths up to 40.0 mm. The advantages of this technology are demonstrated via simultaneously recorded large populations of neurons with tetrode type probes during task performance from a) primary motor cortex and deep brain structures (caudate-putamen and hippocampus) and b) multiple layers within the prefrontal cortex. The means to characterize interactions of well-isolated ensembles of neurons recorded simultaneously from different regions, as shown with this device, has not been previously available for application in primate brain. The device has extensive application to primate models for the detection and study of inoperative or maladaptive neural circuits related to human neurological disorders. Published by Elsevier B.V.

Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

PubMed

Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

2018-04-24

Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

Emergent coordination underlying learning to reach to grasp with a brain-machine interface.

PubMed

Vaidya, Mukta; Balasubramanian, Karthikeyan; Southerland, Joshua; Badreldin, Islam; Eleryan, Ahmed; Shattuck, Kelsey; Gururangan, Suchin; Slutzky, Marc; Osborne, Leslie; Fagg, Andrew; Oweiss, Karim; Hatsopoulos, Nicholas G

2018-04-01

The development of coordinated reach-to-grasp movement has been well studied in infants and children. However, the role of motor cortex during this development is unclear because it is difficult to study in humans. We took the approach of using a brain-machine interface (BMI) paradigm in rhesus macaques with prior therapeutic amputations to examine the emergence of novel, coordinated reach to grasp. Previous research has shown that after amputation, the cortical area previously involved in the control of the lost limb undergoes reorganization, but prior BMI work has largely relied on finding neurons that already encode specific movement-related information. In this study, we taught macaques to cortically control a robotic arm and hand through operant conditioning, using neurons that were not explicitly reach or grasp related. Over the course of training, stereotypical patterns emerged and stabilized in the cross-covariance between the reaching and grasping velocity profiles, between pairs of neurons involved in controlling reach and grasp, and to a comparable, but lesser, extent between other stable neurons in the network. In fact, we found evidence of this structured coordination between pairs composed of all combinations of neurons decoding reach or grasp and other stable neurons in the network. The degree of and participation in coordination was highly correlated across all pair types. Our approach provides a unique model for studying the development of novel, coordinated reach-to-grasp movement at the behavioral and cortical levels. NEW & NOTEWORTHY Given that motor cortex undergoes reorganization after amputation, our work focuses on training nonhuman primates with chronic amputations to use neurons that are not reach or grasp related to control a robotic arm to reach to grasp through the use of operant conditioning, mimicking early development. We studied the development of a novel, coordinated behavior at the behavioral and cortical level, and the neural plasticity in M1 associated with learning to use a brain-machine interface.