Dopaminergic dysfunction in schizophrenia: salience attribution revisited.

PubMed

Heinz, Andreas; Schlagenhauf, Florian

2010-05-01

A dysregulation of the mesolimbic dopamine system in schizophrenia patients may lead to aberrant attribution of incentive salience and contribute to the emergence of psychopathological symptoms like delusions. The dopaminergic signal has been conceptualized to represent a prediction error that indicates the difference between received and predicted reward. The incentive salience hypothesis states that dopamine mediates the attribution of "incentive salience" to conditioned cues that predict reward. This hypothesis was initially applied in the context of drug addiction and then transferred to schizophrenic psychosis. It was hypothesized that increased firing (chaotic or stress associated) of dopaminergic neurons in the striatum of schizophrenia patients attributes incentive salience to otherwise irrelevant stimuli. Here, we review recent neuroimaging studies directly addressing this hypothesis. They suggest that neuronal functions associated with dopaminergic signaling, such as the attribution of salience to reward-predicting stimuli and the computation of prediction errors, are indeed altered in schizophrenia patients and that this impairment appears to contribute to delusion formation.

Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

NASA Astrophysics Data System (ADS)

Dasgupta, Hirak

2016-12-01

The objective of this work is to make a neural network function approximation model to detect human age from the electrocardiogram (ECG) signal. The input vectors of the neural network are the Katz fractal dimension of the ECG signal, frequencies in the QRS complex, male or female (represented by numeric constant) and the average of successive R-R peak distance of a particular ECG signal. The QRS complex has been detected by short time Fourier transform algorithm. The successive R peak has been detected by, first cutting the signal into periods by auto-correlation method and then finding the absolute of the highest point in each period. The neural network used in this problem consists of two layers, with Sigmoid neuron in the input and linear neuron in the output layer. The result shows the mean of errors as -0.49, 1.03, 0.79 years and the standard deviation of errors as 1.81, 1.77, 2.70 years during training, cross validation and testing with unknown data sets, respectively.

Dopaminergic control of motivation and reinforcement learning: a closed-circuit account for reward-oriented behavior.

PubMed

Morita, Kenji; Morishima, Mieko; Sakai, Katsuyuki; Kawaguchi, Yasuo

2013-05-15

Humans and animals take actions quickly when they expect that the actions lead to reward, reflecting their motivation. Injection of dopamine receptor antagonists into the striatum has been shown to slow such reward-seeking behavior, suggesting that dopamine is involved in the control of motivational processes. Meanwhile, neurophysiological studies have revealed that phasic response of dopamine neurons appears to represent reward prediction error, indicating that dopamine plays central roles in reinforcement learning. However, previous attempts to elucidate the mechanisms of these dopaminergic controls have not fully explained how the motivational and learning aspects are related and whether they can be understood by the way the activity of dopamine neurons itself is controlled by their upstream circuitries. To address this issue, we constructed a closed-circuit model of the corticobasal ganglia system based on recent findings regarding intracortical and corticostriatal circuit architectures. Simulations show that the model could reproduce the observed distinct motivational effects of D1- and D2-type dopamine receptor antagonists. Simultaneously, our model successfully explains the dopaminergic representation of reward prediction error as observed in behaving animals during learning tasks and could also explain distinct choice biases induced by optogenetic stimulation of the D1 and D2 receptor-expressing striatal neurons. These results indicate that the suggested roles of dopamine in motivational control and reinforcement learning can be understood in a unified manner through a notion that the indirect pathway of the basal ganglia represents the value of states/actions at a previous time point, an empirically driven key assumption of our model.

Predictive Coding of Dynamical Variables in Balanced Spiking Networks

PubMed Central

Boerlin, Martin; Machens, Christian K.; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated. PMID:24244113

Stimulus-Response-Outcome Coding in the Pigeon Nidopallium Caudolaterale

PubMed Central

Starosta, Sarah; Güntürkün, Onur; Stüttgen, Maik C.

2013-01-01

A prerequisite for adaptive goal-directed behavior is that animals constantly evaluate action outcomes and relate them to both their antecedent behavior and to stimuli predictive of reward or non-reward. Here, we investigate whether single neurons in the avian nidopallium caudolaterale (NCL), a multimodal associative forebrain structure and a presumed analogue of mammalian prefrontal cortex, represent information useful for goal-directed behavior. We subjected pigeons to a go-nogo task, in which responding to one visual stimulus (S+) was partially reinforced, responding to another stimulus (S–) was punished, and responding to test stimuli from the same physical dimension (spatial frequency) was inconsequential. The birds responded most intensely to S+, and their response rates decreased monotonically as stimuli became progressively dissimilar to S+; thereby, response rates provided a behavioral index of reward expectancy. We found that many NCL neurons' responses were modulated in the stimulus discrimination phase, the outcome phase, or both. A substantial fraction of neurons increased firing for cues predicting non-reward or decreased firing for cues predicting reward. Interestingly, the same neurons also responded when reward was expected but not delivered, and could thus provide a negative reward prediction error or, alternatively, signal negative value. In addition, many cells showed motor-related response modulation. In summary, NCL neurons represent information about the reward value of specific stimuli, instrumental actions as well as action outcomes, and therefore provide signals useful for adaptive behavior in dynamically changing environments. PMID:23437383

Hedging Your Bets by Learning Reward Correlations in the Human Brain

PubMed Central

Wunderlich, Klaus; Symmonds, Mkael; Bossaerts, Peter; Dolan, Raymond J.

2011-01-01

Summary Human subjects are proficient at tracking the mean and variance of rewards and updating these via prediction errors. Here, we addressed whether humans can also learn about higher-order relationships between distinct environmental outcomes, a defining ecological feature of contexts where multiple sources of rewards are available. By manipulating the degree to which distinct outcomes are correlated, we show that subjects implemented an explicit model-based strategy to learn the associated outcome correlations and were adept in using that information to dynamically adjust their choices in a task that required a minimization of outcome variance. Importantly, the experimentally generated outcome correlations were explicitly represented neuronally in right midinsula with a learning prediction error signal expressed in rostral anterior cingulate cortex. Thus, our data show that the human brain represents higher-order correlation structures between rewards, a core adaptive ability whose immediate benefit is optimized sampling. PMID:21943609

Expectancy-related changes in firing of dopamine neurons depend on orbitofrontal cortex.

PubMed

Takahashi, Yuji K; Roesch, Matthew R; Wilson, Robert C; Toreson, Kathy; O'Donnell, Patricio; Niv, Yael; Schoenbaum, Geoffrey

2011-10-30

The orbitofrontal cortex has been hypothesized to carry information regarding the value of expected rewards. Such information is essential for associative learning, which relies on comparisons between expected and obtained reward for generating instructive error signals. These error signals are thought to be conveyed by dopamine neurons. To test whether orbitofrontal cortex contributes to these error signals, we recorded from dopamine neurons in orbitofrontal-lesioned rats performing a reward learning task. Lesions caused marked changes in dopaminergic error signaling. However, the effect of lesions was not consistent with a simple loss of information regarding expected value. Instead, without orbitofrontal input, dopaminergic error signals failed to reflect internal information about the impending response that distinguished externally similar states leading to differently valued future rewards. These results are consistent with current conceptualizations of orbitofrontal cortex as supporting model-based behavior and suggest an unexpected role for this information in dopaminergic error signaling.

Bayesian decoding using unsorted spikes in the rat hippocampus

PubMed Central

Layton, Stuart P.; Chen, Zhe; Wilson, Matthew A.

2013-01-01

A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces. PMID:24089403

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

Detection and clustering of features in aerial images by neuron network-based algorithm

NASA Astrophysics Data System (ADS)

Vozenilek, Vit

2015-12-01

The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.

Double dissociation of value computations in orbitofrontal and anterior cingulate neurons

PubMed Central

Kennerley, Steven W.; Behrens, Timothy E. J.; Wallis, Jonathan D.

2011-01-01

Damage to prefrontal cortex (PFC) impairs decision-making, but the underlying value computations that might cause such impairments remain unclear. Here we report that value computations are doubly dissociable within PFC neurons. While many PFC neurons encoded chosen value, they used opponent encoding schemes such that averaging the neuronal population eliminated value coding. However, a special population of neurons in anterior cingulate cortex (ACC) - but not orbitofrontal cortex (OFC) - multiplex chosen value across decision parameters using a unified encoding scheme, and encoded reward prediction errors. In contrast, neurons in OFC - but not ACC - encoded chosen value relative to the recent history of choice values. Together, these results suggest complementary valuation processes across PFC areas: OFC neurons dynamically evaluate current choices relative to recent choice values, while ACC neurons encode choice predictions and prediction errors using a common valuation currency reflecting the integration of multiple decision parameters. PMID:22037498

Effect of electrical coupling on ionic current and synaptic potential measurements.

PubMed

Rabbah, Pascale; Golowasch, Jorge; Nadim, Farzan

2005-07-01

Recent studies have found electrical coupling to be more ubiquitous than previously thought, and coupling through gap junctions is known to play a crucial role in neuronal function and network output. In particular, current spread through gap junctions may affect the activation of voltage-dependent conductances as well as chemical synaptic release. Using voltage-clamp recordings of two strongly electrically coupled neurons of the lobster stomatogastric ganglion and conductance-based models of these neurons, we identified effects of electrical coupling on the measurement of leak and voltage-gated outward currents, as well as synaptic potentials. Experimental measurements showed that both leak and voltage-gated outward currents are recruited by gap junctions from neurons coupled to the clamped cell. Nevertheless, in spite of the strong coupling between these neurons, the errors made in estimating voltage-gated conductance parameters were relatively minor (<10%). Thus in many cases isolation of coupled neurons may not be required if a small degree of measurement error of the voltage-gated currents or the synaptic potentials is acceptable. Modeling results show, however, that such errors may be as high as 20% if the gap-junction position is near the recording site or as high as 90% when measuring smaller voltage-gated ionic currents. Paradoxically, improved space clamp increases the errors arising from electrical coupling because voltage control across gap junctions is poor for even the highest realistic coupling conductances. Furthermore, the common procedure of leak subtraction can add an extra error to the conductance measurement, the sign of which depends on the maximal conductance.

A neural theory of visual attention and short-term memory (NTVA).

PubMed

Bundesen, Claus; Habekost, Thomas; Kyllingsbæk, Søren

2011-05-01

The neural theory of visual attention and short-term memory (NTVA) proposed by Bundesen, Habekost, and Kyllingsbæk (2005) is reviewed. In NTVA, filtering (selection of objects) changes the number of cortical neurons in which an object is represented so that this number increases with the behavioural importance of the object. Another mechanism of selection, pigeonholing (selection of features), scales the level of activation in neurons coding for a particular feature. By these mechanisms, behaviourally important objects and features are likely to win the competition to become encoded into visual short-term memory (VSTM). The VSTM system is conceived as a feedback mechanism that sustains activity in the neurons that have won the attentional competition. NTVA accounts both for a wide range of attentional effects in human performance (reaction times and error rates) and a wide range of effects observed in firing rates of single cells in the primate visual system. Copyright © 2010 Elsevier Ltd. All rights reserved.

Lateral habenula neurons signal errors in the prediction of reward information

PubMed Central

Bromberg-Martin, Ethan S.; Hikosaka, Okihide

2011-01-01

Humans and animals have a remarkable ability to predict future events, which they achieve by persistently searching their environment for sources of predictive information. Yet little is known about the neural systems that motivate this behavior. We hypothesized that information-seeking is assigned value by the same circuits that support reward-seeking, so that neural signals encoding conventional “reward prediction errors” include analogous “information prediction errors”. To test this we recorded from neurons in the lateral habenula, a nucleus which encodes reward prediction errors, while monkeys chose between cues that provided different amounts of information about upcoming rewards. We found that a subpopulation of lateral habenula neurons transmitted signals resembling information prediction errors, responding when reward information was unexpectedly cued, delivered, or denied. Their signals evaluated information sources reliably even when the animal’s decisions did not. These neurons could provide a common instructive signal for reward-seeking and information-seeking behavior. PMID:21857659

Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI.

PubMed

Wang, Jun; Breen, Daniel; Akinin, Abraham; Broccard, Frederic; Abarbanel, Henry D I; Cauwenberghs, Gert

2017-12-01

Representing the biophysics of neuronal dynamics and behavior offers a principled analysis-by-synthesis approach toward understanding mechanisms of nervous system functions. We report on a set of procedures assimilating and emulating neurobiological data on a neuromorphic very large scale integrated (VLSI) circuit. The analog VLSI chip, NeuroDyn, features 384 digitally programmable parameters specifying for 4 generalized Hodgkin-Huxley neurons coupled through 12 conductance-based chemical synapses. The parameters also describe reversal potentials, maximal conductances, and spline regressed kinetic functions for ion channel gating variables. In one set of experiments, we assimilated membrane potential recorded from one of the neurons on the chip to the model structure upon which NeuroDyn was designed using the known current input sequence. We arrived at the programmed parameters except for model errors due to analog imperfections in the chip fabrication. In a related set of experiments, we replicated songbird individual neuron dynamics on NeuroDyn by estimating and configuring parameters extracted using data assimilation from intracellular neural recordings. Faithful emulation of detailed biophysical neural dynamics will enable the use of NeuroDyn as a tool to probe electrical and molecular properties of functional neural circuits. Neuroscience applications include studying the relationship between molecular properties of neurons and the emergence of different spike patterns or different brain behaviors. Clinical applications include studying and predicting effects of neuromodulators or neurodegenerative diseases on ion channel kinetics.

Dopamine prediction errors in reward learning and addiction: from theory to neural circuitry

PubMed Central

Keiflin, Ronald; Janak, Patricia H.

2015-01-01

Summary Midbrain dopamine (DA) neurons are proposed to signal reward prediction error (RPE), a fundamental parameter in associative learning models. This RPE hypothesis provides a compelling theoretical framework for understanding DA function in reward learning and addiction. New studies support a causal role for DA-mediated RPE activity in promoting learning about natural reward; however, this question has not been explicitly tested in the context of drug addiction. In this review, we integrate theoretical models with experimental findings on the activity of DA systems, and on the causal role of specific neuronal projections and cell types, to provide a circuit-based framework for probing DA-RPE function in addiction. By examining error-encoding DA neurons in the neural network in which they are embedded, hypotheses regarding circuit-level adaptations that possibly contribute to pathological error-signaling and addiction can be formulated and tested. PMID:26494275

How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks

PubMed Central

Rombouts, Jaldert O.; Bohte, Sander M.; Roelfsema, Pieter R.

2015-01-01

Intelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory delays. This learning process is not well understood. Here we develop a biologically plausible learning scheme that explains how trial-and-error learning induces neuronal selectivity and working memory representations for task-relevant information. We propose that the response selection stage sends attentional feedback signals to earlier processing levels, forming synaptic tags at those connections responsible for the stimulus-response mapping. Globally released neuromodulators then interact with tagged synapses to determine their plasticity. The resulting learning rule endows neural networks with the capacity to create new working memory representations of task relevant information as persistent activity. It is remarkably generic: it explains how association neurons learn to store task-relevant information for linear as well as non-linear stimulus-response mappings, how they become tuned to category boundaries or analog variables, depending on the task demands, and how they learn to integrate probabilistic evidence for perceptual decisions. PMID:25742003

Pathogenic cascades in lysosomal disease-Why so complex?

PubMed

Walkley, S U

2009-04-01

Lysosomal disease represents a large group of more than 50 clinically recognized conditions resulting from inborn errors of metabolism affecting the organelle known as the lysosome. The lysosome is an integral part of the larger endosomal/lysosomal system, and is closely allied with the ubiquitin-proteosomal and autophagosomal systems, which together comprise essential cell machinery for substrate degradation and recycling, homeostatic control, and signalling. More than two-thirds of lysosomal diseases affect the brain, with neurons appearing particularly vulnerable to lysosomal compromise and showing diverse consequences ranging from specific axonal and dendritic abnormalities to neuron death. While failure of lysosomal function characteristically leads to lysosomal storage, new studies argue that lysosomal diseases may also be appropriately viewed as 'states of deficiency' rather than simply overabundance (storage). Interference with signalling events and salvage processing normally controlled by the endosomal/lysosomal system may represent key mechanisms accounting for the inherent complexity of lysosomal disorders. Analysis of lysosomal disease pathogenesis provides a unique window through which to observe the importance of the greater lysosomal system for normal cell health.

Development of a neuromorphic control system for a lightweight humanoid robot

NASA Astrophysics Data System (ADS)

Folgheraiter, Michele; Keldibek, Amina; Aubakir, Bauyrzhan; Salakchinov, Shyngys; Gini, Giuseppina; Mauro Franchi, Alessio; Bana, Matteo

2017-03-01

A neuromorphic control system for a lightweight middle size humanoid biped robot built using 3D printing techniques is proposed. The control architecture consists of different modules capable to learn and autonomously reproduce complex periodic trajectories. Each module is represented by a chaotic Recurrent Neural Network (RNN) with a core of dynamic neurons randomly and sparsely connected with fixed synapses. A set of read-out units with adaptable synapses realize a linear combination of the neurons output in order to reproduce the target signals. Different experiments were conducted to find out the optimal initialization for the RNN’s parameters. From simulation results, using normalized signals obtained from the robot model, it was proven that all the instances of the control module can learn and reproduce the target trajectories with an average RMS error of 1.63 and variance 0.74.

A Neural Circuit Mechanism for the Involvements of Dopamine in Effort-Related Choices: Decay of Learned Values, Secondary Effects of Depletion, and Calculation of Temporal Difference Error

PubMed Central

2018-01-01

Abstract Dopamine has been suggested to be crucially involved in effort-related choices. Key findings are that dopamine depletion (i) changed preference for a high-cost, large-reward option to a low-cost, small-reward option, (ii) but not when the large-reward option was also low-cost or the small-reward option gave no reward, (iii) while increasing the latency in all the cases but only transiently, and (iv) that antagonism of either dopamine D1 or D2 receptors also specifically impaired selection of the high-cost, large-reward option. The underlying neural circuit mechanisms remain unclear. Here we show that findings i–iii can be explained by the dopaminergic representation of temporal-difference reward-prediction error (TD-RPE), whose mechanisms have now become clarified, if (1) the synaptic strengths storing the values of actions mildly decay in time and (2) the obtained-reward-representing excitatory input to dopamine neurons increases after dopamine depletion. The former is potentially caused by background neural activity–induced weak synaptic plasticity, and the latter is assumed to occur through post-depletion increase of neural activity in the pedunculopontine nucleus, where neurons representing obtained reward exist and presumably send excitatory projections to dopamine neurons. We further show that finding iv, which is nontrivial given the suggested distinct functions of the D1 and D2 corticostriatal pathways, can also be explained if we additionally assume a proposed mechanism of TD-RPE calculation, in which the D1 and D2 pathways encode the values of actions with a temporal difference. These results suggest a possible circuit mechanism for the involvements of dopamine in effort-related choices and, simultaneously, provide implications for the mechanisms of TD-RPE calculation. PMID:29468191

Internally-generated error signals in monkey frontal eye field during an inferred motion task

PubMed Central

Ferrera, Vincent P.; Barborica, Andrei

2010-01-01

An internal model for predictive saccades in frontal cortex was investigated by recording neurons in monkey frontal eye field during an inferred motion task. Monkeys were trained to make saccades to the extrapolated position of a small moving target that was rendered temporarily invisible and whose trajectory was altered. On roughly two-thirds of the trials, monkeys made multiple saccades while the target was invisible. Primary saccades were correlated with extrapolated target position. Secondary saccades significantly reduced residual errors resulting from imperfect accuracy of the first saccade. These observations suggest that the second saccade was corrective. As there was no visual feedback, corrective saccades could only be driven by an internally generated error signal. Neuronal activity in the frontal eye field was directionally tuned prior to both primary and secondary saccades. Separate subpopulations of cells encoded either saccade direction or direction error prior to the second saccade. These results suggest that FEF neurons encode the error after the first saccade, as well as the direction of the second saccade. Hence, FEF appears to contribute to detecting and correcting movement errors based on internally generated signals. PMID:20810882

Disrupted Prediction Error Links Excessive Amygdala Activation to Excessive Fear.

PubMed

Sengupta, Auntora; Winters, Bryony; Bagley, Elena E; McNally, Gavan P

2016-01-13

Basolateral amygdala (BLA) is critical for fear learning, and its heightened activation is widely thought to underpin a variety of anxiety disorders. Here we used chemogenetic techniques in rats to study the consequences of heightened BLA activation for fear learning and memory, and to specifically identify a mechanism linking increased activity of BLA glutamatergic neurons to aberrant fear. We expressed the excitatory hM3Dq DREADD in rat BLA glutamatergic neurons and showed that CNO acted selectively to increase their activity, depolarizing these neurons and increasing their firing rates. This chemogenetic excitation of BLA glutamatergic neurons had no effect on the acquisition of simple fear learning, regardless of whether this learning led to a weak or strong fear memory. However, in an associative blocking task, chemogenetic excitation of BLA glutamatergic neurons yielded significant learning to a blocked conditioned stimulus, which otherwise should not have been learned about. Moreover, in an overexpectation task, chemogenetic manipulation of BLA glutamatergic neurons prevented use of negative prediction error to reduce fear learning, leading to significant impairments in fear inhibition. These effects were not attributable to the chemogenetic manipulation enhancing arousal, increasing asymptotic levels of fear learning or fear memory consolidation. Instead, chemogenetic excitation of BLA glutamatergic neurons disrupted use of prediction error to regulate fear learning. Several neuropsychiatric disorders are characterized by heightened activation of the amygdala. This heightened activation has been hypothesized to underlie increased emotional reactivity, fear over generalization, and deficits in fear inhibition. Yet the mechanisms linking heightened amygdala activation to heightened emotional learning are elusive. Here we combined chemogenetic excitation of rat basolateral amygdala glutamatergic neurons with a variety of behavioral approaches to show that, although simple fear learning is unaffected, the use of prediction error to regulate this learning is profoundly disrupted, leading to formation of inappropriate fear associations and impaired fear inhibition. Copyright © 2016 the authors 0270-6474/16/360385-11$15.00/0.

A calibration-free electrode compensation method

PubMed Central

Rossant, Cyrille; Fontaine, Bertrand; Magnusson, Anna K.

2012-01-01

In a single-electrode current-clamp recording, the measured potential includes both the response of the membrane and that of the measuring electrode. The electrode response is traditionally removed using bridge balance, where the response of an ideal resistor representing the electrode is subtracted from the measurement. Because the electrode is not an ideal resistor, this procedure produces capacitive transients in response to fast or discontinuous currents. More sophisticated methods exist, but they all require a preliminary calibration phase, to estimate the properties of the electrode. If these properties change after calibration, the measurements are corrupted. We propose a compensation method that does not require preliminary calibration. Measurements are compensated offline by fitting a model of the neuron and electrode to the trace and subtracting the predicted electrode response. The error criterion is designed to avoid the distortion of compensated traces by spikes. The technique allows electrode properties to be tracked over time and can be extended to arbitrary models of electrode and neuron. We demonstrate the method using biophysical models and whole cell recordings in cortical and brain-stem neurons. PMID:22896724

Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia.

PubMed

Diuk, Carlos; Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew; Niv, Yael

2013-03-27

Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.

Visual short-term memory capacity for simple and complex objects.

PubMed

Luria, Roy; Sessa, Paola; Gotler, Alex; Jolicoeur, Pierre; Dell'Acqua, Roberto

2010-03-01

Does the capacity of visual short-term memory (VSTM) depend on the complexity of the objects represented in memory? Although some previous findings indicated lower capacity for more complex stimuli, other results suggest that complexity effects arise during retrieval (due to errors in the comparison process with what is in memory) that is not related to storage limitations of VSTM, per se. We used ERPs to track neuronal activity specifically related to retention in VSTM by measuring the sustained posterior contralateral negativity during a change detection task (which required detecting if an item was changed between a memory and a test array). The sustained posterior contralateral negativity, during the retention interval, was larger for complex objects than for simple objects, suggesting that neurons mediating VSTM needed to work harder to maintain more complex objects. This, in turn, is consistent with the view that VSTM capacity depends on complexity.

Human Temporal Cortical Single Neuron Activity During Working Memory Maintenance

PubMed Central

Zamora, Leona; Corina, David; Ojemann, George

2016-01-01

The Working Memory model of human memory, first introduced by Baddeley and Hitch (1974), has been one of the most influential psychological constructs in cognitive psychology and human neuroscience. However the neuronal correlates of core components of this model have yet to be fully elucidated. Here we present data from two studies where human temporal cortical single neuron activity was recorded during tasks differentially affecting the maintenance component of verbal working memory. In Study One we vary the presence or absence of distracting items for the entire period of memory storage. In Study Two we vary the duration of storage so that distractors filled all, or only one-third of the time the memory was stored. Extracellular single neuron recordings were obtained from 36 subjects undergoing awake temporal lobe resections for epilepsy, 25 in Study one, 11 in Study two. Recordings were obtained from a total of 166 lateral temporal cortex neurons during performance of one of these two tasks, 86 study one, 80 study two. Significant changes in activity with distractor manipulation were present in 74 of these neurons (45%), 38 Study one, 36 Study two. In 48 (65%) of those there was increased activity during the period when distracting items were absent, 26 Study One, 22 Study Two. The magnitude of this increase was greater for Study One, 47.6%, than Study Two, 8.1%, paralleling the reduction in memory errors in the absence of distracters, for Study One of 70.3%, Study Two 26.3% These findings establish that human lateral temporal cortex is part of the neural system for working memory, with activity during maintenance of that memory that parallels performance, suggesting it represents active rehearsal. In 31 of these neurons (65%) this activity was an extension of that during working memory encoding that differed significantly from the neural processes recorded during overt and silent language tasks without a recent memory component, 17 Study one, 14 Study two. Contrary to the Baddeley model, that activity during verbal working memory maintenance often represented activity specific to working memory rather than speech or language. PMID:27059210

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

Gamma Spectroscopy by Artificial Neural Network Coupled with MCNP

NASA Astrophysics Data System (ADS)

Sahiner, Huseyin

While neutron activation analysis is widely used in many areas, sensitivity of the analysis depends on how the analysis is conducted. Even though the sensitivity of the techniques carries error, compared to chemical analysis, its range is in parts per million or sometimes billion. Due to this sensitivity, the use of neutron activation analysis becomes important when analyzing bio-samples. Artificial neural network is an attractive technique for complex systems. Although there are neural network applications on spectral analysis, training by simulated data to analyze experimental data has not been made. This study offers an improvement on spectral analysis and optimization on neural network for the purpose. The work considers five elements that are considered as trace elements for bio-samples. However, the system is not limited to five elements. The only limitation of the study comes from data library availability on MCNP. A perceptron network was employed to identify five elements from gamma spectra. In quantitative analysis, better results were obtained when the neural fitting tool in MATLAB was used. As a training function, Levenberg-Marquardt algorithm was used with 23 neurons in the hidden layer with 259 gamma spectra in the input. Because the interest of the study deals with five elements, five neurons representing peak counts of five isotopes in the input layer were used. Five output neurons revealed mass information of these elements from irradiated kidney stones. Results showing max error of 17.9% in APA, 24.9% in UA, 28.2% in COM, 27.9% in STRU type showed the success of neural network approach in analyzing gamma spectra. This high error was attributed to Zn that has a very long decay half-life compared to the other elements. The simulation and experiments were made under certain experimental setup (3 hours irradiation, 96 hours decay time, 8 hours counting time). Nevertheless, the approach is subject to be generalized for different setups.

Hierarchical Learning Induces Two Simultaneous, But Separable, Prediction Errors in Human Basal Ganglia

PubMed Central

Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew

2013-01-01

Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously. PMID:23536092

Synchronization of coupled different chaotic FitzHugh-Nagumo neurons with unknown parameters under communication-direction-dependent coupling.

PubMed

Iqbal, Muhammad; Rehan, Muhammad; Khaliq, Abdul; Saeed-ur-Rehman; Hong, Keum-Shik

2014-01-01

This paper investigates the chaotic behavior and synchronization of two different coupled chaotic FitzHugh-Nagumo (FHN) neurons with unknown parameters under external electrical stimulation (EES). The coupled FHN neurons of different parameters admit unidirectional and bidirectional gap junctions in the medium between them. Dynamical properties, such as the increase in synchronization error as a consequence of the deviation of neuronal parameters for unlike neurons, the effect of difference in coupling strengths caused by the unidirectional gap junctions, and the impact of large time-delay due to separation of neurons, are studied in exploring the behavior of the coupled system. A novel integral-based nonlinear adaptive control scheme, to cope with the infeasibility of the recovery variable, for synchronization of two coupled delayed chaotic FHN neurons of different and unknown parameters under uncertain EES is derived. Further, to guarantee robust synchronization of different neurons against disturbances, the proposed control methodology is modified to achieve the uniformly ultimately bounded synchronization. The parametric estimation errors can be reduced by selecting suitable control parameters. The effectiveness of the proposed control scheme is illustrated via numerical simulations.

Neuronal Reward and Decision Signals: From Theories to Data

PubMed Central

Schultz, Wolfram

2015-01-01

Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act. PMID:26109341

Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons.

PubMed

Keysers, Christian; Perrett, David I; Gazzola, Valeria

2014-04-01

Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization.

Dimension Reduction With Extreme Learning Machine.

PubMed

Kasun, Liyanaarachchi Lekamalage Chamara; Yang, Yan; Huang, Guang-Bin; Zhang, Zhengyou

2016-08-01

Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.

Hebbian Learning is about contingency not contiguity and explains the emergence of predictive mirror neurons

PubMed Central

Keysers, Christian; Perrett, David I.; Gazzola, Valeria

2015-01-01

Hebbian Learning should not be reduced to contiguity since it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: through Hebbian Learning mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization. PMID:24775162

Diversity and Homogeneity in Responses of Midbrain Dopamine Neurons

PubMed Central

Fiorillo, Christopher D.; Yun, Sora R.; Song, Minryung R.

2013-01-01

Dopamine neurons of the ventral midbrain have been found to signal a reward prediction error that can mediate positive reinforcement. Despite the demonstration of modest diversity at the cellular and molecular levels, there has been little analysis of response diversity in behaving animals. Here we examine response diversity in rhesus macaques to appetitive, aversive, and neutral stimuli having relative motivational values that were measured and controlled through a choice task. First, consistent with previous studies, we observed a continuum of response variability and an apparent absence of distinct clusters in scatter plots, suggesting a lack of statistically discrete subpopulations of neurons. Second, we found that a group of “sensitive” neurons tend to be more strongly suppressed by a variety of stimuli and to be more strongly activated by juice. Third, neurons in the “ventral tier” of substantia nigra were found to have greater suppression, and a subset of these had higher baseline firing rates and late “rebound” activation after suppression. These neurons could belong to a previously identified subgroup of dopamine neurons that express high levels of H-type cation channels but lack calbindin. Fourth, neurons further rostral exhibited greater suppression. Fifth, although we observed weak activation of some neurons by aversive stimuli, this was not associated with their aversiveness. In conclusion, we find a diversity of response properties, distributed along a continuum, within what may be a single functional population of neurons signaling reward prediction error. PMID:23486943

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

PubMed

Gilra, Aditya; Gerstner, Wulfram

2017-11-27

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

PubMed Central

Gerstner, Wulfram

2017-01-01

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically. PMID:29173280

The Representation of Prediction Error in Auditory Cortex

PubMed Central

Rubin, Jonathan; Ulanovsky, Nachum; Tishby, Naftali

2016-01-01

To survive, organisms must extract information from the past that is relevant for their future. How this process is expressed at the neural level remains unclear. We address this problem by developing a novel approach from first principles. We show here how to generate low-complexity representations of the past that produce optimal predictions of future events. We then illustrate this framework by studying the coding of ‘oddball’ sequences in auditory cortex. We find that for many neurons in primary auditory cortex, trial-by-trial fluctuations of neuronal responses correlate with the theoretical prediction error calculated from the short-term past of the stimulation sequence, under constraints on the complexity of the representation of this past sequence. In some neurons, the effect of prediction error accounted for more than 50% of response variability. Reliable predictions often depended on a representation of the sequence of the last ten or more stimuli, although the representation kept only few details of that sequence. PMID:27490251

Fast temporal neural learning using teacher forcing

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad (Inventor); Bahren, Jacob (Inventor)

1992-01-01

A neural network is trained to output a time dependent target vector defined over a predetermined time interval in response to a time dependent input vector defined over the same time interval by applying corresponding elements of the error vector, or difference between the target vector and the actual neuron output vector, to the inputs of corresponding output neurons of the network as corrective feedback. This feedback decreases the error and quickens the learning process, so that a much smaller number of training cycles are required to complete the learning process. A conventional gradient descent algorithm is employed to update the neural network parameters at the end of the predetermined time interval. The foregoing process is repeated in repetitive cycles until the actual output vector corresponds to the target vector. In the preferred embodiment, as the overall error of the neural network output decreasing during successive training cycles, the portion of the error fed back to the output neurons is decreased accordingly, allowing the network to learn with greater freedom from teacher forcing as the network parameters converge to their optimum values. The invention may also be used to train a neural network with stationary training and target vectors.

Fast temporal neural learning using teacher forcing

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad (Inventor); Bahren, Jacob (Inventor)

1995-01-01

A neural network is trained to output a time dependent target vector defined over a predetermined time interval in response to a time dependent input vector defined over the same time interval by applying corresponding elements of the error vector, or difference between the target vector and the actual neuron output vector, to the inputs of corresponding output neurons of the network as corrective feedback. This feedback decreases the error and quickens the learning process, so that a much smaller number of training cycles are required to complete the learning process. A conventional gradient descent algorithm is employed to update the neural network parameters at the end of the predetermined time interval. The foregoing process is repeated in repetitive cycles until the actual output vector corresponds to the target vector. In the preferred embodiment, as the overall error of the neural network output decreasing during successive training cycles, the portion of the error fed back to the output neurons is decreased accordingly, allowing the network to learn with greater freedom from teacher forcing as the network parameters converge to their optimum values. The invention may also be used to train a neural network with stationary training and target vectors.

Enhanced storage capacity with errors in scale-free Hopfield neural networks: An analytical study.

PubMed

Kim, Do-Hyun; Park, Jinha; Kahng, Byungnam

2017-01-01

The Hopfield model is a pioneering neural network model with associative memory retrieval. The analytical solution of the model in mean field limit revealed that memories can be retrieved without any error up to a finite storage capacity of O(N), where N is the system size. Beyond the threshold, they are completely lost. Since the introduction of the Hopfield model, the theory of neural networks has been further developed toward realistic neural networks using analog neurons, spiking neurons, etc. Nevertheless, those advances are based on fully connected networks, which are inconsistent with recent experimental discovery that the number of connections of each neuron seems to be heterogeneous, following a heavy-tailed distribution. Motivated by this observation, we consider the Hopfield model on scale-free networks and obtain a different pattern of associative memory retrieval from that obtained on the fully connected network: the storage capacity becomes tremendously enhanced but with some error in the memory retrieval, which appears as the heterogeneity of the connections is increased. Moreover, the error rates are also obtained on several real neural networks and are indeed similar to that on scale-free model networks.

Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations

PubMed Central

Horschig, Jörn M.; Zumer, Johanna M.; Bahramisharif, Ali

2014-01-01

Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with these oscillations have demonstrated effective modulation of both the oscillations and behavior. In this review, we collect evidence in favor of how hypothesis-driven methods can be used to augment cognition by optimizing cortical oscillations. We elaborate their potential usefulness for three target groups: healthy elderly, patients with attention deficit/hyperactivity disorder, and healthy young adults. We discuss the relevance of neuronal oscillations in each group and show how each of them can benefit from the manipulation of functionally-related oscillations. Further, we describe methods for manipulation of neuronal oscillations including direct brain stimulation as well as indirect task alterations. We also discuss practical considerations about the proposed techniques. In conclusion, we propose that insights from neuroscience should guide techniques to augment human cognition, which in turn can provide a better understanding of how the human brain works. PMID:25018706

Autonomous learning derived from experimental modeling of physical laws.

PubMed

Grabec, Igor

2013-05-01

This article deals with experimental description of physical laws by probability density function of measured data. The Gaussian mixture model specified by representative data and related probabilities is utilized for this purpose. The information cost function of the model is described in terms of information entropy by the sum of the estimation error and redundancy. A new method is proposed for searching the minimum of the cost function. The number of the resulting prototype data depends on the accuracy of measurement. Their adaptation resembles a self-organized, highly non-linear cooperation between neurons in an artificial NN. A prototype datum corresponds to the memorized content, while the related probability corresponds to the excitability of the neuron. The method does not include any free parameters except objectively determined accuracy of the measurement system and is therefore convenient for autonomous execution. Since representative data are generally less numerous than the measured ones, the method is applicable for a rather general and objective compression of overwhelming experimental data in automatic data-acquisition systems. Such compression is demonstrated on analytically determined random noise and measured traffic flow data. The flow over a day is described by a vector of 24 components. The set of 365 vectors measured over one year is compressed by autonomous learning to just 4 representative vectors and related probabilities. These vectors represent the flow in normal working days and weekends or holidays, while the related probabilities correspond to relative frequencies of these days. This example reveals that autonomous learning yields a new basis for interpretation of representative data and the optimal model structure. Copyright © 2012 Elsevier Ltd. All rights reserved.

Roles of Octopamine and Dopamine Neurons for Mediating Appetitive and Aversive Signals in Pavlovian Conditioning in Crickets

PubMed Central

Mizunami, Makoto; Matsumoto, Yukihisa

2017-01-01

Revealing neural systems that mediate appetite and aversive signals in associative learning is critical for understanding the brain mechanisms controlling adaptive behavior in animals. In mammals, it has been shown that some classes of dopamine neurons in the midbrain mediate prediction error signals that govern the learning process, whereas other classes of dopamine neurons control execution of learned actions. In this review, based on the results of our studies on Pavlovian conditioning in the cricket Gryllus bimaculatus and by referring to the findings in honey bees and fruit-flies, we argue that comparable aminergic systems exist in the insect brain. We found that administrations of octopamine (the invertebrate counterpart of noradrenaline) and dopamine receptor antagonists impair conditioning to associate an olfactory or visual conditioned stimulus (CS) with water or sodium chloride solution (appetitive or aversive unconditioned stimulus, US), respectively, suggesting that specific octopamine and dopamine neurons mediate appetitive and aversive signals, respectively, in conditioning in crickets. These findings differ from findings in fruit-flies. In fruit-flies, appetitive and aversive signals are mediated by different dopamine neuron subsets, suggesting diversity in neurotransmitters mediating appetitive signals in insects. We also found evidences of “blocking” and “auto-blocking” phenomena, which suggested that the prediction error, the discrepancy between actual US and predicted US, governs the conditioning in crickets and that octopamine neurons mediate prediction error signals for appetitive US. Our studies also showed that activations of octopamine and dopamine neurons are needed for the execution of an appetitive conditioned response (CR) and an aversive CR, respectively, and we, thus, proposed that these neurons mediate US prediction signals that drive appetitive and aversive CRs. Our findings suggest that the basic principles of functioning of aminergic systems in associative learning, i.e., to transmit prediction error signals for conditioning and to convey US prediction signals for execution of CR, are conserved among insects and mammals, on account of the fact that the organization of the insect brain is much simpler than that of the mammalian brain. Further investigation of aminergic systems that govern associative learning in insects should lead to a better understanding of commonalities and diversities of computational rules underlying associative learning in animals. PMID:29311961

Roles of Octopamine and Dopamine Neurons for Mediating Appetitive and Aversive Signals in Pavlovian Conditioning in Crickets.

PubMed

Mizunami, Makoto; Matsumoto, Yukihisa

2017-01-01

Revealing neural systems that mediate appetite and aversive signals in associative learning is critical for understanding the brain mechanisms controlling adaptive behavior in animals. In mammals, it has been shown that some classes of dopamine neurons in the midbrain mediate prediction error signals that govern the learning process, whereas other classes of dopamine neurons control execution of learned actions. In this review, based on the results of our studies on Pavlovian conditioning in the cricket Gryllus bimaculatus and by referring to the findings in honey bees and fruit-flies, we argue that comparable aminergic systems exist in the insect brain. We found that administrations of octopamine (the invertebrate counterpart of noradrenaline) and dopamine receptor antagonists impair conditioning to associate an olfactory or visual conditioned stimulus (CS) with water or sodium chloride solution (appetitive or aversive unconditioned stimulus, US), respectively, suggesting that specific octopamine and dopamine neurons mediate appetitive and aversive signals, respectively, in conditioning in crickets. These findings differ from findings in fruit-flies. In fruit-flies, appetitive and aversive signals are mediated by different dopamine neuron subsets, suggesting diversity in neurotransmitters mediating appetitive signals in insects. We also found evidences of "blocking" and "auto-blocking" phenomena, which suggested that the prediction error, the discrepancy between actual US and predicted US, governs the conditioning in crickets and that octopamine neurons mediate prediction error signals for appetitive US. Our studies also showed that activations of octopamine and dopamine neurons are needed for the execution of an appetitive conditioned response (CR) and an aversive CR, respectively, and we, thus, proposed that these neurons mediate US prediction signals that drive appetitive and aversive CRs. Our findings suggest that the basic principles of functioning of aminergic systems in associative learning, i.e., to transmit prediction error signals for conditioning and to convey US prediction signals for execution of CR, are conserved among insects and mammals, on account of the fact that the organization of the insect brain is much simpler than that of the mammalian brain. Further investigation of aminergic systems that govern associative learning in insects should lead to a better understanding of commonalities and diversities of computational rules underlying associative learning in animals.

Reciprocal interactions between neurons and glia are required for Drosophila peripheral nervous system development.

PubMed

Sepp, Katharine J; Auld, Vanessa J

2003-09-10

A major developmental role of peripheral glia is to mediate sensory axon guidance; however, it is not known whether sensory neurons influence peripheral glial development. To determine whether glia and neurons reciprocally interact during embryonic development, we ablated each cell type by overexpressing the apoptosis gene, grim, and observed the effects on peripheral nervous system (PNS) development. When neurons are ablated, glial defects occur as a secondary effect, and vice versa. Therefore glia and neurons are codependent during embryogenesis. To further explore glial-neuronal interactions, we genetically disrupted glial migration or differentiation and observed the secondary effects on sensory neuron development. Glial migration and ensheathment of PNS axons was blocked by overexpression of activated Rho GTPase, a regulator of actin dynamics. Here, sensory axons extended to the CNS without exhibiting gross pathfinding errors. In contrast, disrupting differentiation by expression of dominant-negative Ras GTPase in glia resulted in major sensory axon pathfinding errors, similar to those seen in glial ablations. Glial overexpression of transgenic components of the epidermal growth factor receptor (EGFR) signaling pathway yielded similar sensory neuron defects and also downregulated the expression of the glial marker Neuroglian. Mutant analysis also suggested that the EGFR ligands Spitz and Vein play roles in peripheral glial development. The observations support a model in which glia express genes necessary for sensory neuron development, and these genes are potentially under the control of the EGFR/Ras signaling pathway.

Dopamine Reward Prediction Error Responses Reflect Marginal Utility

PubMed Central

Stauffer, William R.; Lak, Armin; Schultz, Wolfram

2014-01-01

Summary Background Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. Results In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions’ shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. Conclusions These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). PMID:25283778

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

Noise facilitation in associative memories of exponential capacity.

PubMed

Karbasi, Amin; Salavati, Amir Hesam; Shokrollahi, Amin; Varshney, Lav R

2014-11-01

Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms have allowed reliable learning and recall of an exponential number of patterns that satisfy certain subspace constraints. Although these designs correct external errors in recall, they assume neurons that compute noiselessly, in contrast to the highly variable neurons in brain regions thought to operate associatively, such as hippocampus and olfactory cortex. Here we consider associative memories with boundedly noisy internal computations and analytically characterize performance. As long as the internal noise level is below a specified threshold, the error probability in the recall phase can be made exceedingly small. More surprising, we show that internal noise improves the performance of the recall phase while the pattern retrieval capacity remains intact: the number of stored patterns does not reduce with noise (up to a threshold). Computational experiments lend additional support to our theoretical analysis. This work suggests a functional benefit to noisy neurons in biological neuronal networks.

A unified internal model theory to resolve the paradox of active versus passive self-motion sensation

PubMed Central

Angelaki, Dora E

2017-01-01

Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally generated (‘passive’) movements. However, these neurons show reduced responses during self-generated (‘active’) movements, indicating that predicted sensory consequences of motor commands cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations during passive movements remain unknown. We construct a Kalman filter that incorporates motor commands into a previously established model of optimal passive self-motion estimation. The simulated sensory error and feedback signals match experimentally measured neuronal responses during active and passive head and trunk rotations and translations. We conclude that a single sensory internal model can combine motor commands with vestibular and proprioceptive signals optimally. Thus, although neurons carrying sensory prediction error or feedback signals show attenuated modulation, the sensory cues and internal model are both engaged and critically important for accurate self-motion estimation during active head movements. PMID:29043978

Analysis of Mars Express Ionogram Data via a Multilayer Artificial Neural Network

NASA Astrophysics Data System (ADS)

Wilkinson, Collin; Potter, Arron; Palmer, Greg; Duru, Firdevs

2017-01-01

Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS), which is a low frequency radar on the Mars Express (MEX) Spacecraft, can provide electron plasma densities of the ionosphere local at the spacecraft in addition to densities obtained with remote sounding. The local electron densities are obtained, with a standard error of about 2%, by measuring the electron plasma frequencies with an electronic ruler on ionograms, which are plots of echo intensity as a function of time and frequency. This is done by using a tool created at the University of Iowa (Duru et al., 2008). This approach is time consuming due to the rapid accumulation of ionogram data. In 2013, results from an algorithm-based analysis of ionograms were reported by Andrews et al., but this method did not improve the human error. In the interest of fast, accurate data interpretation, a neural network (NN) has been created based on the Fast Artificial Neural Network C libraries. This NN consists of artificial neurons, with 4 layers of 12960, 10000, 1000 and 1 neuron(s) each, consecutively. This network was trained using 40 iterations of 1000 orbits. The algorithm-based method of Andrews et al. had a standard error of 40%, while the neural network has achieved error on the order of 20%.

Tamping Ramping: Algorithmic, Implementational, and Computational Explanations of Phasic Dopamine Signals in the Accumbens

PubMed Central

Lloyd, Kevin; Dayan, Peter

2015-01-01

Substantial evidence suggests that the phasic activity of dopamine neurons represents reinforcement learning’s temporal difference prediction error. However, recent reports of ramp-like increases in dopamine concentration in the striatum when animals are about to act, or are about to reach rewards, appear to pose a challenge to established thinking. This is because the implied activity is persistently predictable by preceding stimuli, and so cannot arise as this sort of prediction error. Here, we explore three possible accounts of such ramping signals: (a) the resolution of uncertainty about the timing of action; (b) the direct influence of dopamine over mechanisms associated with making choices; and (c) a new model of discounted vigour. Collectively, these suggest that dopamine ramps may be explained, with only minor disturbance, by standard theoretical ideas, though urgent questions remain regarding their proximal cause. We suggest experimental approaches to disentangling which of the proposed mechanisms are responsible for dopamine ramps. PMID:26699940

Differential Contributions of Ventral and Dorsal Striatum to Early and Late Phases of Cognitive Set Reconfiguration

PubMed Central

Sleezer, Brianna J.; Hayden, Benjamin Y.

2017-01-01

Flexible decision-making, a defining feature of human cognition, is typically thought of as a canonical pFC function. Recent work suggests that the striatum may participate as well; however, its role in this process is not well understood. We recorded activity of neurons in both the ventral (VS) and dorsal (DS) striatum while rhesus macaques performed a version of the Wisconsin Card Sorting Test, a classic test of flexibility. Our version of the task involved a trial-and-error phase before monkeys could identify the correct rule on each block. We observed changes in firing rate in both regions when monkeys switched rules. Specifically, VS neurons demonstrated switch-related activity early in the trial-and-error period when the rule needed to be updated, and a portion of these neurons signaled information about the switch context (i.e., whether the switch was intradimensional or extradimensional). Neurons in both VS and DS demonstrated switch-related activity at the end of the trial-and-error period, immediately before the rule was fully established and maintained, but these signals did not carry any information about switch context. We also observed associative learning signals (i.e., specific responses to options associated with rewards in the presentation period before choice) that followed the same pattern as switch signals (early in VS, later in DS). Taken together, these results endorse the idea that the striatum participates directly in cognitive set reconfiguration and suggest that single neurons in the striatum may contribute to a functional handoff from the VS to the DS during reconfiguration processes. PMID:27417204

Dopamine reward prediction error responses reflect marginal utility.

PubMed

Stauffer, William R; Lak, Armin; Schultz, Wolfram

2014-11-03

Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions' shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Numerical Analysis of Modeling Based on Improved Elman Neural Network

PubMed Central

Jie, Shao

2014-01-01

A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance. PMID:25054172

On the capacity of ternary Hebbian networks

NASA Technical Reports Server (NTRS)

Baram, Yoram

1991-01-01

Networks of ternary neurons storing random vectors over the set -1,0,1 by the so-called Hebbian rule are considered. It is shown that the maximal number of stored patterns that are equilibrium states of the network with probability tending to one as N tends to infinity is at least on the order of (N exp 2-1/alpha)/K, where N is the number of neurons, K is the number of nonzero elements in a pattern, and t = alpha x K, alpha between 1/2 and 1, is the threshold in the neuron function. While, for small K, this bound is similar to that obtained for fully connected binary networks, the number of interneural connections required in the ternary case is considerably smaller. Similar bounds, incorporating error probabilities, are shown to guarantee, in the same probabilistic sense, the correction of errors in the nonzero elements and in the location of these elements.

Neural correlates of sensory prediction errors in monkeys: evidence for internal models of voluntary self-motion in the cerebellum.

PubMed

Cullen, Kathleen E; Brooks, Jessica X

2015-02-01

During self-motion, the vestibular system makes essential contributions to postural stability and self-motion perception. To ensure accurate perception and motor control, it is critical to distinguish between vestibular sensory inputs that are the result of externally applied motion (exafference) and that are the result of our own actions (reafference). Indeed, although the vestibular sensors encode vestibular afference and reafference with equal fidelity, neurons at the first central stage of sensory processing selectively encode vestibular exafference. The mechanism underlying this reafferent suppression compares the brain's motor-based expectation of sensory feedback with the actual sensory consequences of voluntary self-motion, effectively computing the sensory prediction error (i.e., exafference). It is generally thought that sensory prediction errors are computed in the cerebellum, yet it has been challenging to explicitly demonstrate this. We have recently addressed this question and found that deep cerebellar nuclei neurons explicitly encode sensory prediction errors during self-motion. Importantly, in everyday life, sensory prediction errors occur in response to changes in the effector or world (muscle strength, load, etc.), as well as in response to externally applied sensory stimulation. Accordingly, we hypothesize that altering the relationship between motor commands and the actual movement parameters will result in the updating in the cerebellum-based computation of exafference. If our hypothesis is correct, under these conditions, neuronal responses should initially be increased--consistent with a sudden increase in the sensory prediction error. Then, over time, as the internal model is updated, response modulation should decrease in parallel with a reduction in sensory prediction error, until vestibular reafference is again suppressed. The finding that the internal model predicting the sensory consequences of motor commands adapts for new relationships would have important implications for understanding how responses to passive stimulation endure despite the cerebellum's ability to learn new relationships between motor commands and sensory feedback.

Temporal filtering of reward signals in the dorsal anterior cingulate cortex during a mixed-strategy game

PubMed Central

Seo, Hyojung; Lee, Daeyeol

2008-01-01

The process of decision making in humans and other animals is adaptive and can be tuned through experience so as to optimize the outcomes of their choices in a dynamic environment. Previous studies have demonstrated that the anterior cingulate cortex plays an important role in updating the animal’s behavioral strategies when the action-outcome contingencies change. Moreover, neurons in the anterior cingulate cortex often encode the signals related to expected or actual reward. We investigated whether reward-related activity in the anterior cingulate cortex is affected by the animal’s previous reward history. This was tested in rhesus monkeys trained to make binary choices in a computer-simulated competitive zero-sum game. The animal’s choice behavior was relatively close to the optimal strategy, but also revealed small but systematic biases that are consistent with the use of a reinforcement learning algorithm. In addition, the activity of neurons in the dorsal anterior cingulate cortex that was related to the reward received by the animal in a given trial was often modulated by the rewards in the previous trials. Some of these neurons encoded the rate of rewards in previous trials, whereas others displayed activity modulations more closely related to the reward prediction errors. By contrast, signals related to the animal’s choices were only weakly represented in this cortical area. These results suggest that neurons in the dorsal anterior cingulate cortex might be involved in the subjective evaluation of choice outcomes based on the animal’s reward history. PMID:17670983

Phenylethanoid glycosides of Pedicularis muscicola Maxim ameliorate high altitude-induced memory impairment.

PubMed

Zhou, Baozhu; Li, Maoxing; Cao, Xinyuan; Zhang, Quanlong; Liu, Yantong; Ma, Qiang; Qiu, Yan; Luan, Fei; Wang, Xianmin

2016-04-01

Exposure to hypobaric hypoxia causes oxidative stress, neuronal degeneration and apoptosis that leads to memory impairment. Though oxidative stress contributes to neuronal degeneration and apoptosis in hypobaric hypoxia, the ability for phenylethanoid glycosides of Pedicularis muscicola Maxim (PhGs) to reverse high altitude memory impairment has not been studied. Rats were supplemented with PhGs orally for a week. After the fourth day of drug administration, rats were exposed to a 7500 m altitude simulation in a specially designed animal decompression chamber for 3 days. Spatial memory was assessed by the 8-arm radial maze test before and after exposure to hypobaric hypoxia. Histological assessment of neuronal degeneration was performed by hematoxylin-eosin (HE) staining. Changes in oxidative stress markers and changes in the expression of the apoptotic marker, caspase-3, were assessed in the hippocampus. Our results demonstrated that after exposure to hypobaric hypoxia, PhGs ameliorated high altitude memory impairment, as shown by the decreased values obtained for reference memory error (RME), working memory error (WME), and total error (TE). Meanwhile, administration of PhGs decreased hippocampal reactive oxygen species levels and consequent lipid peroxidation by elevating reduced glutathione levels and enhancing the free radical scavenging enzyme system. There was also a decrease in the number of pyknotic neurons and a reduction in caspase-3 expression in the hippocampus. These findings suggest that PhGs may be used therapeutically to ameliorate high altitude memory impairment. Copyright © 2016 Elsevier Inc. All rights reserved.

Supervised learning with decision margins in pools of spiking neurons.

PubMed

Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre

2014-10-01

Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

Neural Representations of Personally Familiar and Unfamiliar Faces in the Anterior Inferior Temporal Cortex of Monkeys

PubMed Central

Eifuku, Satoshi; De Souza, Wania C.; Nakata, Ryuzaburo; Ono, Taketoshi; Tamura, Ryoi

2011-01-01

To investigate the neural representations of faces in primates, particularly in relation to their personal familiarity or unfamiliarity, neuronal activities were chronically recorded from the ventral portion of the anterior inferior temporal cortex (AITv) of macaque monkeys during the performance of a facial identification task using either personally familiar or unfamiliar faces as stimuli. By calculating the correlation coefficients between neuronal responses to the faces for all possible pairs of faces given in the task and then using the coefficients as neuronal population-based similarity measures between the faces in pairs, we analyzed the similarity/dissimilarity relationship between the faces, which were potentially represented by the activities of a population of the face-responsive neurons recorded in the area AITv. The results showed that, for personally familiar faces, different identities were represented by different patterns of activities of the population of AITv neurons irrespective of the view (e.g., front, 90° left, etc.), while different views were not represented independently of their facial identities, which was consistent with our previous report. In the case of personally unfamiliar faces, the faces possessing different identities but presented in the same frontal view were represented as similar, which contrasts with the results for personally familiar faces. These results, taken together, outline the neuronal representations of personally familiar and unfamiliar faces in the AITv neuronal population. PMID:21526206

Social place-cells in the bat hippocampus.

PubMed

Omer, David B; Maimon, Shir R; Las, Liora; Ulanovsky, Nachum

2018-01-12

Social animals have to know the spatial positions of conspecifics. However, it is unknown how the position of others is represented in the brain. We designed a spatial observational-learning task, in which an observer bat mimicked a demonstrator bat while we recorded hippocampal dorsal-CA1 neurons from the observer bat. A neuronal subpopulation represented the position of the other bat, in allocentric coordinates. About half of these "social place-cells" represented also the observer's own position-that is, were place cells. The representation of the demonstrator bat did not reflect self-movement or trajectory planning by the observer. Some neurons represented also the position of inanimate moving objects; however, their representation differed from the representation of the demonstrator bat. This suggests a role for hippocampal CA1 neurons in social-spatial cognition. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Distributed and Dynamic Neural Encoding of Multiple Motion Directions of Transparently Moving Stimuli in Cortical Area MT

PubMed Central

Xiao, Jianbo

2015-01-01

Segmenting visual scenes into distinct objects and surfaces is a fundamental visual function. To better understand the underlying neural mechanism, we investigated how neurons in the middle temporal cortex (MT) of macaque monkeys represent overlapping random-dot stimuli moving transparently in slightly different directions. It has been shown that the neuronal response elicited by two stimuli approximately follows the average of the responses elicited by the constituent stimulus components presented alone. In this scheme of response pooling, the ability to segment two simultaneously presented motion directions is limited by the width of the tuning curve to motion in a single direction. We found that, although the population-averaged neuronal tuning showed response averaging, subgroups of neurons showed distinct patterns of response tuning and were capable of representing component directions that were separated by a small angle—less than the tuning width to unidirectional stimuli. One group of neurons preferentially represented the component direction at a specific side of the bidirectional stimuli, weighting one stimulus component more strongly than the other. Another group of neurons pooled the component responses nonlinearly and showed two separate peaks in their tuning curves even when the average of the component responses was unimodal. We also show for the first time that the direction tuning of MT neurons evolved from initially representing the vector-averaged direction of slightly different stimuli to gradually representing the component directions. Our results reveal important neural processes underlying image segmentation and suggest that information about slightly different stimulus components is computed dynamically and distributed across neurons. SIGNIFICANCE STATEMENT Natural scenes often contain multiple entities. The ability to segment visual scenes into distinct objects and surfaces is fundamental to sensory processing and is crucial for generating the perception of our environment. Because cortical neurons are broadly tuned to a given visual feature, segmenting two stimuli that differ only slightly is a challenge for the visual system. In this study, we discovered that many neurons in the visual cortex are capable of representing individual components of slightly different stimuli by selectively and nonlinearly pooling the responses elicited by the stimulus components. We also show for the first time that the neural representation of individual stimulus components developed over a period of ∼70–100 ms, revealing a dynamic process of image segmentation. PMID:26658869

Observations of synaptic structures: origins of the neuron doctrine and its current status

PubMed Central

Guillery, R.W

2004-01-01

The neuron doctrine represents nerve cells as polarized structures that contact each other at specialized (synaptic) junctions and form the developmental, functional, structural and trophic units of nervous systems. The doctrine provided a powerful analytical tool in the past, but is now seldom used in educating neuroscientists. Early observations of, and speculations about, sites of neuronal communication, which were made in the early 1860s, almost 30 years before the neuron doctrine was developed, are presented in relation to later accounts, particularly those made in support of, or opposition to, the neuron doctrine. These markedly differing accounts are considered in relation to limitations imposed by preparative and microscopical methods, and are discussed briefly as representing a post-Darwinian, reductionist view, on the one hand, opposed to a holistic view of mankind as a special part of creation, on the other. The widely misunderstood relationship of the neuron doctrine to the cell theory is discussed, as is the degree to which the neuron doctrine is still strictly applicable to an analysis of nervous systems. Current research represents a ‘post-neuronist’ era. The neuron doctrine provided a strong analytical approach in the past, but can no longer be seen as central to contemporary advances in neuroscience. PMID:16147523

Functional differentiation of macaque visual temporal cortical neurons using a parametric action space.

PubMed

Vangeneugden, Joris; Pollick, Frank; Vogels, Rufin

2009-03-01

Neurons in the rostral superior temporal sulcus (STS) are responsive to displays of body movements. We employed a parametric action space to determine how similarities among actions are represented by visual temporal neurons and how form and motion information contributes to their responses. The stimulus space consisted of a stick-plus-point-light figure performing arm actions and their blends. Multidimensional scaling showed that the responses of temporal neurons represented the ordinal similarity between these actions. Further tests distinguished neurons responding equally strongly to static presentations and to actions ("snapshot" neurons), from those responding much less strongly to static presentations, but responding well when motion was present ("motion" neurons). The "motion" neurons were predominantly found in the upper bank/fundus of the STS, and "snapshot" neurons in the lower bank of the STS and inferior temporal convexity. Most "motion" neurons showed strong response modulation during the course of an action, thus responding to action kinematics. "Motion" neurons displayed a greater average selectivity for these simple arm actions than did "snapshot" neurons. We suggest that the "motion" neurons code for visual kinematics, whereas the "snapshot" neurons code for form/posture, and that both can contribute to action recognition, in agreement with computation models of action recognition.

Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward

PubMed Central

Kishida, Kenneth T.; Saez, Ignacio; Lohrenz, Terry; Witcher, Mark R.; Laxton, Adrian W.; Tatter, Stephen B.; White, Jason P.; Ellis, Thomas L.; Phillips, Paul E. M.; Montague, P. Read

2016-01-01

In the mammalian brain, dopamine is a critical neuromodulator whose actions underlie learning, decision-making, and behavioral control. Degeneration of dopamine neurons causes Parkinson’s disease, whereas dysregulation of dopamine signaling is believed to contribute to psychiatric conditions such as schizophrenia, addiction, and depression. Experiments in animal models suggest the hypothesis that dopamine release in human striatum encodes reward prediction errors (RPEs) (the difference between actual and expected outcomes) during ongoing decision-making. Blood oxygen level-dependent (BOLD) imaging experiments in humans support the idea that RPEs are tracked in the striatum; however, BOLD measurements cannot be used to infer the action of any one specific neurotransmitter. We monitored dopamine levels with subsecond temporal resolution in humans (n = 17) with Parkinson’s disease while they executed a sequential decision-making task. Participants placed bets and experienced monetary gains or losses. Dopamine fluctuations in the striatum fail to encode RPEs, as anticipated by a large body of work in model organisms. Instead, subsecond dopamine fluctuations encode an integration of RPEs with counterfactual prediction errors, the latter defined by how much better or worse the experienced outcome could have been. How dopamine fluctuations combine the actual and counterfactual is unknown. One possibility is that this process is the normal behavior of reward processing dopamine neurons, which previously had not been tested by experiments in animal models. Alternatively, this superposition of error terms may result from an additional yet-to-be-identified subclass of dopamine neurons. PMID:26598677

Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward.

PubMed

Kishida, Kenneth T; Saez, Ignacio; Lohrenz, Terry; Witcher, Mark R; Laxton, Adrian W; Tatter, Stephen B; White, Jason P; Ellis, Thomas L; Phillips, Paul E M; Montague, P Read

2016-01-05

In the mammalian brain, dopamine is a critical neuromodulator whose actions underlie learning, decision-making, and behavioral control. Degeneration of dopamine neurons causes Parkinson's disease, whereas dysregulation of dopamine signaling is believed to contribute to psychiatric conditions such as schizophrenia, addiction, and depression. Experiments in animal models suggest the hypothesis that dopamine release in human striatum encodes reward prediction errors (RPEs) (the difference between actual and expected outcomes) during ongoing decision-making. Blood oxygen level-dependent (BOLD) imaging experiments in humans support the idea that RPEs are tracked in the striatum; however, BOLD measurements cannot be used to infer the action of any one specific neurotransmitter. We monitored dopamine levels with subsecond temporal resolution in humans (n = 17) with Parkinson's disease while they executed a sequential decision-making task. Participants placed bets and experienced monetary gains or losses. Dopamine fluctuations in the striatum fail to encode RPEs, as anticipated by a large body of work in model organisms. Instead, subsecond dopamine fluctuations encode an integration of RPEs with counterfactual prediction errors, the latter defined by how much better or worse the experienced outcome could have been. How dopamine fluctuations combine the actual and counterfactual is unknown. One possibility is that this process is the normal behavior of reward processing dopamine neurons, which previously had not been tested by experiments in animal models. Alternatively, this superposition of error terms may result from an additional yet-to-be-identified subclass of dopamine neurons.

The TINS Lecture. The parietal association cortex in depth perception and visual control of hand action.

PubMed

Sakata, H; Taira, M; Kusunoki, M; Murata, A; Tanaka, Y

1997-08-01

Recent neurophysiological studies in alert monkeys have revealed that the parietal association cortex plays a crucial role in depth perception and visually guided hand movement. The following five classes of parietal neurons covering various aspects of these functions have been identified: (1) depth-selective visual-fixation (VF) neurons of the inferior parietal lobule (IPL), representing egocentric distance; (2) depth-movement sensitive (DMS) neurons of V5A and the ventral intraparietal (VIP) area representing direction of linear movement in 3-D space; (3) depth-rotation-sensitive (RS) neurons of V5A and the posterior parietal (PP) area representing direction of rotary movement in space; (4) visually responsive manipulation-related neurons (visual-dominant or visual-and-motor type) of the anterior intraparietal (AIP) area, representing 3-D shape or orientation (or both) of objects for manipulation; and (5) axis-orientation-selective (AOS) and surface-orientation-selective (SOS) neurons in the caudal intraparietal sulcus (cIPS) sensitive to binocular disparity and representing the 3-D orientation of the longitudinal axes and flat surfaces, respectively. Some AOS and SOS neurons are selective in both orientation and shape. Thus the dorsal visual pathway is divided into at least two subsystems, V5A, PP and VIP areas for motion vision and V6, LIP and cIPS areas for coding position and 3-D features. The cIPS sends the signals of 3-D features of objects to the AIP area, which is reciprocally connected to the ventral premotor (F5) area and plays an essential role in matching hand orientation and shaping with 3-D objects for manipulation.

A low cost, modular, and physiologically inspired electronic neuron

NASA Astrophysics Data System (ADS)

Sitt, J. D.; Campetella, F.; Aliaga, J.

2010-12-01

We describe a low cost design of an electronic neuron, which is designed to represent the dynamical properties of the membrane potential of biological neurons by modeling the states of the membrane channels. This electronic neuron can be used to study the nonlinear properties of the membrane voltage dynamics and to develop and analyze small neuronal circuits using electronic neurons as building blocks.

Optimal sparse approximation with integrate and fire neurons.

PubMed

Shapero, Samuel; Zhu, Mengchen; Hasler, Jennifer; Rozell, Christopher

2014-08-01

Sparse approximation is a hypothesized coding strategy where a population of sensory neurons (e.g. V1) encodes a stimulus using as few active neurons as possible. We present the Spiking LCA (locally competitive algorithm), a rate encoded Spiking Neural Network (SNN) of integrate and fire neurons that calculate sparse approximations. The Spiking LCA is designed to be equivalent to the nonspiking LCA, an analog dynamical system that converges on a ℓ(1)-norm sparse approximations exponentially. We show that the firing rate of the Spiking LCA converges on the same solution as the analog LCA, with an error inversely proportional to the sampling time. We simulate in NEURON a network of 128 neuron pairs that encode 8 × 8 pixel image patches, demonstrating that the network converges to nearly optimal encodings within 20 ms of biological time. We also show that when using more biophysically realistic parameters in the neurons, the gain function encourages additional ℓ(0)-norm sparsity in the encoding, relative both to ideal neurons and digital solvers.

Application of Artificial Neural Network and Response Surface Methodology in Modeling of Surface Roughness in WS2 Solid Lubricant Assisted MQL Turning of Inconel 718

NASA Astrophysics Data System (ADS)

Maheshwera Reddy Paturi, Uma; Devarasetti, Harish; Abimbola Fadare, David; Reddy Narala, Suresh Kumar

2018-04-01

In the present paper, the artificial neural network (ANN) and response surface methodology (RSM) are used in modeling of surface roughness in WS2 (tungsten disulphide) solid lubricant assisted minimal quantity lubrication (MQL) machining. The real time MQL turning of Inconel 718 experimental data considered in this paper was available in the literature [1]. In ANN modeling, performance parameters such as mean square error (MSE), mean absolute percentage error (MAPE) and average error in prediction (AEP) for the experimental data were determined based on Levenberg–Marquardt (LM) feed forward back propagation training algorithm with tansig as transfer function. The MATLAB tool box has been utilized in training and testing of neural network model. Neural network model with three input neurons, one hidden layer with five neurons and one output neuron (3-5-1 architecture) is found to be most confidence and optimal. The coefficient of determination (R2) for both the ANN and RSM model were seen to be 0.998 and 0.982 respectively. The surface roughness predictions from ANN and RSM model were related with experimentally measured values and found to be in good agreement with each other. However, the prediction efficacy of ANN model is relatively high when compared with RSM model predictions.

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.

Somato-dendritic Synaptic Plasticity and Error-backpropagation in Active Dendrites

PubMed Central

Schiess, Mathieu; Urbanczik, Robert; Senn, Walter

2016-01-01

In the last decade dendrites of cortical neurons have been shown to nonlinearly combine synaptic inputs by evoking local dendritic spikes. It has been suggested that these nonlinearities raise the computational power of a single neuron, making it comparable to a 2-layer network of point neurons. But how these nonlinearities can be incorporated into the synaptic plasticity to optimally support learning remains unclear. We present a theoretically derived synaptic plasticity rule for supervised and reinforcement learning that depends on the timing of the presynaptic, the dendritic and the postsynaptic spikes. For supervised learning, the rule can be seen as a biological version of the classical error-backpropagation algorithm applied to the dendritic case. When modulated by a delayed reward signal, the same plasticity is shown to maximize the expected reward in reinforcement learning for various coding scenarios. Our framework makes specific experimental predictions and highlights the unique advantage of active dendrites for implementing powerful synaptic plasticity rules that have access to downstream information via backpropagation of action potentials. PMID:26841235

Cardiac neuronal hierarchy in health and disease.

PubMed

Armour, J Andrew

2004-08-01

The cardiac neuronal hierarchy can be represented as a redundant control system made up of spatially distributed cell stations comprising afferent, efferent, and interconnecting neurons. Its peripheral and central neurons are in constant communication with one another such that, for the most part, it behaves as a stochastic control system. Neurons distributed throughout this hierarchy interconnect via specific linkages such that each neuronal cell station is involved in temporally dependent cardio-cardiac reflexes that control overlapping, spatially organized cardiac regions. Its function depends primarily, but not exclusively, on inputs arising from afferent neurons transducing the cardiovascular milieu to directly or indirectly (via interconnecting neurons) modify cardiac motor neurons coordinating regional cardiac behavior. As the function of the whole is greater than that of its individual parts, stable cardiac control occurs most of the time in the absence of direct cause and effect. During altered cardiac status, its redundancy normally represents a stabilizing feature. However, in the presence of regional myocardial ischemia, components within the intrinsic cardiac nervous system undergo pathological change. That, along with any consequent remodeling of the cardiac neuronal hierarchy, alters its spatially and temporally organized reflexes such that populations of neurons, acting in isolation, may destabilize efferent neuronal control of regional cardiac electrical and/or mechanical events.

Face inversion decreased information about facial identity and expression in face-responsive neurons in macaque area TE.

PubMed

Sugase-Miyamoto, Yasuko; Matsumoto, Narihisa; Ohyama, Kaoru; Kawano, Kenji

2014-09-10

To investigate the effect of face inversion and thatcherization (eye inversion) on temporal processing stages of facial information, single neuron activities in the temporal cortex (area TE) of two rhesus monkeys were recorded. Test stimuli were colored pictures of monkey faces (four with four different expressions), human faces (three with four different expressions), and geometric shapes. Modifications were made in each face-picture, and its four variations were used as stimuli: upright original, inverted original, upright thatcherized, and inverted thatcherized faces. A total of 119 neurons responded to at least one of the upright original facial stimuli. A majority of the neurons (71%) showed activity modulations depending on upright and inverted presentations, and a lesser number of neurons (13%) showed activity modulations depending on original and thatcherized face conditions. In the case of face inversion, information about the fine category (facial identity and expression) decreased, whereas information about the global category (monkey vs human vs shape) was retained for both the original and thatcherized faces. Principal component analysis on the neuronal population responses revealed that the global categorization occurred regardless of the face inversion and that the inverted faces were represented near the upright faces in the principal component analysis space. By contrast, the face inversion decreased the ability to represent human facial identity and monkey facial expression. Thus, the neuronal population represented inverted faces as faces but failed to represent the identity and expression of the inverted faces, indicating that the neuronal representation in area TE cause the perceptual effect of face inversion. Copyright © 2014 the authors 0270-6474/14/3412457-13$15.00/0.

Neurons with object-centered spatial selectivity in macaque SEF: do they represent locations or rules?

PubMed

Tremblay, Léon; Gettner, Sonya N; Olson, Carl R

2002-01-01

In macaque monkeys performing a task that requires eye movements to the leftmost or rightmost of two dots in a horizontal array, some neurons in the supplementary eye field (SEF) fire differentially according to which side of the array is the target regardless of the array's location on the screen. We refer to these neurons as exhibiting selectivity for object-centered location. This form of selectivity might arise from involvement of the neurons in either of two processes: representing the locations of targets or representing the rules by which targets are selected. To distinguish between these possibilities, we monitored neuronal activity in the SEF of two monkeys performing a task that required the selection of targets by either an object-centered spatial rule or a color rule. On each trial, a sample array consisting of two side-by-side dots appeared; then a cue flashed on one dot; then the display vanished and a delay ensued. Next a target array consisting of two side-by-side dots appeared at an unpredictable location and another delay ensued; finally the monkey had to make an eye movement to one of the target dots. On some trials, the monkey had to select the dot on the same side as the cue (right or left). On other trials, he had to select the target of the same color as the cue (red or green). Neuronal activity robustly encoded the object-centered locations first of the cue and then of the target regardless of the whether the monkey was following a rule based on object-centered location or color. Neuronal activity was at most weakly affected by the type of rule the monkey was following (object-centered-location or color) or by the color of the cue and target (red or green). On trials involving a color rule, neuronal activity was moderately enhanced when the cue and target appeared on opposite sides of their respective arrays. We conclude that the general function of SEF neurons selective for object-centered location is to represent where the cue and target are in their respective arrays rather than to represent the rule for target selection.

TED: A Tolerant Edit Distance for segmentation evaluation.

PubMed

Funke, Jan; Klein, Jonas; Moreno-Noguer, Francesc; Cardona, Albert; Cook, Matthew

2017-02-15

In this paper, we present a novel error measure to compare a computer-generated segmentation of images or volumes against ground truth. This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations that we usually encounter in biomedical image processing: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be explicitly expressible in the measure. (2) Non-tolerable errors have to be corrected manually. The effort needed to do so should be reflected by the error measure. Our measure is the minimal weighted sum of split and merge operations to apply to one segmentation such that it resembles another segmentation within specified tolerance bounds. This is in contrast to other commonly used measures like Rand index or variation of information, which integrate small, but tolerable, differences. Additionally, the TED provides intuitive numbers and allows the localization and classification of errors in images or volumes. We demonstrate the applicability of the TED on 3D segmentations of neurons in electron microscopy images where topological correctness is arguable more important than exact boundary locations. Furthermore, we show that the TED is not just limited to evaluation tasks. We use it as the loss function in a max-margin learning framework to find parameters of an automatic neuron segmentation algorithm. We show that training to minimize the TED, i.e., to minimize crucial errors, leads to higher segmentation accuracy compared to other learning methods. Copyright © 2016. Published by Elsevier Inc.

Cognitive processes involved in smooth pursuit eye movements: behavioral evidence, neural substrate and clinical correlation

PubMed Central

Fukushima, Kikuro; Fukushima, Junko; Warabi, Tateo; Barnes, Graham R.

2013-01-01

Smooth-pursuit eye movements allow primates to track moving objects. Efficient pursuit requires appropriate target selection and predictive compensation for inherent processing delays. Prediction depends on expectation of future object motion, storage of motion information and use of extra-retinal mechanisms in addition to visual feedback. We present behavioral evidence of how cognitive processes are involved in predictive pursuit in normal humans and then describe neuronal responses in monkeys and behavioral responses in patients using a new technique to test these cognitive controls. The new technique examines the neural substrate of working memory and movement preparation for predictive pursuit by using a memory-based task in macaque monkeys trained to pursue (go) or not pursue (no-go) according to a go/no-go cue, in a direction based on memory of a previously presented visual motion display. Single-unit task-related neuronal activity was examined in medial superior temporal cortex (MST), supplementary eye fields (SEF), caudal frontal eye fields (FEF), cerebellar dorsal vermis lobules VI–VII, caudal fastigial nuclei (cFN), and floccular region. Neuronal activity reflecting working memory of visual motion direction and go/no-go selection was found predominantly in SEF, cerebellar dorsal vermis and cFN, whereas movement preparation related signals were found predominantly in caudal FEF and the same cerebellar areas. Chemical inactivation produced effects consistent with differences in signals represented in each area. When applied to patients with Parkinson's disease (PD), the task revealed deficits in movement preparation but not working memory. In contrast, patients with frontal cortical or cerebellar dysfunction had high error rates, suggesting impaired working memory. We show how neuronal activity may be explained by models of retinal and extra-retinal interaction in target selection and predictive control and thus aid understanding of underlying pathophysiology. PMID:23515488

Spatial representation and cognitive modulation of response variability in the lateral intraparietal area priority map.

PubMed

Falkner, Annegret L; Goldberg, Michael E; Krishna, B Suresh

2013-10-09

The lateral intraparietal area (LIP) in the macaque contains a priority-based representation of the visual scene. We previously showed that the mean spike rate of LIP neurons is strongly influenced by spatially wide-ranging surround suppression in a manner that effectively sharpens the priority map. Reducing response variability can also improve the precision of LIP's priority map. We show that when a monkey plans a visually guided delayed saccade with an intervening distractor, variability (measured by the Fano factor) decreases both for neurons representing the saccade goal and for neurons representing the broad spatial surround. The reduction in Fano factor is maximal for neurons representing the saccade goal and steadily decreases for neurons representing more distant locations. LIP Fano factor changes are behaviorally significant: increasing expected reward leads to lower variability for the LIP representation of both the target and distractor locations, and trials with shorter latency saccades are associated with lower Fano factors in neurons representing the surround. Thus, the LIP Fano factor reflects both stimulus and behavioral engagement. Quantitative modeling shows that the interaction between mean spike count and target-receptive field (RF) distance in the surround during the predistractor epoch is multiplicative: the Fano factor increases more steeply with mean spike count further away from the RF. A negative-binomial model for LIP spike counts captures these findings quantitatively, suggests underlying mechanisms based on trial-by-trial variations in mean spike rate or burst-firing patterns, and potentially provides a principled framework to account simultaneously for the previously observed unsystematic relationships between spike rate and variability in different brain areas.

Precision of Discrete and Rhythmic Forelimb Movements Requires a Distinct Neuronal Subpopulation in the Interposed Anterior Nucleus.

PubMed

Low, Aloysius Y T; Thanawalla, Ayesha R; Yip, Alaric K K; Kim, Jinsook; Wong, Kelly L L; Tantra, Martesa; Augustine, George J; Chen, Albert I

2018-02-27

The deep cerebellar nuclei (DCN) represent output channels of the cerebellum, and they transmit integrated sensorimotor signals to modulate limb movements. But the functional relevance of identifiable neuronal subpopulations within the DCN remains unclear. Here, we examine a genetically tractable population of neurons in the mouse interposed anterior nucleus (IntA). We show that these neurons represent a subset of glutamatergic neurons in the IntA and constitute a specific element of an internal feedback circuit within the cerebellar cortex and cerebello-thalamo-cortical pathway associated with limb control. Ablation and optogenetic stimulation of these neurons disrupt efficacy of skilled reach and locomotor movement and reveal that they control positioning and timing of the forelimb and hindlimb. Together, our findings uncover the function of a distinct neuronal subpopulation in the deep cerebellum and delineate the anatomical substrates and kinematic parameters through which it modulates precision of discrete and rhythmic limb movements. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

A Deep Learning Algorithm of Neural Network for the Parameterization of Typhoon-Ocean Feedback in Typhoon Forecast Models

NASA Astrophysics Data System (ADS)

Jiang, Guo-Qing; Xu, Jing; Wei, Jun

2018-04-01

Two algorithms based on machine learning neural networks are proposed—the shallow learning (S-L) and deep learning (D-L) algorithms—that can potentially be used in atmosphere-only typhoon forecast models to provide flow-dependent typhoon-induced sea surface temperature cooling (SSTC) for improving typhoon predictions. The major challenge of existing SSTC algorithms in forecast models is how to accurately predict SSTC induced by an upcoming typhoon, which requires information not only from historical data but more importantly also from the target typhoon itself. The S-L algorithm composes of a single layer of neurons with mixed atmospheric and oceanic factors. Such a structure is found to be unable to represent correctly the physical typhoon-ocean interaction. It tends to produce an unstable SSTC distribution, for which any perturbations may lead to changes in both SSTC pattern and strength. The D-L algorithm extends the neural network to a 4 × 5 neuron matrix with atmospheric and oceanic factors being separated in different layers of neurons, so that the machine learning can determine the roles of atmospheric and oceanic factors in shaping the SSTC. Therefore, it produces a stable crescent-shaped SSTC distribution, with its large-scale pattern determined mainly by atmospheric factors (e.g., winds) and small-scale features by oceanic factors (e.g., eddies). Sensitivity experiments reveal that the D-L algorithms improve maximum wind intensity errors by 60-70% for four case study simulations, compared to their atmosphere-only model runs.

Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

PubMed

Barton, Alan J; Valdés, Julio J; Orchard, Robert

2009-01-01

Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

3-dimensional electron microscopic imaging of the zebrafish olfactory bulb and dense reconstruction of neurons.

PubMed

Wanner, Adrian A; Genoud, Christel; Friedrich, Rainer W

2016-11-08

Large-scale reconstructions of neuronal populations are critical for structural analyses of neuronal cell types and circuits. Dense reconstructions of neurons from image data require ultrastructural resolution throughout large volumes, which can be achieved by automated volumetric electron microscopy (EM) techniques. We used serial block face scanning EM (SBEM) and conductive sample embedding to acquire an image stack from an olfactory bulb (OB) of a zebrafish larva at a voxel resolution of 9.25×9.25×25 nm 3 . Skeletons of 1,022 neurons, 98% of all neurons in the OB, were reconstructed by manual tracing and efficient error correction procedures. An ergonomic software package, PyKNOSSOS, was created in Python for data browsing, neuron tracing, synapse annotation, and visualization. The reconstructions allow for detailed analyses of morphology, projections and subcellular features of different neuron types. The high density of reconstructions enables geometrical and topological analyses of the OB circuitry. Image data can be accessed and viewed through the neurodata web services (http://www.neurodata.io). Raw data and reconstructions can be visualized in PyKNOSSOS.

3-dimensional electron microscopic imaging of the zebrafish olfactory bulb and dense reconstruction of neurons

PubMed Central

Wanner, Adrian A.; Genoud, Christel; Friedrich, Rainer W.

2016-01-01

Large-scale reconstructions of neuronal populations are critical for structural analyses of neuronal cell types and circuits. Dense reconstructions of neurons from image data require ultrastructural resolution throughout large volumes, which can be achieved by automated volumetric electron microscopy (EM) techniques. We used serial block face scanning EM (SBEM) and conductive sample embedding to acquire an image stack from an olfactory bulb (OB) of a zebrafish larva at a voxel resolution of 9.25×9.25×25 nm3. Skeletons of 1,022 neurons, 98% of all neurons in the OB, were reconstructed by manual tracing and efficient error correction procedures. An ergonomic software package, PyKNOSSOS, was created in Python for data browsing, neuron tracing, synapse annotation, and visualization. The reconstructions allow for detailed analyses of morphology, projections and subcellular features of different neuron types. The high density of reconstructions enables geometrical and topological analyses of the OB circuitry. Image data can be accessed and viewed through the neurodata web services (http://www.neurodata.io). Raw data and reconstructions can be visualized in PyKNOSSOS. PMID:27824337

Modeling shifts in the rate and pattern of subthalamopallidal network activity during deep brain stimulation.

PubMed

Hahn, Philip J; McIntyre, Cameron C

2010-06-01

Deep brain stimulation (DBS) of the subthlamic nucleus (STN) represents an effective treatment for medically refractory Parkinson's disease; however, understanding of its effects on basal ganglia network activity remains limited. We constructed a computational model of the subthalamopallidal network, trained it to fit in vivo recordings from parkinsonian monkeys, and evaluated its response to STN DBS. The network model was created with synaptically connected single compartment biophysical models of STN and pallidal neurons, and stochastically defined inputs driven by cortical beta rhythms. A least mean square error training algorithm was developed to parameterize network connections and minimize error when compared to experimental spike and burst rates in the parkinsonian condition. The output of the trained network was then compared to experimental data not used in the training process. We found that reducing the influence of the cortical beta input on the model generated activity that agreed well with recordings from normal monkeys. Further, during STN DBS in the parkinsonian condition the simulations reproduced the reduction in GPi bursting found in existing experimental data. The model also provided the opportunity to greatly expand analysis of GPi bursting activity, generating three major predictions. First, its reduction was proportional to the volume of STN activated by DBS. Second, GPi bursting decreased in a stimulation frequency dependent manner, saturating at values consistent with clinically therapeutic DBS. And third, ablating STN neurons, reported to generate similar therapeutic outcomes as STN DBS, also reduced GPi bursting. Our theoretical analysis of stimulation induced network activity suggests that regularization of GPi firing is dependent on the volume of STN tissue activated and a threshold level of burst reduction may be necessary for therapeutic effect.

Neural Network Burst Pressure Prediction in Graphite/Epoxy Pressure Vessels from Acoustic Emission Amplitude Data

NASA Technical Reports Server (NTRS)

Hill, Eric v. K.; Walker, James L., II; Rowell, Ginger H.

1995-01-01

Acoustic emission (AE) data were taken during hydroproof for three sets of ASTM standard 5.75 inch diameter filament wound graphite/epoxy bottles. All three sets of bottles had the same design and were wound from the same graphite fiber; the only difference was in the epoxies used. Two of the epoxies had similar mechanical properties, and because the acoustic properties of materials are a function of their stiffnesses, it was thought that the AE data from the two sets might also be similar; however, this was not the case. Therefore, the three resin types were categorized using dummy variables, which allowed the prediction of burst pressures all three sets of bottles using a single neural network. Three bottles from each set were used to train the network. The resin category, the AE amplitude distribution data taken up to 25 % of the expected burst pressure, and the actual burst pressures were used as inputs. Architecturally, the network consisted of a forty-three neuron input layer (a single categorical variable defining the resin type plus forty-two continuous variables for the AE amplitude frequencies), a fifteen neuron hidden layer for mapping, and a single output neuron for burst pressure prediction. The network trained on all three bottle sets was able to predict burst pressures in the remaining bottles with a worst case error of + 6.59%, slightly greater than the desired goal of + 5%. This larger than desired error was due to poor resolution in the amplitude data for the third bottle set. When the third set of bottles was eliminated from consideration, only four hidden layer neurons were necessary to generate a worst case prediction error of - 3.43%, well within the desired goal.

The mirror neuron analogy: Implications for rehabilitation neuroscience. Comment on "Grasping synergies: A motor-control approach to the mirror neuron mechanism" by A. D'Ausilio et al.

NASA Astrophysics Data System (ADS)

Frey, Scott H.; Chen, Pin-Wei

2015-03-01

The discovery of individual neurons that respond selectively to both the perception and execution of actions in macaques has had a profound impact on cognitive neuroscience [1]. By demonstrating a neurophysiological mechanism linking perception and motor performance, these mirror neurons have inspired a broad range of research in humans using non-invasive neuroimaging [2,3], and transcranial magnetic stimulation (TMS) [4]. In the present review, D'Ausilio and colleagues point out inconsistencies among TMS evidence concerning whether the so-called mirror neuron system (MNS) represents actions as low-level kinematic features, or more abstract goals [5]. They propose instead that actions are represented in the MNS as a modest number of motor synergies, a position arrived at through following propositional reasoning:

Periodic activation function and a modified learning algorithm for the multivalued neuron.

PubMed

Aizenberg, Igor

2010-12-01

In this paper, we consider a new periodic activation function for the multivalued neuron (MVN). The MVN is a neuron with complex-valued weights and inputs/output, which are located on the unit circle. Although the MVN outperforms many other neurons and MVN-based neural networks have shown their high potential, the MVN still has a limited capability of learning highly nonlinear functions. A periodic activation function, which is introduced in this paper, makes it possible to learn nonlinearly separable problems and non-threshold multiple-valued functions using a single multivalued neuron. We call this neuron a multivalued neuron with a periodic activation function (MVN-P). The MVN-Ps functionality is much higher than that of the regular MVN. The MVN-P is more efficient in solving various classification problems. A learning algorithm based on the error-correction rule for the MVN-P is also presented. It is shown that a single MVN-P can easily learn and solve those benchmark classification problems that were considered unsolvable using a single neuron. It is also shown that a universal binary neuron, which can learn nonlinearly separable Boolean functions, and a regular MVN are particular cases of the MVN-P.

The transfer function of neuron spike.

PubMed

Palmieri, Igor; Monteiro, Luiz H A; Miranda, Maria D

2015-08-01

The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting. Copyright © 2015 Elsevier Ltd. All rights reserved.

Responses of primate taste cortex neurons to the astringent tastant tannic acid.

PubMed

Critchley, H D; Rolls, E T

1996-04-01

In order to advance knowledge of the neural control of feeding, we investigated the cortical representation of the taste of tannic acid, which produces the taste of astringency. It is a dietary component of biological importance particularly to arboreal primates. Recordings were made from 74 taste responsive neurons in the orbitofrontal cortex. Single neurons were found that were tuned to respond to 0.001 M tannic acid, and represented a subpopulation of neurons that was distinct from neurons responsive to the tastes of glucose (sweet), NaCl (salty), HCl (sour), quinine (bitter) and monosodium glutamate (umami). In addition, across the population of 74 neurons, tannic acid was as well represented as the tastes of NaCl, HCl quinine or monosodium glutamate. Multidimensional scaling analysis of the neuronal responses to the tastants indicates that tannic acid lies outside the boundaries of the four conventional taste qualities (sweet, sour, bitter and salty). Taken together these data indicate that the astringent taste of tannic acid should be considered as a taste quality, which receives a separate representation from sweet, salt, bitter and sour in the primate cortical taste areas.

Trial-to-trial adjustments of speed-accuracy trade-offs in premotor and primary motor cortex

PubMed Central

Guberman, Guido; Cisek, Paul

2016-01-01

Recent studies have shown that activity in sensorimotor structures varies depending on the speed-accuracy trade-off (SAT) context in which a decision is made. Here we tested the hypothesis that the same areas also reflect a more local adjustment of SAT established between individual trials, based on the outcome of the previous decision. Two monkeys performed a reaching decision task in which sensory evidence continuously evolves during the time course of a trial. In two SAT contexts, we compared neural activity in trials following a correct choice vs. those following an error. In dorsal premotor cortex (PMd), we found that 23% of cells exhibited significantly weaker baseline activity after error trials, and for ∼30% of these this effect persisted into the deliberation epoch. These cells also contributed to the process of combining sensory evidence with the growing urgency to commit to a choice. We also found that the activity of 22% of PMd cells was increased after error trials. These neurons appeared to carry less information about sensory evidence and time-dependent urgency. For most of these modulated cells, the effect was independent of whether the previous error was expected or unexpected. We found similar phenomena in primary motor cortex (M1), with 25% of cells decreasing and 34% increasing activity after error trials, but unlike PMd, these neurons showed less clear differences in their response properties. These findings suggest that PMd and M1 belong to a network of brain areas involved in SAT adjustments established using the recent history of reinforcement. NEW & NOTEWORTHY Setting the speed-accuracy trade-off (SAT) is crucial for efficient decision making. Previous studies have reported that subjects adjust their SAT after individual decisions, usually choosing more conservatively after errors, but the neural correlates of this phenomenon are only partially known. Here, we show that neurons in PMd and M1 of monkeys performing a reach decision task support this mechanism by adequately modulating their firing rate as a function of the outcome of the previous decision. PMID:27852735

A Probabilistic Palimpsest Model of Visual Short-term Memory

PubMed Central

Matthey, Loic; Bays, Paul M.; Dayan, Peter

2015-01-01

Working memory plays a key role in cognition, and yet its mechanisms remain much debated. Human performance on memory tasks is severely limited; however, the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished. We propose a palimpsest model, with the occurrent activity of a single population of neurons coding for several multi-featured items. Using a probabilistic approach to storage and recall, we show how this model can account for many qualitative aspects of existing experimental data. In our account, the underlying nature of a memory item depends entirely on the characteristics of the population representation, and we provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data. Our model provides the first principled account of misbinding errors. We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ. PMID:25611204

A probabilistic palimpsest model of visual short-term memory.

PubMed

Matthey, Loic; Bays, Paul M; Dayan, Peter

2015-01-01

Working memory plays a key role in cognition, and yet its mechanisms remain much debated. Human performance on memory tasks is severely limited; however, the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished. We propose a palimpsest model, with the occurrent activity of a single population of neurons coding for several multi-featured items. Using a probabilistic approach to storage and recall, we show how this model can account for many qualitative aspects of existing experimental data. In our account, the underlying nature of a memory item depends entirely on the characteristics of the population representation, and we provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data. Our model provides the first principled account of misbinding errors. We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ.

A neuronal model of a global workspace in effortful cognitive tasks.

PubMed

Dehaene, S; Kerszberg, M; Changeux, J P

1998-11-24

A minimal hypothesis is proposed concerning the brain processes underlying effortful tasks. It distinguishes two main computational spaces: a unique global workspace composed of distributed and heavily interconnected neurons with long-range axons, and a set of specialized and modular perceptual, motor, memory, evaluative, and attentional processors. Workspace neurons are mobilized in effortful tasks for which the specialized processors do not suffice. They selectively mobilize or suppress, through descending connections, the contribution of specific processor neurons. In the course of task performance, workspace neurons become spontaneously coactivated, forming discrete though variable spatio-temporal patterns subject to modulation by vigilance signals and to selection by reward signals. A computer simulation of the Stroop task shows workspace activation to increase during acquisition of a novel task, effortful execution, and after errors. We outline predictions for spatio-temporal activation patterns during brain imaging, particularly about the contribution of dorsolateral prefrontal cortex and anterior cingulate to the workspace.

Design of time-pulse coded optoelectronic neuronal elements for nonlinear transformation and integration

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Lazareva, Maria V.

2008-03-01

In the paper the actuality of neurophysiologically motivated neuron arrays with flexibly programmable functions and operations with possibility to select required accuracy and type of nonlinear transformation and learning are shown. We consider neurons design and simulation results of multichannel spatio-time algebraic accumulation - integration of optical signals. Advantages for nonlinear transformation and summation - integration are shown. The offered circuits are simple and can have intellectual properties such as learning and adaptation. The integrator-neuron is based on CMOS current mirrors and comparators. The performance: consumable power - 100...500 μW, signal period- 0.1...1ms, input optical signals power - 0.2...20 μW time delays - less 1μs, the number of optical signals - 2...10, integration time - 10...100 of signal periods, accuracy or integration error - about 1%. Various modifications of the neuron-integrators with improved performance and for different applications are considered in the paper.

Midbrain dopamine neurons signal aversion in a reward-context-dependent manner

PubMed Central

Matsumoto, Hideyuki; Tian, Ju; Uchida, Naoshige; Watabe-Uchida, Mitsuko

2016-01-01

Dopamine is thought to regulate learning from appetitive and aversive events. Here we examined how optogenetically-identified dopamine neurons in the lateral ventral tegmental area of mice respond to aversive events in different conditions. In low reward contexts, most dopamine neurons were exclusively inhibited by aversive events, and expectation reduced dopamine neurons’ responses to reward and punishment. When a single odor predicted both reward and punishment, dopamine neurons’ responses to that odor reflected the integrated value of both outcomes. Thus, in low reward contexts, dopamine neurons signal value prediction errors (VPEs) integrating information about both reward and aversion in a common currency. In contrast, in high reward contexts, dopamine neurons acquired a short-latency excitation to aversive events that masked their VPE signaling. Our results demonstrate the importance of considering the contexts to examine the representation in dopamine neurons and uncover different modes of dopamine signaling, each of which may be adaptive for different environments. DOI: http://dx.doi.org/10.7554/eLife.17328.001 PMID:27760002

EEG oscillatory patterns are associated with error prediction during music performance and are altered in musician's dystonia.

PubMed

Ruiz, María Herrojo; Strübing, Felix; Jabusch, Hans-Christian; Altenmüller, Eckart

2011-04-15

Skilled performance requires the ability to monitor ongoing behavior, detect errors in advance and modify the performance accordingly. The acquisition of fast predictive mechanisms might be possible due to the extensive training characterizing expertise performance. Recent EEG studies on piano performance reported a negative event-related potential (ERP) triggered in the ACC 70 ms before performance errors (pitch errors due to incorrect keypress). This ERP component, termed pre-error related negativity (pre-ERN), was assumed to reflect processes of error detection in advance. However, some questions remained to be addressed: (i) Does the electrophysiological marker prior to errors reflect an error signal itself or is it related instead to the implementation of control mechanisms? (ii) Does the posterior frontomedial cortex (pFMC, including ACC) interact with other brain regions to implement control adjustments following motor prediction of an upcoming error? (iii) Can we gain insight into the electrophysiological correlates of error prediction and control by assessing the local neuronal synchronization and phase interaction among neuronal populations? (iv) Finally, are error detection and control mechanisms defective in pianists with musician's dystonia (MD), a focal task-specific dystonia resulting from dysfunction of the basal ganglia-thalamic-frontal circuits? Consequently, we investigated the EEG oscillatory and phase synchronization correlates of error detection and control during piano performances in healthy pianists and in a group of pianists with MD. In healthy pianists, the main outcomes were increased pre-error theta and beta band oscillations over the pFMC and 13-15 Hz phase synchronization, between the pFMC and the right lateral prefrontal cortex, which predicted corrective mechanisms. In MD patients, the pattern of phase synchronization appeared in a different frequency band (6-8 Hz) and correlated with the severity of the disorder. The present findings shed new light on the neural mechanisms, which might implement motor prediction by means of forward control processes, as they function in healthy pianists and in their altered form in patients with MD. Copyright © 2010 Elsevier Inc. All rights reserved.

Graphene foam as a biocompatible scaffold for culturing human neurons

PubMed Central

Mattei, Cristiana; Nasr, Babak; Hudson, Emma J.; Alshawaf, Abdullah J.; Chana, Gursharan; Everall, Ian P.; Dottori, Mirella; Skafidas, Efstratios

2018-01-01

In this study, we explore the use of electrically active graphene foam as a scaffold for the culture of human-derived neurons. Human embryonic stem cell (hESC)-derived cortical neurons fated as either glutamatergic or GABAergic neuronal phenotypes were cultured on graphene foam. We show that graphene foam is biocompatible for the culture of human neurons, capable of supporting cell viability and differentiation of hESC-derived cortical neurons. Based on the findings, we propose that graphene foam represents a suitable scaffold for engineering neuronal tissue and warrants further investigation as a model for understanding neuronal maturation, function and circuit formation. PMID:29657752

The Neuronal Ceroid-Lipofuscinoses

ERIC Educational Resources Information Center

Bennett, Michael J.; Rakheja, Dinesh

2013-01-01

The neuronal ceroid-lipofuscinoses (NCL's, Batten disease) represent a group of severe neurodegenerative diseases, which mostly present in childhood. The phenotypes are similar and include visual loss, seizures, loss of motor and cognitive function, and early death. At autopsy, there is massive neuronal loss with characteristic storage in…

Where the thoughts dwell: the physiology of neuronal-glial "diffuse neural net".

PubMed

Verkhratsky, Alexei; Parpura, Vladimir; Rodríguez, José J

2011-01-07

The mechanisms underlying the production of thoughts by exceedingly complex cellular networks that construct the human brain constitute the most challenging problem of natural sciences. Our understanding of the brain function is very much shaped by the neuronal doctrine that assumes that neuronal networks represent the only substrate for cognition. These neuronal networks however are embedded into much larger and probably more complex network formed by neuroglia. The latter, although being electrically silent, employ many different mechanisms for intercellular signalling. It appears that astrocytes can control synaptic networks and in such a capacity they may represent an integral component of the computational power of the brain rather than being just brain "connective tissue". The fundamental question of whether neuroglia is involved in cognition and information processing remains, however, open. Indeed, a remarkable increase in the number of glial cells that distinguishes the human brain can be simply a result of exceedingly high specialisation of the neuronal networks, which delegated all matters of survival and maintenance to the neuroglia. At the same time potential power of analogue processing offered by internally connected glial networks may represent the alternative mechanism involved in cognition. Copyright © 2010 Elsevier B.V. All rights reserved.

Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages

PubMed Central

Nunez-Iglesias, Juan; Kennedy, Ryan; Plaza, Stephen M.; Chakraborty, Anirban; Katz, William T.

2014-01-01

The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them. PMID:24772079

Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

PubMed

Leibig, Christian; Wachtler, Thomas; Zeck, Günther

2016-09-15

Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

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

Models of Innate Neural Attractors and Their Applications for Neural Information Processing

PubMed Central

Solovyeva, Ksenia P.; Karandashev, Iakov M.; Zhavoronkov, Alex; Dunin-Barkowski, Witali L.

2016-01-01

In this work we reveal and explore a new class of attractor neural networks, based on inborn connections provided by model molecular markers, the molecular marker based attractor neural networks (MMBANN). Each set of markers has a metric, which is used to make connections between neurons containing the markers. We have explored conditions for the existence of attractor states, critical relations between their parameters and the spectrum of single neuron models, which can implement the MMBANN. Besides, we describe functional models (perceptron and SOM), which obtain significant advantages over the traditional implementation of these models, while using MMBANN. In particular, a perceptron, based on MMBANN, gets specificity gain in orders of error probabilities values, MMBANN SOM obtains real neurophysiological meaning, the number of possible grandma cells increases 1000-fold with MMBANN. MMBANN have sets of attractor states, which can serve as finite grids for representation of variables in computations. These grids may show dimensions of d = 0, 1, 2,…. We work with static and dynamic attractor neural networks of the dimensions d = 0 and 1. We also argue that the number of dimensions which can be represented by attractors of activities of neural networks with the number of elements N = 104 does not exceed 8. PMID:26778977

Difference in Perseverative Errors during a Visual Attention Task with Auditory Distractors in Alpha-9 Nicotinic Receptor Subunit Wild Type and Knock-Out Mice.

PubMed

Jorratt, Pascal; Delano, Paul H; Delgado, Carolina; Dagnino-Subiabre, Alexies; Terreros, Gonzalo

2017-01-01

The auditory efferent system is a neural network that originates in the auditory cortex and projects to the cochlear receptor through olivocochlear (OC) neurons. Medial OC neurons make cholinergic synapses with outer hair cells (OHCs) through nicotinic receptors constituted by α9 and α10 subunits. One of the physiological functions of the α9 nicotinic receptor subunit (α9-nAChR) is the suppression of auditory distractors during selective attention to visual stimuli. In a recent study we demonstrated that the behavioral performance of alpha-9 nicotinic receptor knock-out (KO) mice is altered during selective attention to visual stimuli with auditory distractors since they made less correct responses and more omissions than wild type (WT) mice. As the inhibition of the behavioral responses to irrelevant stimuli is an important mechanism of the selective attention processes, behavioral errors are relevant measures that can reflect altered inhibitory control. Errors produced during a cued attention task can be classified as premature, target and perseverative errors. Perseverative responses can be considered as an inability to inhibit the repetition of an action already planned, while premature responses can be considered as an index of the ability to wait or retain an action. Here, we studied premature, target and perseverative errors during a visual attention task with auditory distractors in WT and KO mice. We found that α9-KO mice make fewer perseverative errors with longer latencies than WT mice in the presence of auditory distractors. In addition, although we found no significant difference in the number of target error between genotypes, KO mice made more short-latency target errors than WT mice during the presentation of auditory distractors. The fewer perseverative error made by α9-KO mice could be explained by a reduced motivation for reward and an increased impulsivity during decision making with auditory distraction in KO mice.

Spatiotemporal Coding of Individual Chemicals by the Gustatory System

PubMed Central

Reiter, Sam; Campillo Rodriguez, Chelsey; Sun, Kui

2015-01-01

Four of the five major sensory systems (vision, olfaction, somatosensation, and audition) are thought to use different but partially overlapping sets of neurons to form unique representations of vast numbers of stimuli. The only exception is gustation, which is thought to represent only small numbers of basic taste categories. However, using new methods for delivering tastant chemicals and making electrophysiological recordings from the tractable gustatory system of the moth Manduca sexta, we found chemical-specific information is as follows: (1) initially encoded in the population of gustatory receptor neurons as broadly distributed spatiotemporal patterns of activity; (2) dramatically integrated and temporally transformed as it propagates to monosynaptically connected second-order neurons; and (3) observed in tastant-specific behavior. Our results are consistent with an emerging view of the gustatory system: rather than constructing basic taste categories, it uses a spatiotemporal population code to generate unique neural representations of individual tastant chemicals. SIGNIFICANCE STATEMENT Our results provide a new view of taste processing. Using a new, relatively simple model system and a new set of techniques to deliver taste stimuli and to examine gustatory receptor neurons and their immediate followers, we found no evidence for labeled line connectivity, or basic taste categories such as sweet, salty, bitter, and sour. Rather, individual tastant chemicals are represented as patterns of spiking activity distributed across populations of receptor neurons. These representations are transformed substantially as multiple types of receptor neurons converge upon follower neurons, leading to a combinatorial coding format that uniquely, rapidly, and efficiently represents individual taste chemicals. Finally, we found that the information content of these neurons can drive tastant-specific behavior. PMID:26338341

Spatiotemporal Coding of Individual Chemicals by the Gustatory System.

PubMed

Reiter, Sam; Campillo Rodriguez, Chelsey; Sun, Kui; Stopfer, Mark

2015-09-02

Four of the five major sensory systems (vision, olfaction, somatosensation, and audition) are thought to use different but partially overlapping sets of neurons to form unique representations of vast numbers of stimuli. The only exception is gustation, which is thought to represent only small numbers of basic taste categories. However, using new methods for delivering tastant chemicals and making electrophysiological recordings from the tractable gustatory system of the moth Manduca sexta, we found chemical-specific information is as follows: (1) initially encoded in the population of gustatory receptor neurons as broadly distributed spatiotemporal patterns of activity; (2) dramatically integrated and temporally transformed as it propagates to monosynaptically connected second-order neurons; and (3) observed in tastant-specific behavior. Our results are consistent with an emerging view of the gustatory system: rather than constructing basic taste categories, it uses a spatiotemporal population code to generate unique neural representations of individual tastant chemicals. Our results provide a new view of taste processing. Using a new, relatively simple model system and a new set of techniques to deliver taste stimuli and to examine gustatory receptor neurons and their immediate followers, we found no evidence for labeled line connectivity, or basic taste categories such as sweet, salty, bitter, and sour. Rather, individual tastant chemicals are represented as patterns of spiking activity distributed across populations of receptor neurons. These representations are transformed substantially as multiple types of receptor neurons converge upon follower neurons, leading to a combinatorial coding format that uniquely, rapidly, and efficiently represents individual taste chemicals. Finally, we found that the information content of these neurons can drive tastant-specific behavior. Copyright © 2015 the authors 0270-6474/15/3512309-13$15.00/0.

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

Arbitrary nonlinearity is sufficient to represent all functions by neural networks - A theorem

NASA Technical Reports Server (NTRS)

Kreinovich, Vladik YA.

1991-01-01

It is proved that if we have neurons implementing arbitrary linear functions and a neuron implementing one (arbitrary but smooth) nonlinear function g(x), then for every continuous function f(x sub 1,..., x sub m) of arbitrarily many variables, and for arbitrary e above 0, we can construct a network that consists of g-neurons and linear neurons, and computes f with precision e.

A cross-platform freeware tool for digital reconstruction of neuronal arborizations from image stacks.

PubMed

Brown, Kerry M; Donohue, Duncan E; D'Alessandro, Giampaolo; Ascoli, Giorgio A

2005-01-01

Digital reconstruction of neuronal arborizations is an important step in the quantitative investigation of cellular neuroanatomy. In this process, neurites imaged by microscopy are semi-manually traced through the use of specialized computer software and represented as binary trees of branching cylinders (or truncated cones). Such form of the reconstruction files is efficient and parsimonious, and allows extensive morphometric analysis as well as the implementation of biophysical models of electrophysiology. Here, we describe Neuron_ Morpho, a plugin for the popular Java application ImageJ that mediates the digital reconstruction of neurons from image stacks. Both the executable and code of Neuron_ Morpho are freely distributed (www.maths. soton.ac.uk/staff/D'Alessandro/morpho or www.krasnow.gmu.edu/L-Neuron), and are compatible with all major computer platforms (including Windows, Mac, and Linux). We tested Neuron_Morpho by reconstructing two neurons from each of the two preparations representing different brain areas (hippocampus and cerebellum), neuritic type (pyramidal cell dendrites and olivar axonal projection terminals), and labeling method (rapid Golgi impregnation and anterograde dextran amine), and quantitatively comparing the resulting morphologies to those of the same cells reconstructed with the standard commercial system, Neurolucida. None of the numerous morphometric measures that were analyzed displayed any significant or systematic difference between the two reconstructing systems.

Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals

NASA Astrophysics Data System (ADS)

Yu, Lianchun; Liu, Liwei

2014-03-01

The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals.

PubMed

Yu, Lianchun; Liu, Liwei

2014-03-01

The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

Roles of Aminergic Neurons in Formation and Recall of Associative Memory in Crickets

PubMed Central

Mizunami, Makoto; Matsumoto, Yukihisa

2010-01-01

We review recent progress in the study of roles of octopaminergic (OA-ergic) and dopaminergic (DA-ergic) signaling in insect classical conditioning, focusing on our studies on crickets. Studies on olfactory learning in honey bees and fruit-flies have suggested that OA-ergic and DA-ergic neurons convey reinforcing signals of appetitive unconditioned stimulus (US) and aversive US, respectively. Our work suggested that this is applicable to olfactory, visual pattern, and color learning in crickets, indicating that this feature is ubiquitous in learning of various sensory stimuli. We also showed that aversive memory decayed much faster than did appetitive memory, and we proposed that this feature is common in insects and humans. Our study also suggested that activation of OA- or DA-ergic neurons is needed for appetitive or aversive memory recall, respectively. To account for this finding, we proposed a model in which it is assumed that two types of synaptic connections are strengthened by conditioning and are activated during memory recall, one type being connections from neurons representing conditioned stimulus (CS) to neurons inducing conditioned response and the other being connections from neurons representing CS to OA- or DA-ergic neurons representing appetitive or aversive US, respectively. The former is called stimulus–response (S–R) connection and the latter is called stimulus–stimulus (S–S) connection by theorists studying classical conditioning in vertebrates. Results of our studies using a second-order conditioning procedure supported our model. We propose that insect classical conditioning involves the formation of S–S connection and its activation for memory recall, which are often called cognitive processes. PMID:21119781

Differences in reward processing between putative cell types in primate prefrontal cortex

PubMed Central

Fan, Hongwei; Wang, Rubin; Sakagami, Masamichi

2017-01-01

Single-unit studies in monkeys have demonstrated that neurons in the prefrontal cortex predict the reward type, reward amount or reward availability associated with a stimulus. To examine contributions of pyramidal cells and interneurons in reward processing, single-unit activity was extracellularly recorded in prefrontal cortices of four monkeys performing a reward prediction task. Based on their shapes of spike waveforms, prefrontal neurons were classified into broad-spike and narrow-spike units that represented putative pyramidal cells and interneurons, respectively. We mainly observed that narrow-spike neurons showed higher firing rates but less bursty discharges than did broad-spike neurons. Both narrow-spike and broad-spike cells selectively responded to the stimulus, reward and their interaction, and the proportions of each type of selective neurons were similar between the two cell classes. Moreover, the two types of cells displayed equal reliability of reward or stimulus discrimination. Furthermore, we found that broad-spike and narrow-spike cells showed distinct mechanisms for encoding reward or stimulus information. Broad-spike neurons raised their firing rate relative to the baseline rate to represent the preferred reward or stimulus information, whereas narrow-spike neurons inhibited their firing rate lower than the baseline rate to encode the non-preferred reward or stimulus information. Our results suggest that narrow-spike and broad-spike cells were equally involved in reward and stimulus processing in the prefrontal cortex. They utilized a binary strategy to complementarily represent reward or stimulus information, which was consistent with the task structure in which the monkeys were required to remember two reward conditions and two visual stimuli. PMID:29261734

Differences in reward processing between putative cell types in primate prefrontal cortex.

PubMed

Fan, Hongwei; Pan, Xiaochuan; Wang, Rubin; Sakagami, Masamichi

2017-01-01

Single-unit studies in monkeys have demonstrated that neurons in the prefrontal cortex predict the reward type, reward amount or reward availability associated with a stimulus. To examine contributions of pyramidal cells and interneurons in reward processing, single-unit activity was extracellularly recorded in prefrontal cortices of four monkeys performing a reward prediction task. Based on their shapes of spike waveforms, prefrontal neurons were classified into broad-spike and narrow-spike units that represented putative pyramidal cells and interneurons, respectively. We mainly observed that narrow-spike neurons showed higher firing rates but less bursty discharges than did broad-spike neurons. Both narrow-spike and broad-spike cells selectively responded to the stimulus, reward and their interaction, and the proportions of each type of selective neurons were similar between the two cell classes. Moreover, the two types of cells displayed equal reliability of reward or stimulus discrimination. Furthermore, we found that broad-spike and narrow-spike cells showed distinct mechanisms for encoding reward or stimulus information. Broad-spike neurons raised their firing rate relative to the baseline rate to represent the preferred reward or stimulus information, whereas narrow-spike neurons inhibited their firing rate lower than the baseline rate to encode the non-preferred reward or stimulus information. Our results suggest that narrow-spike and broad-spike cells were equally involved in reward and stimulus processing in the prefrontal cortex. They utilized a binary strategy to complementarily represent reward or stimulus information, which was consistent with the task structure in which the monkeys were required to remember two reward conditions and two visual stimuli.

TPEN, a Specific Zn2+ Chelator, Inhibits Sodium Dithionite and Glucose Deprivation (SDGD)-Induced Neuronal Death by Modulating Apoptosis, Glutamate Signaling, and Voltage-Gated K+ and Na+ Channels.

PubMed

Zhang, Feng; Ma, Xue-Ling; Wang, Yu-Xiang; He, Cong-Cong; Tian, Kun; Wang, Hong-Gang; An, Di; Heng, Bin; Xie, Lai-Hua; Liu, Yan-Qiang

2017-03-01

Hypoxia-ischemia-induced neuronal death is an important pathophysiological process that accompanies ischemic stroke and represents a major challenge in preventing ischemic stroke. To elucidate factors related to and a potential preventative mechanism of hypoxia-ischemia-induced neuronal death, primary neurons were exposed to sodium dithionite and glucose deprivation (SDGD) to mimic hypoxic-ischemic conditions. The effects of N,N,N',N'-tetrakis (2-pyridylmethyl) ethylenediamine (TPEN), a specific Zn 2+ -chelating agent, on SDGD-induced neuronal death, glutamate signaling (including the free glutamate concentration and expression of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA) receptor (GluR2) and N-methyl-D-aspartate (NMDA) receptor subunits (NR2B), and voltage-dependent K + and Na + channel currents were also investigated. Our results demonstrated that TPEN significantly suppressed increases in cell death, apoptosis, neuronal glutamate release into the culture medium, NR2B protein expression, and I K as well as decreased GluR2 protein expression and Na + channel activity in primary cultured neurons exposed to SDGD. These results suggest that TPEN could inhibit SDGD-induced neuronal death by modulating apoptosis, glutamate signaling (via ligand-gated channels such as AMPA and NMDA receptors), and voltage-gated K + and Na + channels in neurons. Hence, Zn 2+ chelation might be a promising approach for counteracting the neuronal loss caused by transient global ischemia. Moreover, TPEN could represent a potential cell-targeted therapy.

Theoretical Limitations on Functional Imaging Resolution in Auditory Cortex

PubMed Central

Chen, Thomas L.; Watkins, Paul V.; Barbour, Dennis L.

2010-01-01

Functional imaging can reveal detailed organizational structure in cerebral cortical areas, but neuronal response features and local neural interconnectivity can influence the resulting images, possibly limiting the inferences that can be drawn about neural function. Discerning the fundamental principles of organizational structure in the auditory cortex of multiple species has been somewhat challenging historically both with functional imaging and with electrophysiology. A possible limitation affecting any methodology using pooled neuronal measures may be the relative distribution of response selectivity throughout the population of auditory cortex neurons. One neuronal response type inherited from the cochlea, for example, exhibits a receptive field that increases in size (i.e., decreases in selectivity) at higher stimulus intensities. Even though these neurons appear to represent a minority of auditory cortex neurons, they are likely to contribute disproportionately to the activity detected in functional images, especially if intense sounds are used for stimulation. To evaluate the potential influence of neuronal subpopulations upon functional images of primary auditory cortex, a model array representing cortical neurons was probed with virtual imaging experiments under various assumptions about the local circuit organization. As expected, different neuronal subpopulations were activated preferentially under different stimulus conditions. In fact, stimulus protocols that can preferentially excite selective neurons, resulting in a relatively sparse activation map, have the potential to improve the effective resolution of functional auditory cortical images. These experimental results also make predictions about auditory cortex organization that can be tested with refined functional imaging experiments. PMID:20079343

Mechano-sensitization of mammalian neuronal networks through expression of the bacterial large-conductance mechanosensitive ion channel

PubMed Central

Contestabile, Andrea; Moroni, Monica; Hallinan, Grace I.; Palazzolo, Gemma; Chad, John; Deinhardt, Katrin; Carugo, Dario

2018-01-01

ABSTRACT Development of remote stimulation techniques for neuronal tissues represents a challenging goal. Among the potential methods, mechanical stimuli are the most promising vectors to convey information non-invasively into intact brain tissue. In this context, selective mechano-sensitization of neuronal circuits would pave the way to develop a new cell-type-specific stimulation approach. We report here, for the first time, the development and characterization of mechano-sensitized neuronal networks through the heterologous expression of an engineered bacterial large-conductance mechanosensitive ion channel (MscL). The neuronal functional expression of the MscL was validated through patch-clamp recordings upon application of calibrated suction pressures. Moreover, we verified the effective development of in-vitro neuronal networks expressing the engineered MscL in terms of cell survival, number of synaptic puncta and spontaneous network activity. The pure mechanosensitivity of the engineered MscL, with its wide genetic modification library, may represent a versatile tool to further develop a mechano-genetic approach. This article has an associated First Person interview with the first author of the paper. PMID:29361543

Basic mathematical rules are encoded by primate prefrontal cortex neurons

PubMed Central

Bongard, Sylvia; Nieder, Andreas

2010-01-01

Mathematics is based on highly abstract principles, or rules, of how to structure, process, and evaluate numerical information. If and how mathematical rules can be represented by single neurons, however, has remained elusive. We therefore recorded the activity of individual prefrontal cortex (PFC) neurons in rhesus monkeys required to switch flexibly between “greater than” and “less than” rules. The monkeys performed this task with different numerical quantities and generalized to set sizes that had not been presented previously, indicating that they had learned an abstract mathematical principle. The most prevalent activity recorded from randomly selected PFC neurons reflected the mathematical rules; purely sensory- and memory-related activity was almost absent. These data show that single PFC neurons have the capacity to represent flexible operations on most abstract numerical quantities. Our findings support PFC network models implementing specific “rule-coding” units that control the flow of information between segregated input, memory, and output layers. We speculate that these neuronal circuits in the monkey lateral PFC could readily have been adopted in the course of primate evolution for syntactic processing of numbers in formalized mathematical systems. PMID:20133872

Single neuron modeling and data assimilation in BNST neurons

NASA Astrophysics Data System (ADS)

Farsian, Reza

Neurons, although tiny in size, are vastly complicated systems, which are responsible for the most basic yet essential functions of any nervous system. Even the most simple models of single neurons are usually high dimensional, nonlinear, and contain many parameters and states which are unobservable in a typical neurophysiological experiment. One of the most fundamental problems in experimental neurophysiology is the estimation of these parameters and states, since knowing their values is essential in identification, model construction, and forward prediction of biological neurons. Common methods of parameter and state estimation do not perform well for neural models due to their high dimensionality and nonlinearity. In this dissertation, two alternative approaches for parameters and state estimation of biological neurons have been demonstrated: dynamical parameter estimation (DPE) and a Markov Chain Monte Carlo (MCMC) method. The first method uses elements of chaos control and synchronization theory for parameter and state estimation. MCMC is a statistical approach which uses a path integral formulation to evaluate a mean and an error bound for these unobserved parameters and states. These methods have been applied to biological system of neurons in Bed Nucleus of Stria Termialis neurons (BNST) of rats. State and parameters of neurons in both systems were estimated, and their value were used for recreating a realistic model and predicting the behavior of the neurons successfully. The knowledge of biological parameters can ultimately provide a better understanding of the internal dynamics of a neuron in order to build robust models of neuron networks.

Modeling functional neuroanatomy for an anatomy information system.

PubMed

Niggemann, Jörg M; Gebert, Andreas; Schulz, Stefan

2008-01-01

Existing neuroanatomical ontologies, databases and information systems, such as the Foundational Model of Anatomy (FMA), represent outgoing connections from brain structures, but cannot represent the "internal wiring" of structures and as such, cannot distinguish between different independent connections from the same structure. Thus, a fundamental aspect of Neuroanatomy, the functional pathways and functional systems of the brain such as the pupillary light reflex system, is not adequately represented. This article identifies underlying anatomical objects which are the source of independent connections (collections of neurons) and uses these as basic building blocks to construct a model of functional neuroanatomy and its functional pathways. The basic representational elements of the model are unnamed groups of neurons or groups of neuron segments. These groups, their relations to each other, and the relations to the objects of macroscopic anatomy are defined. The resulting model can be incorporated into the FMA. The capabilities of the presented model are compared to the FMA and the Brain Architecture Management System (BAMS). Internal wiring as well as functional pathways can correctly be represented and tracked. This model bridges the gap between representations of single neurons and their parts on the one hand and representations of spatial brain structures and areas on the other hand. It is capable of drawing correct inferences on pathways in a nervous system. The object and relation definitions are related to the Open Biomedical Ontology effort and its relation ontology, so that this model can be further developed into an ontology of neuronal functional systems.

How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

PubMed

Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C

2014-03-01

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.

Adding dynamic rules to self-organizing fuzzy systems

NASA Technical Reports Server (NTRS)

Buhusi, Catalin V.

1992-01-01

This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.

Interlayer neurones in the rat superior colliculus: a tracer study using Dil/Di-ASP.

PubMed

Hilbig, H; Schierwagen, A

1994-01-12

Five different populations of interlayer neurones (ILNs) can be described after DiI/Di-ASP tracing in rat superior colliculus (SC). All of these labelled neurones preferentially lay in the rostro-medial part of the SC. Most of them are located in the stratum opticum and in the stratum griseum superficiale. Our results indicate that ILNs represent a minority of neurones in the superficial layers but may constitute a substantial population of neurones in the stratum opticum connecting the visual and the multimodal collicular layers.

Genetic correction of tauopathy phenotypes in neurons derived from human induced pluripotent stem cells.

PubMed

Fong, Helen; Wang, Chengzhong; Knoferle, Johanna; Walker, David; Balestra, Maureen E; Tong, Leslie M; Leung, Laura; Ring, Karen L; Seeley, William W; Karydas, Anna; Kshirsagar, Mihir A; Boxer, Adam L; Kosik, Kenneth S; Miller, Bruce L; Huang, Yadong

2013-01-01

Tauopathies represent a group of neurodegenerative disorders characterized by the accumulation of pathological TAU protein in brains. We report a human neuronal model of tauopathy derived from induced pluripotent stem cells (iPSCs) carrying a TAU-A152T mutation. Using zinc-finger nuclease-mediated gene editing, we generated two isogenic iPSC lines: one with the mutation corrected, and another with the homozygous mutation engineered. The A152T mutation increased TAU fragmentation and phosphorylation, leading to neurodegeneration and especially axonal degeneration. These cellular phenotypes were consistent with those observed in a patient with TAU-A152T. Upon mutation correction, normal neuronal and axonal morphologies were restored, accompanied by decreases in TAU fragmentation and phosphorylation, whereas the severity of tauopathy was intensified in neurons with the homozygous mutation. These isogenic TAU-iPSC lines represent a critical advancement toward the accurate modeling and mechanistic study of tauopathies with human neurons and will be invaluable for drug-screening efforts and future cell-based therapies.

Correction to verdonck and tuerlinckx (2014).

PubMed

2015-01-01

Reports an error in "The Ising Decision Maker: A binary stochastic network for choice response time" by Stijn Verdonck and Francis Tuerlinckx (Psychological Review, 2014[Jul], Vol 121[3], 422-462). An inaccurate assumption in Appendix B (provided in the erratum) led to an oversimplified result in Equation 18 (the diffusion equations associated with the microscopically defined dynamics). The authors sincerely thank Rani Moran for making them aware of the problem. Only the expression of the diffusion coefficient D is incorrect, and should be changed, as indicated in the erratum. Both the cause of the problem and the solution are also explained in the erratum. (The following abstract of the original article appeared in record 2014-31650-006.) The Ising Decision Maker (IDM) is a new formal model for speeded two-choice decision making derived from the stochastic Hopfield network or dynamic Ising model. On a microscopic level, it consists of 2 pools of binary stochastic neurons with pairwise interactions. Inside each pool, neurons excite each other, whereas between pools, neurons inhibit each other. The perceptual input is represented by an external excitatory field. Using methods from statistical mechanics, the high-dimensional network of neurons (microscopic level) is reduced to a two-dimensional stochastic process, describing the evolution of the mean neural activity per pool (macroscopic level). The IDM can be seen as an abstract, analytically tractable multiple attractor network model of information accumulation. In this article, the properties of the IDM are studied, the relations to existing models are discussed, and it is shown that the most important basic aspects of two-choice response time data can be reproduced. In addition, the IDM is shown to predict a variety of observed psychophysical relations such as Piéron's law, the van der Molen-Keuss effect, and Weber's law. Using Bayesian methods, the model is fitted to both simulated and real data, and its performance is compared to the Ratcliff diffusion model. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Imaging auditory representations of song and syllables in populations of sensorimotor neurons essential to vocal communication.

PubMed

Peh, Wendy Y X; Roberts, Todd F; Mooney, Richard

2015-04-08

Vocal communication depends on the coordinated activity of sensorimotor neurons important to vocal perception and production. How vocalizations are represented by spatiotemporal activity patterns in these neuronal populations remains poorly understood. Here we combined intracellular recordings and two-photon calcium imaging in anesthetized adult zebra finches (Taeniopygia guttata) to examine how learned birdsong and its component syllables are represented in identified projection neurons (PNs) within HVC, a sensorimotor region important for song perception and production. These experiments show that neighboring HVC PNs can respond at markedly different times to song playback and that different syllables activate spatially intermingled PNs within a local (~100 μm) region of HVC. Moreover, noise correlations were stronger between PNs that responded most strongly to the same syllable and were spatially graded within and between classes of PNs. These findings support a model in which syllabic and temporal features of song are represented by spatially intermingled PNs functionally organized into cell- and syllable-type networks within local spatial scales in HVC. Copyright © 2015 the authors 0270-6474/15/355589-17$15.00/0.

Learning about Expectation Violation from Prediction Error Paradigms – A Meta-Analysis on Brain Processes Following a Prediction Error

PubMed Central

D’Astolfo, Lisa; Rief, Winfried

2017-01-01

Modifying patients’ expectations by exposing them to expectation violation situations (thus maximizing the difference between the expected and the actual situational outcome) is proposed to be a crucial mechanism for therapeutic success for a variety of different mental disorders. However, clinical observations suggest that patients often maintain their expectations regardless of experiences contradicting their expectations. It remains unclear which information processing mechanisms lead to modification or persistence of patients’ expectations. Insight in the processing could be provided by Neuroimaging studies investigating prediction error (PE, i.e., neuronal reactions to non-expected stimuli). Two methods are often used to investigate the PE: (1) paradigms, in which participants passively observe PEs (”passive” paradigms) and (2) paradigms, which encourage a behavioral adaptation following a PE (“active” paradigms). These paradigms are similar to the methods used to induce expectation violations in clinical settings: (1) the confrontation with an expectation violation situation and (2) an enhanced confrontation in which the patient actively challenges his expectation. We used this similarity to gain insight in the different neuronal processing of the two PE paradigms. We performed a meta-analysis contrasting neuronal activity of PE paradigms encouraging a behavioral adaptation following a PE and paradigms enforcing passiveness following a PE. We found more neuronal activity in the striatum, the insula and the fusiform gyrus in studies encouraging behavioral adaptation following a PE. Due to the involvement of reward assessment and avoidance learning associated with the striatum and the insula we propose that the deliberate execution of action alternatives following a PE is associated with the integration of new information into previously existing expectations, therefore leading to an expectation change. While further research is needed to directly assess expectations of participants, this study provides new insights into the information processing mechanisms following an expectation violation. PMID:28804467

Learning about Expectation Violation from Prediction Error Paradigms - A Meta-Analysis on Brain Processes Following a Prediction Error.

PubMed

D'Astolfo, Lisa; Rief, Winfried

2017-01-01

Modifying patients' expectations by exposing them to expectation violation situations (thus maximizing the difference between the expected and the actual situational outcome) is proposed to be a crucial mechanism for therapeutic success for a variety of different mental disorders. However, clinical observations suggest that patients often maintain their expectations regardless of experiences contradicting their expectations. It remains unclear which information processing mechanisms lead to modification or persistence of patients' expectations. Insight in the processing could be provided by Neuroimaging studies investigating prediction error (PE, i.e., neuronal reactions to non-expected stimuli). Two methods are often used to investigate the PE: (1) paradigms, in which participants passively observe PEs ("passive" paradigms) and (2) paradigms, which encourage a behavioral adaptation following a PE ("active" paradigms). These paradigms are similar to the methods used to induce expectation violations in clinical settings: (1) the confrontation with an expectation violation situation and (2) an enhanced confrontation in which the patient actively challenges his expectation. We used this similarity to gain insight in the different neuronal processing of the two PE paradigms. We performed a meta-analysis contrasting neuronal activity of PE paradigms encouraging a behavioral adaptation following a PE and paradigms enforcing passiveness following a PE. We found more neuronal activity in the striatum, the insula and the fusiform gyrus in studies encouraging behavioral adaptation following a PE. Due to the involvement of reward assessment and avoidance learning associated with the striatum and the insula we propose that the deliberate execution of action alternatives following a PE is associated with the integration of new information into previously existing expectations, therefore leading to an expectation change. While further research is needed to directly assess expectations of participants, this study provides new insights into the information processing mechanisms following an expectation violation.

Spatial Representativeness Error in the Ground-Level Observation Networks for Black Carbon Radiation Absorption

NASA Astrophysics Data System (ADS)

Wang, Rong; Andrews, Elisabeth; Balkanski, Yves; Boucher, Olivier; Myhre, Gunnar; Samset, Bjørn Hallvard; Schulz, Michael; Schuster, Gregory L.; Valari, Myrto; Tao, Shu

2018-02-01

There is high uncertainty in the direct radiative forcing of black carbon (BC), an aerosol that strongly absorbs solar radiation. The observation-constrained estimate, which is several times larger than the bottom-up estimate, is influenced by the spatial representativeness error due to the mesoscale inhomogeneity of the aerosol fields and the relatively low resolution of global chemistry-transport models. Here we evaluated the spatial representativeness error for two widely used observational networks (AErosol RObotic NETwork and Global Atmosphere Watch) by downscaling the geospatial grid in a global model of BC aerosol absorption optical depth to 0.1° × 0.1°. Comparing the models at a spatial resolution of 2° × 2° with BC aerosol absorption at AErosol RObotic NETwork sites (which are commonly located near emission hot spots) tends to cause a global spatial representativeness error of 30%, as a positive bias for the current top-down estimate of global BC direct radiative forcing. By contrast, the global spatial representativeness error will be 7% for the Global Atmosphere Watch network, because the sites are located in such a way that there are almost an equal number of sites with positive or negative representativeness error.

PeakCaller: an automated graphical interface for the quantification of intracellular calcium obtained by high-content screening.

PubMed

Artimovich, Elena; Jackson, Russell K; Kilander, Michaela B C; Lin, Yu-Chih; Nestor, Michael W

2017-10-16

Intracellular calcium is an important ion involved in the regulation and modulation of many neuronal functions. From regulating cell cycle and proliferation to initiating signaling cascades and regulating presynaptic neurotransmitter release, the concentration and timing of calcium activity governs the function and fate of neurons. Changes in calcium transients can be used in high-throughput screening applications as a basic measure of neuronal maturity, especially in developing or immature neuronal cultures derived from stem cells. Using human induced pluripotent stem cell derived neurons and dissociated mouse cortical neurons combined with the calcium indicator Fluo-4, we demonstrate that PeakCaller reduces type I and type II error in automated peak calling when compared to the oft-used PeakFinder algorithm under both basal and pharmacologically induced conditions. Here we describe PeakCaller, a novel MATLAB script and graphical user interface for the quantification of intracellular calcium transients in neuronal cultures. PeakCaller allows the user to set peak parameters and smoothing algorithms to best fit their data set. This new analysis script will allow for automation of calcium measurements and is a powerful software tool for researchers interested in high-throughput measurements of intracellular calcium.

Accurate counting of neurons in frozen sections: some necessary precautions.

PubMed Central

Cooper, J D; Payne, J N; Horobin, R W

1988-01-01

In 30 microns frozen sections of rat midbrain the retrograde axonal transport of diamidino yellow, a fluorescent tracer, was used to demonstrate a population of neurons in the substantia nigra. However, when visualisation was carried out using the routine Nissl method a significant proportion of neurons failed to stain. As the presence of the retrograde tracer did not affect Nissl staining of such cells, such incomplete staining, with consequent underestimation of neuronal populations, is probably a common error in similar material. Further investigation revealed that the proportion of such unstained neurons was greater when the staining time was short, when stain concentration was low, or when section thickness was increased. Some stains were worse in this respect than others. Cresyl fast violet resulted in the highest proportion of unstained neurons, thionin resulted in the lowest proportion. It was concluded that the rate of diffusion of the stain into the section was the main factor limiting the staining of neurons present. Staining with pure thionin at 0.1% concentration for at least 3 minutes and with sections no thicker than 30 microns is one regime which would avoid this problem. Images Fig. 1 PMID:2461923

Activity patterns of serotonin neurons underlying cognitive flexibility

PubMed Central

Matias, Sara; Lottem, Eran; Dugué, Guillaume P; Mainen, Zachary F

2017-01-01

Serotonin is implicated in mood and affective disorders. However, growing evidence suggests that a core endogenous role is to promote flexible adaptation to changes in the causal structure of the environment, through behavioral inhibition and enhanced plasticity. We used long-term photometric recordings in mice to study a population of dorsal raphe serotonin neurons, whose activity we could link to normal reversal learning using pharmacogenetics. We found that these neurons are activated by both positive and negative prediction errors, and thus report signals similar to those proposed to promote learning in conditions of uncertainty. Furthermore, by comparing the cue responses of serotonin and dopamine neurons, we found differences in learning rates that could explain the importance of serotonin in inhibiting perseverative responding. Our findings show how the activity patterns of serotonin neurons support a role in cognitive flexibility, and suggest a revised model of dopamine–serotonin opponency with potential clinical implications. DOI: http://dx.doi.org/10.7554/eLife.20552.001 PMID:28322190

A network of spiking neurons for computing sparse representations in an energy efficient way

PubMed Central

Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B.

2013-01-01

Computing sparse redundant representations is an important problem both in applied mathematics and neuroscience. In many applications, this problem must be solved in an energy efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating via low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, such operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We compare the numerical performance of HDA with existing algorithms and show that in the asymptotic regime the representation error of HDA decays with time, t, as 1/t. We show that HDA is stable against time-varying noise, specifically, the representation error decays as 1/t for Gaussian white noise. PMID:22920853

Learning-Induced Plasticity in Medial Prefrontal Cortex Predicts Preference Malleability

PubMed Central

Garvert, Mona M.; Moutoussis, Michael; Kurth-Nelson, Zeb; Behrens, Timothy E.J.; Dolan, Raymond J.

2015-01-01

Summary Learning induces plasticity in neuronal networks. As neuronal populations contribute to multiple representations, we reasoned plasticity in one representation might influence others. We used human fMRI repetition suppression to show that plasticity induced by learning another individual’s values impacts upon a value representation for oneself in medial prefrontal cortex (mPFC), a plasticity also evident behaviorally in a preference shift. We show this plasticity is driven by a striatal “prediction error,” signaling the discrepancy between the other’s choice and a subject’s own preferences. Thus, our data highlight that mPFC encodes agent-independent representations of subjective value, such that prediction errors simultaneously update multiple agents’ value representations. As the resulting change in representational similarity predicts interindividual differences in the malleability of subjective preferences, our findings shed mechanistic light on complex human processes such as the powerful influence of social interaction on beliefs and preferences. PMID:25611512

A network of spiking neurons for computing sparse representations in an energy-efficient way.

PubMed

Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B

2012-11-01

Computing sparse redundant representations is an important problem in both applied mathematics and neuroscience. In many applications, this problem must be solved in an energy-efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating by low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, the operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We show that the numerical performance of HDA is on par with existing algorithms. In the asymptotic regime, the representation error of HDA decays with time, t, as 1/t. HDA is stable against time-varying noise; specifically, the representation error decays as 1/√t for gaussian white noise.

A computational substrate for incentive salience.

PubMed

McClure, Samuel M; Daw, Nathaniel D; Montague, P Read

2003-08-01

Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron function diminishes the ability of an animal to motivate behaviors directed at acquiring rewards. These lesion experiments have raised the possibility that dopamine release encodes a measure of the incentive value of a contemplated behavioral act. The most complete psychological idea that captures this notion frames the dopamine signal as carrying 'incentive salience'. On the surface, these two competing accounts of dopamine function seem incommensurate. To the contrary, we demonstrate that both of these functions can be captured in a single computational model of the involvement of dopamine in reward prediction for the purpose of reward seeking.

Morphological elucidation of basal ganglia circuits contributing reward prediction

PubMed Central

Fujiyama, Fumino; Takahashi, Susumu; Karube, Fuyuki

2015-01-01

Electrophysiological studies in monkeys have shown that dopaminergic neurons respond to the reward prediction error. In addition, striatal neurons alter their responsiveness to cortical or thalamic inputs in response to the dopamine signal, via the mechanism of dopamine-regulated synaptic plasticity. These findings have led to the hypothesis that the striatum exhibits synaptic plasticity under the influence of the reward prediction error and conduct reinforcement learning throughout the basal ganglia circuits. The reinforcement learning model is useful; however, the mechanism by which such a process emerges in the basal ganglia needs to be anatomically explained. The actor–critic model has been previously proposed and extended by the existence of role sharing within the striatum, focusing on the striosome/matrix compartments. However, this hypothesis has been difficult to confirm morphologically, partly because of the complex structure of the striosome/matrix compartments. Here, we review recent morphological studies that elucidate the input/output organization of the striatal compartments. PMID:25698913

Corticostriatal circuit mechanisms of value-based action selection: Implementation of reinforcement learning algorithms and beyond.

PubMed

Morita, Kenji; Jitsev, Jenia; Morrison, Abigail

2016-09-15

Value-based action selection has been suggested to be realized in the corticostriatal local circuits through competition among neural populations. In this article, we review theoretical and experimental studies that have constructed and verified this notion, and provide new perspectives on how the local-circuit selection mechanisms implement reinforcement learning (RL) algorithms and computations beyond them. The striatal neurons are mostly inhibitory, and lateral inhibition among them has been classically proposed to realize "Winner-Take-All (WTA)" selection of the maximum-valued action (i.e., 'max' operation). Although this view has been challenged by the revealed weakness, sparseness, and asymmetry of lateral inhibition, which suggest more complex dynamics, WTA-like competition could still occur on short time scales. Unlike the striatal circuit, the cortical circuit contains recurrent excitation, which may enable retention or temporal integration of information and probabilistic "soft-max" selection. The striatal "max" circuit and the cortical "soft-max" circuit might co-implement an RL algorithm called Q-learning; the cortical circuit might also similarly serve for other algorithms such as SARSA. In these implementations, the cortical circuit presumably sustains activity representing the executed action, which negatively impacts dopamine neurons so that they can calculate reward-prediction-error. Regarding the suggested more complex dynamics of striatal, as well as cortical, circuits on long time scales, which could be viewed as a sequence of short WTA fragments, computational roles remain open: such a sequence might represent (1) sequential state-action-state transitions, constituting replay or simulation of the internal model, (2) a single state/action by the whole trajectory, or (3) probabilistic sampling of state/action. Copyright © 2016. Published by Elsevier B.V.

Lactate release from astrocytes to neurons contributes to cocaine memory formation.

PubMed

Boury-Jamot, Benjamin; Halfon, Olivier; Magistretti, Pierre J; Boutrel, Benjamin

2016-12-01

The identification of neural substrates underlying the long lasting debilitating impact of drug cues is critical for developing novel therapeutic tools. Metabolic coupling has long been considered a key mechanism through which astrocytes and neurons actively interact in response of neuronal activity, but recent findings suggested that disrupting metabolic coupling may represent an innovative approach to prevent memory formation, in particular drug-related memories. Here, we review converging evidence illustrating how memory and addiction share neural circuitry and molecular mechanisms implicating lactate-mediated metabolic coupling between astrocytes and neurons. With several aspects of addiction depending on mnemonic processes elicited by drug experience, disrupting lactate transport involved in the formation of a pathological learning, linking the incentive, and motivational effects of drugs with drug-conditioned stimuli represent a promising approach to encourage abstinence. © 2016 WILEY Periodicals, Inc.

Visual Receptive Field Heterogeneity and Functional Connectivity of Adjacent Neurons in Primate Frontoparietal Association Cortices.

PubMed

Viswanathan, Pooja; Nieder, Andreas

2017-09-13

The basic organization principles of the primary visual cortex (V1) are commonly assumed to also hold in the association cortex such that neurons within a cortical column share functional connectivity patterns and represent the same region of the visual field. We mapped the visual receptive fields (RFs) of neurons recorded at the same electrode in the ventral intraparietal area (VIP) and the lateral prefrontal cortex (PFC) of rhesus monkeys. We report that the spatial characteristics of visual RFs between adjacent neurons differed considerably, with increasing heterogeneity from VIP to PFC. In addition to RF incongruences, we found differential functional connectivity between putative inhibitory interneurons and pyramidal cells in PFC and VIP. These findings suggest that local RF topography vanishes with hierarchical distance from visual cortical input and argue for increasingly modified functional microcircuits in noncanonical association cortices that contrast V1. SIGNIFICANCE STATEMENT Our visual field is thought to be represented faithfully by the early visual brain areas; all the information from a certain region of the visual field is conveyed to neurons situated close together within a functionally defined cortical column. We examined this principle in the association areas, PFC, and ventral intraparietal area of rhesus monkeys and found that adjacent neurons represent markedly different areas of the visual field. This is the first demonstration of such noncanonical organization of these brain areas. Copyright © 2017 the authors 0270-6474/17/378919-10$15.00/0.

Automatically tracking neurons in a moving and deforming brain

PubMed Central

Nguyen, Jeffrey P.; Linder, Ashley N.; Plummer, George S.; Shaevitz, Joshua W.

2017-01-01

Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal’s brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches. PMID:28545068

Automatically tracking neurons in a moving and deforming brain.

PubMed

Nguyen, Jeffrey P; Linder, Ashley N; Plummer, George S; Shaevitz, Joshua W; Leifer, Andrew M

2017-05-01

Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal's brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches.

Modeling Pharmacological Clock and Memory Patterns of Interval Timing in a Striatal Beat-Frequency Model with Realistic, Noisy Neurons

PubMed Central

Oprisan, Sorinel A.; Buhusi, Catalin V.

2011-01-01

In most species, the capability of perceiving and using the passage of time in the seconds-to-minutes range (interval timing) is not only accurate but also scalar: errors in time estimation are linearly related to the estimated duration. The ubiquity of scalar timing extends over behavioral, lesion, and pharmacological manipulations. For example, in mammals, dopaminergic drugs induce an immediate, scalar change in the perceived time (clock pattern), whereas cholinergic drugs induce a gradual, scalar change in perceived time (memory pattern). How do these properties emerge from unreliable, noisy neurons firing in the milliseconds range? Neurobiological information relative to the brain circuits involved in interval timing provide support for an striatal beat frequency (SBF) model, in which time is coded by the coincidental activation of striatal spiny neurons by cortical neural oscillators. While biologically plausible, the impracticality of perfect oscillators, or their lack thereof, questions this mechanism in a brain with noisy neurons. We explored the computational mechanisms required for the clock and memory patterns in an SBF model with biophysically realistic and noisy Morris–Lecar neurons (SBF–ML). Under the assumption that dopaminergic drugs modulate the firing frequency of cortical oscillators, and that cholinergic drugs modulate the memory representation of the criterion time, we show that our SBF–ML model can reproduce the pharmacological clock and memory patterns observed in the literature. Numerical results also indicate that parameter variability (noise) – which is ubiquitous in the form of small fluctuations in the intrinsic frequencies of neural oscillators within and between trials, and in the errors in recording/retrieving stored information related to criterion time – seems to be critical for the time-scale invariance of the clock and memory patterns. PMID:21977014

Automatic parameter estimation of multicompartmental neuron models via minimization of trace error with control adjustment.

PubMed

Brookings, Ted; Goeritz, Marie L; Marder, Eve

2014-11-01

We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy. Copyright © 2014 the American Physiological Society.

Coupled Activation of Primary Sensory Neurons Contributes to Chronic Pain.

PubMed

Kim, Yu Shin; Anderson, Michael; Park, Kyoungsook; Zheng, Qin; Agarwal, Amit; Gong, Catherine; Saijilafu; Young, LeAnne; He, Shaoqiu; LaVinka, Pamela Colleen; Zhou, Fengquan; Bergles, Dwight; Hanani, Menachem; Guan, Yun; Spray, David C; Dong, Xinzhong

2016-09-07

Primary sensory neurons in the DRG play an essential role in initiating pain by detecting painful stimuli in the periphery. Tissue injury can sensitize DRG neurons, causing heightened pain sensitivity, often leading to chronic pain. Despite the functional importance, how DRG neurons function at a population level is unclear due to the lack of suitable tools. Here we developed an imaging technique that allowed us to simultaneously monitor the activities of >1,600 neurons/DRG in live mice and discovered a striking neuronal coupling phenomenon that adjacent neurons tend to activate together following tissue injury. This coupled activation occurs among various neurons and is mediated by an injury-induced upregulation of gap junctions in glial cells surrounding DRG neurons. Blocking gap junctions attenuated neuronal coupling and mechanical hyperalgesia. Therefore, neuronal coupling represents a new form of neuronal plasticity in the DRG and contributes to pain hypersensitivity by "hijacking" neighboring neurons through gap junctions. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

Fast maximum likelihood estimation using continuous-time neural point process models.

PubMed

Lepage, Kyle Q; MacDonald, Christopher J

2015-06-01

A recent report estimates that the number of simultaneously recorded neurons is growing exponentially. A commonly employed statistical paradigm using discrete-time point process models of neural activity involves the computation of a maximum-likelihood estimate. The time to computate this estimate, per neuron, is proportional to the number of bins in a finely spaced discretization of time. By using continuous-time models of neural activity and the optimally efficient Gaussian quadrature, memory requirements and computation times are dramatically decreased in the commonly encountered situation where the number of parameters p is much less than the number of time-bins n. In this regime, with q equal to the quadrature order, memory requirements are decreased from O(np) to O(qp), and the number of floating-point operations are decreased from O(np(2)) to O(qp(2)). Accuracy of the proposed estimates is assessed based upon physiological consideration, error bounds, and mathematical results describing the relation between numerical integration error and numerical error affecting both parameter estimates and the observed Fisher information. A check is provided which is used to adapt the order of numerical integration. The procedure is verified in simulation and for hippocampal recordings. It is found that in 95 % of hippocampal recordings a q of 60 yields numerical error negligible with respect to parameter estimate standard error. Statistical inference using the proposed methodology is a fast and convenient alternative to statistical inference performed using a discrete-time point process model of neural activity. It enables the employment of the statistical methodology available with discrete-time inference, but is faster, uses less memory, and avoids any error due to discretization.

Recurrent Neural Networks With Auxiliary Memory Units.

PubMed

Wang, Jianyong; Zhang, Lei; Guo, Quan; Yi, Zhang

2018-05-01

Memory is one of the most important mechanisms in recurrent neural networks (RNNs) learning. It plays a crucial role in practical applications, such as sequence learning. With a good memory mechanism, long term history can be fused with current information, and can thus improve RNNs learning. Developing a suitable memory mechanism is always desirable in the field of RNNs. This paper proposes a novel memory mechanism for RNNs. The main contributions of this paper are: 1) an auxiliary memory unit (AMU) is proposed, which results in a new special RNN model (AMU-RNN), separating the memory and output explicitly and 2) an efficient learning algorithm is developed by employing the technique of error flow truncation. The proposed AMU-RNN model, together with the developed learning algorithm, can learn and maintain stable memory over a long time range. This method overcomes both the learning conflict problem and gradient vanishing problem. Unlike the traditional method, which mixes the memory and output with a single neuron in a recurrent unit, the AMU provides an auxiliary memory neuron to maintain memory in particular. By separating the memory and output in a recurrent unit, the problem of learning conflicts can be eliminated easily. Moreover, by using the technique of error flow truncation, each auxiliary memory neuron ensures constant error flow during the learning process. The experiments demonstrate good performance of the proposed AMU-RNNs and the developed learning algorithm. The method exhibits quite efficient learning performance with stable convergence in the AMU-RNN learning and outperforms the state-of-the-art RNN models in sequence generation and sequence classification tasks.

Effect of eye position on saccades and neuronal responses to acoustic stimuli in the superior colliculus of the behaving cat.

PubMed

Populin, Luis C; Tollin, Daniel J; Yin, Tom C T

2004-10-01

We examined the motor error hypothesis of visual and auditory interaction in the superior colliculus (SC), first tested by Jay and Sparks in the monkey. We trained cats to direct their eyes to the location of acoustic sources and studied the effects of eye position on both the ability of cats to localize sounds and the auditory responses of SC neurons with the head restrained. Sound localization accuracy was generally not affected by initial eye position, i.e., accuracy was not proportionally affected by the deviation of the eyes from the primary position at the time of stimulus presentation, showing that eye position is taken into account when orienting to acoustic targets. The responses of most single SC neurons to acoustic stimuli in the intact cat were modulated by eye position in the direction consistent with the predictions of the "motor error" hypothesis, but the shift accounted for only two-thirds of the initial deviation of the eyes. However, when the average horizontal sound localization error, which was approximately 35% of the target amplitude, was taken into account, the magnitude of the horizontal shifts in the SC auditory receptive fields matched the observed behavior. The modulation by eye position was not due to concomitant movements of the external ears, as confirmed by recordings carried out after immobilizing the pinnae of one cat. However, the pattern of modulation after pinnae immobilization was inconsistent with the observations in the intact cat, suggesting that, in the intact animal, information about the position of the pinnae may be taken into account.

POSTNATAL PHENOTYPE AND LOCALIZATION OF SPINAL CORD V1 DERIVED INTERNEURONS

PubMed Central

Alvarez, Francisco J.; Jonas, Philip C.; Sapir, Tamar; Hartley, Robert; Berrocal, Maria C.; Geiman, Eric J.; Todd, Andrew J.; Goulding, Martyn

2010-01-01

Developmental studies identified four classes (V0, V1, V2, V3) of embryonic interneurons in the ventral spinal cord. Very little however is known about their adult phenotypes. In order to further characterize interneuron cell types in the adult, the location, neurotransmitter phenotype, calcium-buffering protein expression and axon distributions of V1-derived neurons in the mouse spinal cord was determined. In the mature (P20 and older) spinal cord, most V1-derived neurons are located in lateral LVII and in LIX, few in medial LVII and none in LVIII. Approximately 40% express calbindin and/or parvalbumin, while few express calretinin. Of seven groups of ventral interneurons identified according to calcium-buffering protein expression, two groups (1 and 4) correspond with V1-derived neurons. Group 1 are Renshaw cells and intensely express calbindin and coexpress parvalbumin and calretinin. They represent 9% of the V1 population. Group 4 express only parvalbumin and represent 27% of V1-derived neurons. V1-derived group 4 neurons receive contacts from primary sensory afferents and are therefore proprioceptive interneurons and the most ventral neurons in this group receive convergent calbindin-IR Renshaw cell inputs. This subgroup resembles Ia inhibitory interneurons (IaINs) and represents 13% of V1-derived neurons. Adult V1-interneuron axons target LIX and LVII and some enter the deep dorsal horn. V1-axons do not cross the midline. V1 derived axonal varicosities were mostly (>80%) glycinergic and a third were GABAergic. None were glutamatergic or cholinergic. In summary, V1 interneurons develop into ipsilaterally projecting, inhibitory interneurons that include Renshaw cells, Ia inhibitory interneurons and other unidentified proprioceptive interneurons. PMID:16255029

Two fast and accurate heuristic RBF learning rules for data classification.

PubMed

Rouhani, Modjtaba; Javan, Dawood S

2016-03-01

This paper presents new Radial Basis Function (RBF) learning methods for classification problems. The proposed methods use some heuristics to determine the spreads, the centers and the number of hidden neurons of network in such a way that the higher efficiency is achieved by fewer numbers of neurons, while the learning algorithm remains fast and simple. To retain network size limited, neurons are added to network recursively until termination condition is met. Each neuron covers some of train data. The termination condition is to cover all training data or to reach the maximum number of neurons. In each step, the center and spread of the new neuron are selected based on maximization of its coverage. Maximization of coverage of the neurons leads to a network with fewer neurons and indeed lower VC dimension and better generalization property. Using power exponential distribution function as the activation function of hidden neurons, and in the light of new learning approaches, it is proved that all data became linearly separable in the space of hidden layer outputs which implies that there exist linear output layer weights with zero training error. The proposed methods are applied to some well-known datasets and the simulation results, compared with SVM and some other leading RBF learning methods, show their satisfactory and comparable performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Exposure to bisphenol A affects GABAergic neuron differentiation in neurosphere cultures.

PubMed

Fukushima, Nobuyuki; Nagao, Tetsuji

2018-06-13

Endocrine-disrupting chemicals (EDCs) influence not only endocrine functions but also neuronal development and functions. In-vivo studies have suggested the relationship of EDC-induced neurobehavioral disorders with dysfunctions of neurotransmitter mechanisms including γ-aminobutyric acid (GABA)ergic mechanisms. However, whether EDCs affect GABAergic neuron differentiation remains unclear. In the present study, we show that a representative EDC, bisphenol A (BPA), affects GABAergic neuron differentiation. Cortical neurospheres prepared from embryonic mice were exposed to BPA for 7 days, and then neuronal differentiation was induced. We found that BPA exposure resulted in a decrease in the ratio of GABAergic neurons to total neurons. However, the same exposure stimulated the differentiation of neurons expressing calbindin, a calcium-binding protein observed in a subpopulation of GABAergic neurons. These findings suggested that BPA might influence the formation of an inhibitory neuronal network in developing cerebral cortex involved in the occurrence of neurobehavioral disorders.

Neurometaplasticity: Glucoallostasis control of plasticity of the neural networks of error commission, detection, and correction modulates neuroplasticity to influence task precision

NASA Astrophysics Data System (ADS)

Welcome, Menizibeya O.; Dane, Şenol; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

2017-12-01

The term "metaplasticity" is a recent one, which means plasticity of synaptic plasticity. Correspondingly, neurometaplasticity simply means plasticity of neuroplasticity, indicating that a previous plastic event determines the current plasticity of neurons. Emerging studies suggest that neurometaplasticity underlie many neural activities and neurobehavioral disorders. In our previous work, we indicated that glucoallostasis is essential for the control of plasticity of the neural network that control error commission, detection and correction. Here we review recent works, which suggest that task precision depends on the modulatory effects of neuroplasticity on the neural networks of error commission, detection, and correction. Furthermore, we discuss neurometaplasticity and its role in error commission, detection, and correction.

Modeling Functional Neuroanatomy for an Anatomy Information System

PubMed Central

Niggemann, Jörg M.; Gebert, Andreas; Schulz, Stefan

2008-01-01

Objective Existing neuroanatomical ontologies, databases and information systems, such as the Foundational Model of Anatomy (FMA), represent outgoing connections from brain structures, but cannot represent the “internal wiring” of structures and as such, cannot distinguish between different independent connections from the same structure. Thus, a fundamental aspect of Neuroanatomy, the functional pathways and functional systems of the brain such as the pupillary light reflex system, is not adequately represented. This article identifies underlying anatomical objects which are the source of independent connections (collections of neurons) and uses these as basic building blocks to construct a model of functional neuroanatomy and its functional pathways. Design The basic representational elements of the model are unnamed groups of neurons or groups of neuron segments. These groups, their relations to each other, and the relations to the objects of macroscopic anatomy are defined. The resulting model can be incorporated into the FMA. Measurements The capabilities of the presented model are compared to the FMA and the Brain Architecture Management System (BAMS). Results Internal wiring as well as functional pathways can correctly be represented and tracked. Conclusion This model bridges the gap between representations of single neurons and their parts on the one hand and representations of spatial brain structures and areas on the other hand. It is capable of drawing correct inferences on pathways in a nervous system. The object and relation definitions are related to the Open Biomedical Ontology effort and its relation ontology, so that this model can be further developed into an ontology of neuronal functional systems. PMID:18579841

Egocentric and allocentric representations in auditory cortex

PubMed Central

Brimijoin, W. Owen; Bizley, Jennifer K.

2017-01-01

A key function of the brain is to provide a stable representation of an object’s location in the world. In hearing, sound azimuth and elevation are encoded by neurons throughout the auditory system, and auditory cortex is necessary for sound localization. However, the coordinate frame in which neurons represent sound space remains undefined: classical spatial receptive fields in head-fixed subjects can be explained either by sensitivity to sound source location relative to the head (egocentric) or relative to the world (allocentric encoding). This coordinate frame ambiguity can be resolved by studying freely moving subjects; here we recorded spatial receptive fields in the auditory cortex of freely moving ferrets. We found that most spatially tuned neurons represented sound source location relative to the head across changes in head position and direction. In addition, we also recorded a small number of neurons in which sound location was represented in a world-centered coordinate frame. We used measurements of spatial tuning across changes in head position and direction to explore the influence of sound source distance and speed of head movement on auditory cortical activity and spatial tuning. Modulation depth of spatial tuning increased with distance for egocentric but not allocentric units, whereas, for both populations, modulation was stronger at faster movement speeds. Our findings suggest that early auditory cortex primarily represents sound source location relative to ourselves but that a minority of cells can represent sound location in the world independent of our own position. PMID:28617796

SINGLE NEURON ACTIVITY AND THETA MODULATION IN POSTRHINAL CORTEX DURING VISUAL OBJECT DISCRIMINATION

PubMed Central

Furtak, Sharon C.; Ahmed, Omar J.; Burwell, Rebecca D.

2012-01-01

Postrhinal cortex, the rodent homolog of the primate parahippocampal cortex, processes spatial and contextual information. Our hypothesis of postrhinal function is that it serves to encode context, in part, by forming representations that link objects to places. We recorded postrhinal neuronal activity and local field potentials (LFPs) in rats trained on a two-choice, visual discrimination task. As predicted, a large proportion of postrhinal neurons signaled object-location conjunctions. In addition, postrhinal LFPs exhibited strong oscillatory rhythms in the theta band, and many postrhinal neurons were phase locked to theta. Although correlated with running speed, theta power was lower than predicted by speed alone immediately before and after choice. However, theta power was significantly increased following incorrect decisions, suggesting a role in signaling error. These findings provide evidence that postrhinal cortex encodes representations that link objects to places and suggest that postrhinal theta modulation extends to cognitive as well as spatial functions. PMID:23217745

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

Conditional Lyapunov exponents and transfer entropy in coupled bursting neurons under excitation and coupling mismatch

NASA Astrophysics Data System (ADS)

Soriano, Diogo C.; Santos, Odair V. dos; Suyama, Ricardo; Fazanaro, Filipe I.; Attux, Romis

2018-03-01

This work has a twofold aim: (a) to analyze an alternative approach for computing the conditional Lyapunov exponent (λcmax) aiming to evaluate the synchronization stability between nonlinear oscillators without solving the classical variational equations for the synchronization error dynamical system. In this first framework, an analytic reference value for λcmax is also provided in the context of Duffing master-slave scenario and precisely evaluated by the proposed numerical approach; (b) to apply this technique to the study of synchronization stability in chaotic Hindmarsh-Rose (HR) neuronal models under uni- and bi-directional resistive coupling and different excitation bias, which also considered the root mean square synchronization error, information theoretic measures and asymmetric transfer entropy in order to offer a better insight of the synchronization phenomenon. In particular, statistical and information theoretical measures were able to capture similarity increase between the neuronal oscillators just after a critical coupling value in accordance to the largest conditional Lyapunov exponent behavior. On the other hand, transfer entropy was able to detect neuronal emitter influence even in a weak coupling condition, i.e. under the increase of conditional Lyapunov exponent and apparently desynchronization tendency. In the performed set of numerical simulations, the synchronization measures were also evaluated for a two-dimensional parameter space defined by the neuronal coupling (emitter to a receiver neuron) and the (receiver) excitation current. Such analysis is repeated for different feedback couplings as well for different (emitter) excitation currents, revealing interesting characteristics of the attained synchronization region and conditions that facilitate the emergence of the synchronous behavior. These results provide a more detailed numerical insight of the underlying behavior of a HR in the excitation and coupling space, being in accordance with some general findings concerning HR coupling topologies. As a perspective, besides the synchronization overview from different standpoints, we hope that the proposed numerical approach for conditional Lyapunov exponent evaluation could outline a valuable strategy for studying neuronal stability, especially when realistic models are considered, in which analytical or even Jacobian evaluation could define a laborious or impracticable task.

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity

PubMed Central

Whittington, James C. R.; Bogacz, Rafal

2017-01-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output. PMID:28333583

Brain signaling and behavioral responses induced by exposure to (56)Fe-particle radiation

NASA Technical Reports Server (NTRS)

Denisova, N. A.; Shukitt-Hale, B.; Rabin, B. M.; Joseph, J. A.

2002-01-01

Previous experiments have demonstrated that exposure to 56Fe-particle irradiation (1.5 Gy, 1 GeV) produced aging-like accelerations in neuronal and behavioral deficits. Astronauts on long-term space flights will be exposed to similar heavy-particle radiations that might have similar deleterious effects on neuronal signaling and cognitive behavior. Therefore, the present study evaluated whether radiation-induced spatial learning and memory behavioral deficits are associated with region-specific brain signaling deficits by measuring signaling molecules previously found to be essential for behavior [pre-synaptic vesicle proteins, synaptobrevin and synaptophysin, and protein kinases, calcium-dependent PRKCs (also known as PKCs) and PRKA (PRKA RIIbeta)]. The results demonstrated a significant radiation-induced increase in reference memory errors. The increases in reference memory errors were significantly negatively correlated with striatal synaptobrevin and frontal cortical synaptophysin expression. Both synaptophysin and synaptobrevin are synaptic vesicle proteins that are important in cognition. Striatal PRKA, a memory signaling molecule, was also significantly negatively correlated with reference memory errors. Overall, our findings suggest that radiation-induced pre-synaptic facilitation may contribute to some previously reported radiation-induced decrease in striatal dopamine release and for the disruption of the central dopaminergic system integrity and dopamine-mediated behavior.

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.

PubMed

Whittington, James C R; Bogacz, Rafal

2017-05-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.

Brain signaling and behavioral responses induced by exposure to (56)Fe-particle radiation.

PubMed

Denisova, N A; Shukitt-Hale, B; Rabin, B M; Joseph, J A

2002-12-01

Previous experiments have demonstrated that exposure to 56Fe-particle irradiation (1.5 Gy, 1 GeV) produced aging-like accelerations in neuronal and behavioral deficits. Astronauts on long-term space flights will be exposed to similar heavy-particle radiations that might have similar deleterious effects on neuronal signaling and cognitive behavior. Therefore, the present study evaluated whether radiation-induced spatial learning and memory behavioral deficits are associated with region-specific brain signaling deficits by measuring signaling molecules previously found to be essential for behavior [pre-synaptic vesicle proteins, synaptobrevin and synaptophysin, and protein kinases, calcium-dependent PRKCs (also known as PKCs) and PRKA (PRKA RIIbeta)]. The results demonstrated a significant radiation-induced increase in reference memory errors. The increases in reference memory errors were significantly negatively correlated with striatal synaptobrevin and frontal cortical synaptophysin expression. Both synaptophysin and synaptobrevin are synaptic vesicle proteins that are important in cognition. Striatal PRKA, a memory signaling molecule, was also significantly negatively correlated with reference memory errors. Overall, our findings suggest that radiation-induced pre-synaptic facilitation may contribute to some previously reported radiation-induced decrease in striatal dopamine release and for the disruption of the central dopaminergic system integrity and dopamine-mediated behavior.

Neuropharmacology of performance monitoring.

PubMed

Jocham, Gerhard; Ullsperger, Markus

2009-01-01

Adaptive, goal-directed behavior requires that organisms evaluate their actions in terms of their outcomes. Neuroimaging studies show that unfavorable outcomes or situations with high level of conflict engage the posterior medial frontal cortex (pMFC). Recording of event-related potentials revealed that these situations are accompanied by a negative deflection, the so-called error-related negativity (ERN), which appears after an erroneous response or after negative feedback. Both activation of the pMFC and the ERN are thought to represent a signal that indicates the need for behavioral adjustment, and to recruit other brain regions that implement these adjustments. While many fMRI and EEG studies have shed light on the anatomical structures and the cognitive processes involved in performance monitoring, only very recently have researchers begun to investigate the underlying neurochemical mechanisms. Drawing on the putative involvement of dopamine (DA) neurons in coding a reward prediction error, an influential theory has ascribed a pivotal role to DA in performance monitoring. However, although important, DA is certainly not the only neuromodulator involved. Recent studies point to a role for serotonin, norepinephrine and GABA, but also for adenosine in performance monitoring. Here, we review the evidence for neurotransmitter effects on this function in humans. In this light, we critically discuss currently debated models of performance monitoring and potential alternatives.

Deep evolutionary origins of neurobiology

PubMed Central

Mancuso, Stefano

2009-01-01

It is generally assumed, both in common-sense argumentations and scientific concepts, that brains and neurons represent late evolutionary achievements which are present only in more advanced animals. Here we overview recently published data clearly revealing that our understanding of bacteria, unicellular eukaryotic organisms, plants, brains and neurons, rooted in the Aristotelian philosophy is flawed. Neural aspects of biological systems are obvious already in bacteria and unicellular biological units such as sexual gametes and diverse unicellular eukaryotic organisms. Altogether, processes and activities thought to represent evolutionary ‘recent’ specializations of the nervous system emerge rather to represent ancient and fundamental cell survival processes. PMID:19513267

A unifying view of synchronization for data assimilation in complex nonlinear networks

NASA Astrophysics Data System (ADS)

Abarbanel, Henry D. I.; Shirman, Sasha; Breen, Daniel; Kadakia, Nirag; Rey, Daniel; Armstrong, Eve; Margoliash, Daniel

2017-12-01

Networks of nonlinear systems contain unknown parameters and dynamical degrees of freedom that may not be observable with existing instruments. From observable state variables, we want to estimate the connectivity of a model of such a network and determine the full state of the model at the termination of a temporal observation window during which measurements transfer information to a model of the network. The model state at the termination of a measurement window acts as an initial condition for predicting the future behavior of the network. This allows the validation (or invalidation) of the model as a representation of the dynamical processes producing the observations. Once the model has been tested against new data, it may be utilized as a predictor of responses to innovative stimuli or forcing. We describe a general framework for the tasks involved in the "inverse" problem of determining properties of a model built to represent measured output from physical, biological, or other processes when the measurements are noisy, the model has errors, and the state of the model is unknown when measurements begin. This framework is called statistical data assimilation and is the best one can do in estimating model properties through the use of the conditional probability distributions of the model state variables, conditioned on observations. There is a very broad arena of applications of the methods described. These include numerical weather prediction, properties of nonlinear electrical circuitry, and determining the biophysical properties of functional networks of neurons. Illustrative examples will be given of (1) estimating the connectivity among neurons with known dynamics in a network of unknown connectivity, and (2) estimating the biophysical properties of individual neurons in vitro taken from a functional network underlying vocalization in songbirds.

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.

Spatial Representativeness Error in the Ground‐Level Observation Networks for Black Carbon Radiation Absorption

PubMed Central

Andrews, Elisabeth; Balkanski, Yves; Boucher, Olivier; Myhre, Gunnar; Samset, Bjørn Hallvard; Schulz, Michael; Schuster, Gregory L.; Valari, Myrto; Tao, Shu

2018-01-01

Abstract There is high uncertainty in the direct radiative forcing of black carbon (BC), an aerosol that strongly absorbs solar radiation. The observation‐constrained estimate, which is several times larger than the bottom‐up estimate, is influenced by the spatial representativeness error due to the mesoscale inhomogeneity of the aerosol fields and the relatively low resolution of global chemistry‐transport models. Here we evaluated the spatial representativeness error for two widely used observational networks (AErosol RObotic NETwork and Global Atmosphere Watch) by downscaling the geospatial grid in a global model of BC aerosol absorption optical depth to 0.1° × 0.1°. Comparing the models at a spatial resolution of 2° × 2° with BC aerosol absorption at AErosol RObotic NETwork sites (which are commonly located near emission hot spots) tends to cause a global spatial representativeness error of 30%, as a positive bias for the current top‐down estimate of global BC direct radiative forcing. By contrast, the global spatial representativeness error will be 7% for the Global Atmosphere Watch network, because the sites are located in such a way that there are almost an equal number of sites with positive or negative representativeness error. PMID:29937603

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

Roles of octopaminergic and dopaminergic neurons in appetitive and aversive memory recall in an insect.

PubMed

Mizunami, Makoto; Unoki, Sae; Mori, Yasuhiro; Hirashima, Daisuke; Hatano, Ai; Matsumoto, Yukihisa

2009-08-04

In insect classical conditioning, octopamine (the invertebrate counterpart of noradrenaline) or dopamine has been suggested to mediate reinforcing properties of appetitive or aversive unconditioned stimulus, respectively. However, the roles of octopaminergic and dopaminergic neurons in memory recall have remained unclear. We studied the roles of octopaminergic and dopaminergic neurons in appetitive and aversive memory recall in olfactory and visual conditioning in crickets. We found that pharmacological blockade of octopamine and dopamine receptors impaired aversive memory recall and appetitive memory recall, respectively, thereby suggesting that activation of octopaminergic and dopaminergic neurons and the resulting release of octopamine and dopamine are needed for appetitive and aversive memory recall, respectively. On the basis of this finding, we propose a new model in which it is assumed that two types of synaptic connections are formed by conditioning and are activated during memory recall, one type being connections from neurons representing conditioned stimulus to neurons inducing conditioned response and the other being connections from neurons representing conditioned stimulus to octopaminergic or dopaminergic neurons representing appetitive or aversive unconditioned stimulus, respectively. The former is called 'stimulus-response connection' and the latter is called 'stimulus-stimulus connection' by theorists studying classical conditioning in higher vertebrates. Our model predicts that pharmacological blockade of octopamine or dopamine receptors during the first stage of second-order conditioning does not impair second-order conditioning, because it impairs the formation of the stimulus-response connection but not the stimulus-stimulus connection. The results of our study with a cross-modal second-order conditioning were in full accordance with this prediction. We suggest that insect classical conditioning involves the formation of two kinds of memory traces, which match to stimulus-stimulus connection and stimulus-response connection. This is the first study to suggest that classical conditioning in insects involves, as does classical conditioning in higher vertebrates, the formation of stimulus-stimulus connection and its activation for memory recall, which are often called cognitive processes.

Populations of subplate and interstitial neurons in fetal and adult human telencephalon.

PubMed

Judaš, Miloš; Sedmak, Goran; Pletikos, Mihovil; Jovanov-Milošević, Nataša

2010-10-01

In the adult human telencephalon, subcortical (gyral) white matter contains a special population of interstitial neurons considered to be surviving descendants of fetal subplate neurons [Kostovic & Rakic (1980) Cytology and the time of origin of interstitial neurons in the white matter in infant and adult human and monkey telencephalon. J Neurocytol9, 219]. We designate this population of cells as superficial (gyral) interstitial neurons and describe their morphology and distribution in the postnatal and adult human cerebrum. Human fetal subplate neurons cannot be regarded as interstitial, because the subplate zone is an essential part of the fetal cortex, the major site of synaptogenesis and the 'waiting' compartment for growing cortical afferents, and contains both projection neurons and interneurons with distinct input-output connectivity. However, although the subplate zone is a transient fetal structure, many subplate neurons survive postnatally as superficial (gyral) interstitial neurons. The fetal white matter is represented by the intermediate zone and well-defined deep periventricular tracts of growing axons, such as the corpus callosum, anterior commissure, internal and external capsule, and the fountainhead of the corona radiata. These tracts gradually occupy the territory of transient fetal subventricular and ventricular zones.The human fetal white matter also contains distinct populations of deep fetal interstitial neurons, which, by virtue of their location, morphology, molecular phenotypes and advanced level of dendritic maturation, remain distinct from subplate neurons and neurons in adjacent structures (e.g. basal ganglia, basal forebrain). We describe the morphological, histochemical (nicotinamide-adenine dinucleotide phosphate-diaphorase) and immunocytochemical (neuron-specific nuclear protein, microtubule-associated protein-2, calbindin, calretinin, neuropeptide Y) features of both deep fetal interstitial neurons and deep (periventricular) interstitial neurons in the postnatal and adult deep cerebral white matter (i.e. corpus callosum, anterior commissure, internal and external capsule and the corona radiata/centrum semiovale). Although these deep interstitial neurons are poorly developed or absent in the brains of rodents, they represent a prominent feature of the significantly enlarged white matter of human and non-human primate brains. © 2010 The Authors. Journal of Anatomy © 2010 Anatomical Society of Great Britain and Ireland.

Light-evoked hyperpolarization and silencing of neurons by conjugated polymers.

PubMed

Feyen, Paul; Colombo, Elisabetta; Endeman, Duco; Nova, Mattia; Laudato, Lucia; Martino, Nicola; Antognazza, Maria Rosa; Lanzani, Guglielmo; Benfenati, Fabio; Ghezzi, Diego

2016-03-04

The ability to control and modulate the action potential firing in neurons represents a powerful tool for neuroscience research and clinical applications. While neuronal excitation has been achieved with many tools, including electrical and optical stimulation, hyperpolarization and neuronal inhibition are typically obtained through patch-clamp or optogenetic manipulations. Here we report the use of conjugated polymer films interfaced with neurons for inducing a light-mediated inhibition of their electrical activity. We show that prolonged illumination of the interface triggers a sustained hyperpolarization of the neuronal membrane that significantly reduces both spontaneous and evoked action potential firing. We demonstrate that the polymeric interface can be activated by either visible or infrared light and is capable of modulating neuronal activity in brain slices and explanted retinas. These findings prove the ability of conjugated polymers to tune neuronal firing and suggest their potential application for the in-vivo modulation of neuronal activity.

Light-evoked hyperpolarization and silencing of neurons by conjugated polymers

PubMed Central

Feyen, Paul; Colombo, Elisabetta; Endeman, Duco; Nova, Mattia; Laudato, Lucia; Martino, Nicola; Antognazza, Maria Rosa; Lanzani, Guglielmo; Benfenati, Fabio; Ghezzi, Diego

2016-01-01

The ability to control and modulate the action potential firing in neurons represents a powerful tool for neuroscience research and clinical applications. While neuronal excitation has been achieved with many tools, including electrical and optical stimulation, hyperpolarization and neuronal inhibition are typically obtained through patch-clamp or optogenetic manipulations. Here we report the use of conjugated polymer films interfaced with neurons for inducing a light-mediated inhibition of their electrical activity. We show that prolonged illumination of the interface triggers a sustained hyperpolarization of the neuronal membrane that significantly reduces both spontaneous and evoked action potential firing. We demonstrate that the polymeric interface can be activated by either visible or infrared light and is capable of modulating neuronal activity in brain slices and explanted retinas. These findings prove the ability of conjugated polymers to tune neuronal firing and suggest their potential application for the in-vivo modulation of neuronal activity. PMID:26940513

On the continuous differentiability of inter-spike intervals of synaptically connected cortical spiking neurons in a neuronal network.

PubMed

Kumar, Gautam; Kothare, Mayuresh V

2013-12-01

We derive conditions for continuous differentiability of inter-spike intervals (ISIs) of spiking neurons with respect to parameters (decision variables) of an external stimulating input current that drives a recurrent network of synaptically connected neurons. The dynamical behavior of individual neurons is represented by a class of discontinuous single-neuron models. We report here that ISIs of neurons in the network are continuously differentiable with respect to decision variables if (1) a continuously differentiable trajectory of the membrane potential exists between consecutive action potentials with respect to time and decision variables and (2) the partial derivative of the membrane potential of spiking neurons with respect to time is not equal to the partial derivative of their firing threshold with respect to time at the time of action potentials. Our theoretical results are supported by showing fulfillment of these conditions for a class of known bidimensional spiking neuron models.

The Use of Neural Networks for Determining Tank Routes

DTIC Science & Technology

1992-09-01

ADDRESS (City, State, and ZIP Code) Monterey, CA 93943-5000 Monterey, CA 93943-5000 &a. NAME OF FUNDINGJSPONSORING 8b. OFFICE SYMBOL 9. PROCUREMENT...Weights Figure 1. Neural Network Architecture 6 The back-error propagation technique iteratively assigns weights to connections, computes the errors...neurons as the start. From that we decided to try 4, 6 , 8, 10, 12, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90 and 100 or until it was obvious that

Role of temporal processing stages by inferior temporal neurons in facial recognition.

PubMed

Sugase-Miyamoto, Yasuko; Matsumoto, Narihisa; Kawano, Kenji

2011-01-01

In this review, we focus on the role of temporal stages of encoded facial information in the visual system, which might enable the efficient determination of species, identity, and expression. Facial recognition is an important function of our brain and is known to be processed in the ventral visual pathway, where visual signals are processed through areas V1, V2, V4, and the inferior temporal (IT) cortex. In the IT cortex, neurons show selective responses to complex visual images such as faces, and at each stage along the pathway the stimulus selectivity of the neural responses becomes sharper, particularly in the later portion of the responses. In the IT cortex of the monkey, facial information is represented by different temporal stages of neural responses, as shown in our previous study: the initial transient response of face-responsive neurons represents information about global categories, i.e., human vs. monkey vs. simple shapes, whilst the later portion of these responses represents information about detailed facial categories, i.e., expression and/or identity. This suggests that the temporal stages of the neuronal firing pattern play an important role in the coding of visual stimuli, including faces. This type of coding may be a plausible mechanism underlying the temporal dynamics of recognition, including the process of detection/categorization followed by the identification of objects. Recent single-unit studies in monkeys have also provided evidence consistent with the important role of the temporal stages of encoded facial information. For example, view-invariant facial identity information is represented in the response at a later period within a region of face-selective neurons. Consistent with these findings, temporally modulated neural activity has also been observed in human studies. These results suggest a close correlation between the temporal processing stages of facial information by IT neurons and the temporal dynamics of face recognition.

Role of Temporal Processing Stages by Inferior Temporal Neurons in Facial Recognition

PubMed Central

Sugase-Miyamoto, Yasuko; Matsumoto, Narihisa; Kawano, Kenji

2011-01-01

In this review, we focus on the role of temporal stages of encoded facial information in the visual system, which might enable the efficient determination of species, identity, and expression. Facial recognition is an important function of our brain and is known to be processed in the ventral visual pathway, where visual signals are processed through areas V1, V2, V4, and the inferior temporal (IT) cortex. In the IT cortex, neurons show selective responses to complex visual images such as faces, and at each stage along the pathway the stimulus selectivity of the neural responses becomes sharper, particularly in the later portion of the responses. In the IT cortex of the monkey, facial information is represented by different temporal stages of neural responses, as shown in our previous study: the initial transient response of face-responsive neurons represents information about global categories, i.e., human vs. monkey vs. simple shapes, whilst the later portion of these responses represents information about detailed facial categories, i.e., expression and/or identity. This suggests that the temporal stages of the neuronal firing pattern play an important role in the coding of visual stimuli, including faces. This type of coding may be a plausible mechanism underlying the temporal dynamics of recognition, including the process of detection/categorization followed by the identification of objects. Recent single-unit studies in monkeys have also provided evidence consistent with the important role of the temporal stages of encoded facial information. For example, view-invariant facial identity information is represented in the response at a later period within a region of face-selective neurons. Consistent with these findings, temporally modulated neural activity has also been observed in human studies. These results suggest a close correlation between the temporal processing stages of facial information by IT neurons and the temporal dynamics of face recognition. PMID:21734904

Effect of acute lateral hemisection of the spinal cord on spinal neurons of postural networks

PubMed Central

Zelenin, P. V.; Lyalka, V. F.; Orlovsky, G. N.; Deliagina, T. G.

2016-01-01

In quadrupeds, acute lateral hemisection of the spinal cord (LHS) severely impairs postural functions, which recover over time. Postural limb reflexes (PLRs) represent a substantial component of postural corrections in intact animals. The aim of the present study was to characterize the effects of acute LHS on two populations of spinal neurons (F and E) mediating PLRs. For this purpose, in decerebrate rabbits, responses of individual neurons from L5 to stimulation causing PLRs were recorded before and during reversible LHS (caused by temporal cold block of signal transmission in lateral spinal pathways at L1), as well as after acute surgical (Sur) LHS at L1. Results obtained after Sur-LHS were compared to control data obtained in our previous study. We found that acute LHS caused disappearance of PLRs on the affected side. It also changed a proportion of different types of neurons on that side. A significant decrease and increase in the proportion of F- and non-modulated neurons, respectively, was found. LHS caused a significant decrease in most parameters of activity in F-neurons located in the ventral horn on the lesioned side and in E-neurons of the dorsal horn on both sides. These changes were caused by a significant decrease in the efficacy of posture-related sensory input from the ipsilateral limb to F-neurons, and from the contralateral limb to both F- and E-neurons. These distortions in operation of postural networks underlie the impairment of postural control after acute LHS, and represent a starting point for the subsequent recovery of postural functions. PMID:27702647

The Role of Noise in Brain Function

NASA Astrophysics Data System (ADS)

Roy, S.; Llinás, R.

2012-12-01

Noise plays a fundamental role in all living organisms from the earliest prokaryotes to advanced mammalian forms, such as ourselves. In the context of living organisms, the term 'noise' usually refers to the variance amongst measurements obtained from repeated identical experimental conditions, or from output signals from these systems. It is noteworthy that both these conditions are universally characterized by the presence of background fluctuations. In non-biological systems, such as electronics or in communications sciences, where the aim is to send error-free messages, noise was generally regarded as a problem. The discovery of Stochastic Resonances (SR) in non-linear dynamics brought a shift of perception where noise, rather than representing a problem, became fundamental to system function, especially so in biology. The question now is: to what extent is biological function dependent on random noise. Indeed, it seems feasible that noise also plays an important role in neuronal communication and oscillatory synchronization. Given this approach, it follows that determining Fisher information content could be relevant in neuronal communication. It also seems possible that the principle of least time, and that of the sum over histories, could be important basic principles in understanding the coherence dynamics responsible for action and perception. Ultimately, external noise cancellation combined with intrinsic noise signal embedding and, the use of the principle of least time may be considered an essential step in the organization of central nervous system (CNS) function.

The emergence of polychronization and feature binding in a spiking neural network model of the primate ventral visual system.

PubMed

Eguchi, Akihiro; Isbister, James B; Ahmad, Nasir; Stringer, Simon

2018-07-01

We present a hierarchical neural network model, in which subpopulations of neurons develop fixed and regularly repeating temporal chains of spikes (polychronization), which respond specifically to randomized Poisson spike trains representing the input training images. The performance is improved by including top-down and lateral synaptic connections, as well as introducing multiple synaptic contacts between each pair of pre- and postsynaptic neurons, with different synaptic contacts having different axonal delays. Spike-timing-dependent plasticity thus allows the model to select the most effective axonal transmission delay between neurons. Furthermore, neurons representing the binding relationship between low-level and high-level visual features emerge through visually guided learning. This begins to provide a way forward to solving the classic feature binding problem in visual neuroscience and leads to a new hypothesis concerning how information about visual features at every spatial scale may be projected upward through successive neuronal layers. We name this hypothetical upward projection of information the "holographic principle." (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Differential representation of Pavlovian-instrumental transfer by prefrontal cortex subregions and striatum.

PubMed

Homayoun, Houman; Moghaddam, Bita

2009-04-01

Environmental cues that once predicted reward can restore extinguished behavior directed toward that reward. This process may be modeled by the Pavlovian-instrumental transfer (PIT) paradigm where a previously learned Pavlovian conditioned stimulus (CS) elicits a representation of the reward associated with that CS, prompts motivation toward the absent reward, and triggers an instrumental action. We recorded in the medial and orbital prefrontal cortex (mPFC and OFC) and dorsal striatum (DS) of freely moving rats during PIT and found that a Pavlovian CS, as compared with neutral or no stimuli, amplified the phasic neuronal responses to instrumental nosepokes ('transfer' event). In mPFC and OFC, but not the DS, representation of the transfer event correlated with the strength of PIT behavior. Neurons in all three regions showed CS-selective amplification of Pavlovian approaches toward the reward delivery site. Whereas striatal neurons represented transfer and approach behavior through mostly segregated neuronal subsets, overlapping subsets represented these events in the mPFC and OFC. These findings suggest that parallel phasic activation of mPFC and OFC neuronal subsets participates in the transfer from Pavlovian incentives to instrumental actions.

GABA neurons are the major cell type of the nucleus reticularis thalami.

PubMed

Houser, C R; Vaughn, J E; Barber, R P; Roberts, E

1980-11-03

Glutamic acid decarboxylase (GAD), the synthesizing enzyme for the neurotransmitter gamma-aminobutyric acid (GABA), has been localized in a large number of neuronal somata within the nucleus reticularis thalami (NR) of rat brain by light microscopic immunocytochemical methods. GAD-positive staining of neuronal somata and proximal dendrites is observed in the NR of normal (untreated) rats, and this staining is substantially enhanced following colchicine injection into the lateral cerebral ventricle. GAD-positive neuronal cell bodies are prominent throughout the dorsoventral and rostrocaudal extents of the NR and, thus, form a band around the entire lateral aspect of the thalamus. In the lateral part of the NR, oval-shaped neurons with elongated GAD-positive dendritic processes are oriented parallel to the narrow axis of the NR and lie perpendicular to the penetrating fascicles of unstained thalamocortical and corticothalamic fibers. Semithin (2 micrometers) sections confirm that GAD-positive reaction product is contain within the cytoplasm of cell bodies and proximal dendrites. In addition, GAD-positive punctate structures, representing axon terminals, are present in the neuropil and, occasionally, are observed in close proximity to positively-stained neuronal somata. This finding suggests that GABA-mediated inhibition of GABA neurons may occur in the NR. The large number of GAD-positive cell bodies within the NR contrasts with a paucity of positively-stained somata in the more internally located thalamic nuclei. Within these nuclei, GAD-positive punctate structures that represent GABAergic synaptic sites are a characteristic feature. Since previous anatomical studies have demonstrated that a large proportion or reticularis neurons project into the thalamus, it is suggested that many of these GAD-positive punctate structures are the axon terminals of reticularis neurons. Through these projections, reticularis neurons may contribute to GABA-mediated inhibition within many of the thalamic nuclei.

Pharmacological activation of autophagy favors the clearing of intracellular aggregates of misfolded prion protein peptide to prevent neuronal death.

PubMed

Thellung, Stefano; Scoti, Beatrice; Corsaro, Alessandro; Villa, Valentina; Nizzari, Mario; Gagliani, Maria Cristina; Porcile, Carola; Russo, Claudio; Pagano, Aldo; Tacchetti, Carlo; Cortese, Katia; Florio, Tullio

2018-02-07

According to the "gain-of-toxicity mechanism", neuronal loss during cerebral proteinopathies is caused by accumulation of aggregation-prone conformers of misfolded cellular proteins, although it is still debated which aggregation state actually corresponds to the neurotoxic entity. Autophagy, originally described as a variant of programmed cell death, is now emerging as a crucial mechanism for cell survival in response to a variety of cell stressors, including nutrient deprivation, damage of cytoplasmic organelles, or accumulation of misfolded proteins. Impairment of autophagic flux in neurons often associates with neurodegeneration during cerebral amyloidosis, suggesting a role in clearing neurons from aggregation-prone misfolded proteins. Thus, autophagy may represent a target for innovative therapies. In this work, we show that alterations of autophagy progression occur in neurons following in vitro exposure to the amyloidogenic and neurotoxic prion protein-derived peptide PrP90-231. We report that the increase of autophagic flux represents a strategy adopted by neurons to survive the intracellular accumulation of misfolded PrP90-231. In particular, PrP90-231 internalization in A1 murine mesencephalic neurons occurs in acidic structures, showing electron microscopy hallmarks of autophagosomes and autophagolysosomes. However, these structures do not undergo resolution and accumulate in cytosol, suggesting that, in the presence of PrP90-231, autophagy is activated but its progression is impaired; the inability to clear PrP90-231 via autophagy induces cytotoxicity, causing impairment of lysosomal integrity and cytosolic diffusion of hydrolytic enzymes. Conversely, the induction of autophagy by pharmacological  blockade of mTOR kinase or trophic factor deprivation restored autophagy resolution, reducing intracellular PrP90-231 accumulation and neuronal death. Taken together, these data indicate that PrP90-231 internalization induces an autophagic defensive response in A1 neurons, although incomplete and insufficient to grant survival; the pharmacological enhancement of this process exerts neuroprotection favoring the clearing of the internalized peptide and could represents a promising neuroprotective tool for neurodegenerative proteinopathies.

Potential role of monkey inferior parietal neurons coding action semantic equivalences as precursors of parts of speech.

PubMed

Yamazaki, Yumiko; Yokochi, Hiroko; Tanaka, Michio; Okanoya, Kazuo; Iriki, Atsushi

2010-01-01

The anterior portion of the inferior parietal cortex possesses comprehensive representations of actions embedded in behavioural contexts. Mirror neurons, which respond to both self-executed and observed actions, exist in this brain region in addition to those originally found in the premotor cortex. We found that parietal mirror neurons responded differentially to identical actions embedded in different contexts. Another type of parietal mirror neuron represents an inverse and complementary property of responding equally to dissimilar actions made by itself and others for an identical purpose. Here, we propose a hypothesis that these sets of inferior parietal neurons constitute a neural basis for encoding the semantic equivalence of various actions across different agents and contexts. The neurons have mirror neuron properties, and they encoded generalization of agents, differentiation of outcomes, and categorization of actions that led to common functions. By integrating the activities of these mirror neurons with various codings, we further suggest that in the ancestral primates' brains, these various representations of meaningful action led to the gradual establishment of equivalence relations among the different types of actions, by sharing common action semantics. Such differential codings of the components of actions might represent precursors to the parts of protolanguage, such as gestural communication, which are shared among various members of a society. Finally, we suggest that the inferior parietal cortex serves as an interface between this action semantics system and other higher semantic systems, through common structures of action representation that mimic language syntax.

Potential role of monkey inferior parietal neurons coding action semantic equivalences as precursors of parts of speech

PubMed Central

Yamazaki, Yumiko; Yokochi, Hiroko; Tanaka, Michio; Okanoya, Kazuo; Iriki, Atsushi

2010-01-01

The anterior portion of the inferior parietal cortex possesses comprehensive representations of actions embedded in behavioural contexts. Mirror neurons, which respond to both self-executed and observed actions, exist in this brain region in addition to those originally found in the premotor cortex. We found that parietal mirror neurons responded differentially to identical actions embedded in different contexts. Another type of parietal mirror neuron represents an inverse and complementary property of responding equally to dissimilar actions made by itself and others for an identical purpose. Here, we propose a hypothesis that these sets of inferior parietal neurons constitute a neural basis for encoding the semantic equivalence of various actions across different agents and contexts. The neurons have mirror neuron properties, and they encoded generalization of agents, differentiation of outcomes, and categorization of actions that led to common functions. By integrating the activities of these mirror neurons with various codings, we further suggest that in the ancestral primates' brains, these various representations of meaningful action led to the gradual establishment of equivalence relations among the different types of actions, by sharing common action semantics. Such differential codings of the components of actions might represent precursors to the parts of protolanguage, such as gestural communication, which are shared among various members of a society. Finally, we suggest that the inferior parietal cortex serves as an interface between this action semantics system and other higher semantic systems, through common structures of action representation that mimic language syntax. PMID:20119879

An online supervised learning method based on gradient descent for spiking neurons.

PubMed

Xu, Yan; Yang, Jing; Zhong, Shuiming

2017-09-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

P2X receptors, sensory neurons and pain.

PubMed

Bele, Tanja; Fabbretti, Elsa

2015-01-01

Pain represents a very large social and clinical problem since the current treatment provides insufficient pain relief. Plasticity of pain receptors together with sensitisation of sensory neurons, and the role of soluble mediators released from non-neuronal cells render difficult to understand the spatial and temporal scale of pain development, neuronal responses and disease progression. In pathological conditions, ATP is one of the most powerful mediators that activates P2X receptors that behave as sensitive ATP-detectors, such as neuronal P2X3 receptor subtypes and P2X4 and P2X7 receptors expressed on non-neuronal cells. Dissecting the molecular mechanisms occurring in sensory neurons and in accessory cells allows to design appropriate tissue- and cell- targeted approaches to treat chronic pain.

Intrinsic Neuronal Properties Switch the Mode of Information Transmission in Networks

PubMed Central

Gjorgjieva, Julijana; Mease, Rebecca A.; Moody, William J.; Fairhall, Adrienne L.

2014-01-01

Diverse ion channels and their dynamics endow single neurons with complex biophysical properties. These properties determine the heterogeneity of cell types that make up the brain, as constituents of neural circuits tuned to perform highly specific computations. How do biophysical properties of single neurons impact network function? We study a set of biophysical properties that emerge in cortical neurons during the first week of development, eventually allowing these neurons to adaptively scale the gain of their response to the amplitude of the fluctuations they encounter. During the same time period, these same neurons participate in large-scale waves of spontaneously generated electrical activity. We investigate the potential role of experimentally observed changes in intrinsic neuronal properties in determining the ability of cortical networks to propagate waves of activity. We show that such changes can strongly affect the ability of multi-layered feedforward networks to represent and transmit information on multiple timescales. With properties modeled on those observed at early stages of development, neurons are relatively insensitive to rapid fluctuations and tend to fire synchronously in response to wave-like events of large amplitude. Following developmental changes in voltage-dependent conductances, these same neurons become efficient encoders of fast input fluctuations over few layers, but lose the ability to transmit slower, population-wide input variations across many layers. Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission. The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity. This work underscores the significance of simple changes in conductance parameters in governing how neurons represent and propagate information, and suggests a role for background synaptic noise in switching the mode of information transmission. PMID:25474701

Innervation of the mammalian esophagus.

PubMed

Neuhuber, Winfried L; Raab, Marion; Berthoud, Hans-Rudolf; Wörl, Jürgen

2006-01-01

Understanding the innervation of the esophagus is a prerequisite for successful treatment of a variety of disorders, e.g., dysphagia, achalasia, gastroesophageal reflux disease (GERD) and non-cardiac chest pain. Although, at first glance, functions of the esophagus are relatively simple, their neuronal control is considerably complex. Vagal motor neurons of the nucleus ambiguus and preganglionic neurons of the dorsal motor nucleus innervate striated and smooth muscle, respectively. Myenteric neurons represent the interface between the dorsal motor nucleus and smooth muscle but they are also involved in striated muscle innervation. Intraganglionic laminar endings (IGLEs) represent mechanosensory vagal afferent terminals. They also establish intricate connections with enteric neurons. Afferent information is implemented by the swallowing central pattern generator in the brainstem, which generates and coordinates deglutitive activity in both striated and smooth esophageal muscle and orchestrates esophageal sphincters as well as gastric adaptive relaxation. Disturbed excitation/inhibition balance in the lower esophageal sphincter results in motility disorders, e.g., achalasia and GERD. Loss of mechanosensory afferents disrupts adaptation of deglutitive motor programs to bolus variables, eventually leading to megaesophagus. Both spinal and vagal afferents appear to contribute to painful sensations, e.g., non-cardiac chest pain. Extrinsic and intrinsic neurons may be involved in intramural reflexes using acetylcholine, nitric oxide, substance P, CGRP and glutamate as main transmitters. In addition, other molecules, e.g., ATP, GABA and probably also inflammatory cytokines, may modulate these neuronal functions.

Localization by interaural time difference (ITD): Effects of interaural frequency mismatch

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

Bonham, B.H.; Lewis, E.R.

1999-07-01

A commonly accepted physiological model for lateralization of low-frequency sounds by interaural time delay (ITD) stipulates that binaural comparison neurons receive input from frequency-matched channels from each ear. Here, the effects of hypothetical interaural frequency mismatches on this model are reported. For this study, the cat{close_quote}s auditory system peripheral to the binaural comparison neurons was represented by a neurophysiologically derived model, and binaural comparison neurons were represented by cross-correlators. The results of the study indicate that, for binaural comparison neurons receiving input from one cochlear channel from each ear, interaural CF mismatches may serve to either augment or diminish themore » effective difference in ipsilateral and contralateral axonal time delays from the periphery to the binaural comparison neuron. The magnitude of this increase or decrease in the effective time delay difference can be up to 400 {mu}s for CF mismatches of 0.2 octaves or less for binaural neurons with CFs between 250 Hz and 2.5 kHz. For binaural comparison neurons with nominal CFs near 500 Hz, the 25-{mu}s effective time delay difference caused by a 0.012-octave CF mismatch is equal to the ITD previously shown to be behaviorally sufficient for the cat to lateralize a low-frequency sound source. {copyright} {ital 1999 Acoustical Society of America.}« less

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.

Debiasing affective forecasting errors with targeted, but not representative, experience narratives.

PubMed

Shaffer, Victoria A; Focella, Elizabeth S; Scherer, Laura D; Zikmund-Fisher, Brian J

2016-10-01

To determine whether representative experience narratives (describing a range of possible experiences) or targeted experience narratives (targeting the direction of forecasting bias) can reduce affective forecasting errors, or errors in predictions of experiences. In Study 1, participants (N=366) were surveyed about their experiences with 10 common medical events. Those who had never experienced the event provided ratings of predicted discomfort and those who had experienced the event provided ratings of actual discomfort. Participants making predictions were randomly assigned to either the representative experience narrative condition or the control condition in which they made predictions without reading narratives. In Study 2, participants (N=196) were again surveyed about their experiences with these 10 medical events, but participants making predictions were randomly assigned to either the targeted experience narrative condition or the control condition. Affective forecasting errors were observed in both studies. These forecasting errors were reduced with the use of targeted experience narratives (Study 2) but not representative experience narratives (Study 1). Targeted, but not representative, narratives improved the accuracy of predicted discomfort. Public collections of patient experiences should favor stories that target affective forecasting biases over stories representing the range of possible experiences. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Memory: Ironing Out a Wrinkle in Time.

PubMed

Miller, Adam M P; Frankland, Paul W; Josselyn, Sheena A

2018-05-21

Individual hippocampal neurons encode time over seconds, whereas large-scale changes in population activity of hippocampal neurons encode time over minutes and days. New research shows how the hippocampus represents these multiple timescales simultaneously. Copyright © 2018 Elsevier Ltd. All rights reserved.

Dynamic encoding of responses and outcomes by neurons in medial prefrontal cortex

PubMed Central

Luk, Chung-Hay; Wallis, Jonathan D.

2009-01-01

Medial prefrontal cortex (MPFC) and lateral prefrontal cortex (LPFC) both contribute to goal-directed behavior, but their precise role remains unclear. Several lines of evidence suggest that MPFC is more important than LPFC for outcome-guided response selection. To examine this, we trained two subjects to perform a task that required them to monitor the specific outcome associated with a specific response on a trial-by-trial basis. While the subjects performed this task, we recorded the electrical activity of single neurons simultaneously from MPFC and LPFC. There were marked differences in the neuronal properties of these two areas. Neurons encoding the response were present in both areas, but in MPFC, there were also neurons that encoded the outcome. In particular, neurons encoded the subject’s intended response and how preferable the received outcome was. Thus, only in MPFC was all the information necessary to solve the task encoded. In addition, largely separate populations of MPFC neurons encoded the response and the outcome. Neurons encoding the outcome were in the anterior parts of MPFC: posterior to the corpus callosum there was a marked drop in their incidence. Our results suggest differences in the contribution of MPFC and LPFC to action control. MPFC neurons encode the desirability of the outcome produced by a specific response on a trial-by-trial basis. This capability may contribute to several of the functions of MPFC, such as action valuation, error detection and decision-making. PMID:19515921

Dopamine neurons modulate pheromone responses in Drosophila courtship learning.

PubMed

Keleman, Krystyna; Vrontou, Eleftheria; Krüttner, Sebastian; Yu, Jai Y; Kurtovic-Kozaric, Amina; Dickson, Barry J

2012-09-06

Learning through trial-and-error interactions allows animals to adapt innate behavioural ‘rules of thumb’ to the local environment, improving their prospects for survival and reproduction. Naive Drosophila melanogaster males, for example, court both virgin and mated females, but learn through experience to selectively suppress futile courtship towards females that have already mated. Here we show that courtship learning reflects an enhanced response to the male pheromone cis-vaccenyl acetate (cVA), which is deposited on females during mating and thus distinguishes mated females from virgins. Dissociation experiments suggest a simple learning rule in which unsuccessful courtship enhances sensitivity to cVA. The learning experience can be mimicked by artificial activation of dopaminergic neurons, and we identify a specific class of dopaminergic neuron that is critical for courtship learning. These neurons provide input to the mushroom body (MB) γ lobe, and the DopR1 dopamine receptor is required in MBγ neurons for both natural and artificial courtship learning. Our work thus reveals critical behavioural, cellular and molecular components of the learning rule by which Drosophila adjusts its innate mating strategy according to experience.

Reducing Neuronal Networks to Discrete Dynamics

PubMed Central

Terman, David; Ahn, Sungwoo; Wang, Xueying; Just, Winfried

2008-01-01

We consider a general class of purely inhibitory and excitatory-inhibitory neuronal networks, with a general class of network architectures, and characterize the complex firing patterns that emerge. Our strategy for studying these networks is to first reduce them to a discrete model. In the discrete model, each neuron is represented as a finite number of states and there are rules for how a neuron transitions from one state to another. In this paper, we rigorously demonstrate that the continuous neuronal model can be reduced to the discrete model if the intrinsic and synaptic properties of the cells are chosen appropriately. In a companion paper [1], we analyze the discrete model. PMID:18443649

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

PubMed Central

Potjans, Wiebke; Diesmann, Markus; Morrison, Abigail

2011-01-01

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards. PMID:21589888

A three-dimensional image processing program for accurate, rapid, and semi-automated segmentation of neuronal somata with dense neurite outgrowth

PubMed Central

Ross, James D.; Cullen, D. Kacy; Harris, James P.; LaPlaca, Michelle C.; DeWeerth, Stephen P.

2015-01-01

Three-dimensional (3-D) image analysis techniques provide a powerful means to rapidly and accurately assess complex morphological and functional interactions between neural cells. Current software-based identification methods of neural cells generally fall into two applications: (1) segmentation of cell nuclei in high-density constructs or (2) tracing of cell neurites in single cell investigations. We have developed novel methodologies to permit the systematic identification of populations of neuronal somata possessing rich morphological detail and dense neurite arborization throughout thick tissue or 3-D in vitro constructs. The image analysis incorporates several novel automated features for the discrimination of neurites and somata by initially classifying features in 2-D and merging these classifications into 3-D objects; the 3-D reconstructions automatically identify and adjust for over and under segmentation errors. Additionally, the platform provides for software-assisted error corrections to further minimize error. These features attain very accurate cell boundary identifications to handle a wide range of morphological complexities. We validated these tools using confocal z-stacks from thick 3-D neural constructs where neuronal somata had varying degrees of neurite arborization and complexity, achieving an accuracy of ≥95%. We demonstrated the robustness of these algorithms in a more complex arena through the automated segmentation of neural cells in ex vivo brain slices. These novel methods surpass previous techniques by improving the robustness and accuracy by: (1) the ability to process neurites and somata, (2) bidirectional segmentation correction, and (3) validation via software-assisted user input. This 3-D image analysis platform provides valuable tools for the unbiased analysis of neural tissue or tissue surrogates within a 3-D context, appropriate for the study of multi-dimensional cell-cell and cell-extracellular matrix interactions. PMID:26257609

Drawing on student knowledge of neuroanatomy and neurophysiology.

PubMed

Slominski, Tara N; Momsen, Jennifer L; Montplaisir, Lisa M

2017-06-01

Drawings are an underutilized assessment format in Human Anatomy and Physiology (HA&P), despite their potential to reveal student content understanding and alternative conceptions. This study used student-generated drawings to explore student knowledge in a HA&P course. The drawing tasks in this study focused on chemical synapses between neurons, an abstract concept in HA&P. Using two preinstruction drawing tasks, students were asked to depict synaptic transmission and summation. In response to the first drawing task, 20% of students ( n = 352) created accurate representations of neuron anatomy. The remaining students created drawings suggesting an inaccurate or incomplete understanding of synaptic transmission. Of the 208 inaccurate student-generated drawings, 21% depicted the neurons as touching. When asked to illustrate summation, only 10 students (roughly 4%) were able to produce an accurate drawing. Overall, students were more successful at drawing anatomy (synapse) than physiology (summation) before formal instruction. The common errors observed in student-generated drawings indicate students do not enter the classroom as blank slates. The error of "touching" neurons in a chemical synapse suggests that students may be using intuitive or experiential knowledge when reasoning about physiological concepts. These results 1 ) support the utility of drawing tasks as a tool to reveal student content knowledge about neuroanatomy and neurophysiology; and 2 ) suggest students enter the classroom with better knowledge of anatomy than physiology. Collectively, the findings from this study inform both practitioners and researchers about the prevalence and nature of student difficulties in HA&P, while also demonstrating the utility of drawing in revealing student knowledge. Copyright © 2017 the American Physiological Society.

The Cellular Basis of a Corollary Discharge

NASA Astrophysics Data System (ADS)

Poulet, James F. A.; Hedwig, Berthold

2006-01-01

How do animals discriminate self-generated from external stimuli during behavior and prevent desensitization of their sensory pathways? A fundamental concept in neuroscience states that neural signals, termed corollary discharges or efference copies, are forwarded from motor to sensory areas. Neurons mediating these signals have proved difficult to identify. We show that a single, multisegmental interneuron is responsible for the pre- and postsynaptic inhibition of auditory neurons in singing crickets (Gryllus bimaculatus). Therefore, this neuron represents a corollary discharge interneuron that provides a neuronal basis for the central control of sensory responses.

Topographic organizations of taste-responsive neurons in the parabrachial nucleus of C57BL/6J mice: An electrophysiological mapping study.

PubMed

Tokita, K; Boughter, J D

2016-03-01

The activities of 178 taste-responsive neurons were recorded extracellularly from the parabrachial nucleus (PbN) in the anesthetized C57BL/6J mouse. Taste stimuli included those representative of five basic taste qualities, sweet, salty, sour, bitter and umami. Umami synergism was represented by all sucrose-best and sweet-sensitive sodium chloride-best neurons. Mediolaterally the PbN was divided into medial, brachium conjunctivum (BC) and lateral subdivisions while rostrocaudally the PbN was divided into rostral and caudal subdivisions for mapping and reconstruction of recording sites. Neurons in the medial and BC subdivisions had a significantly greater magnitude of response to sucrose and to the mixture of monopotassium glutamate and inosine monophosphate than those found in the lateral subdivision. In contrast, neurons in the lateral subdivision possessed a more robust response to quinine hydrochloride. Rostrocaudally no difference was found in the mean magnitude of response. Analysis on the distribution pattern of neuron types classified by their best stimulus revealed that the proportion of neuron types in the medial vs. lateral and BC vs. lateral subdivisions was significantly different, with a greater amount of sucrose-best neurons found medially and within the BC, and a greater amount of sodium chloride-, citric acid- and quinine hydrochloride-best neurons found laterally. There was no significant difference in the neuron-type distribution between rostral and caudal PbN. We also assessed breadth of tuning in these neurons by calculating entropy (H) and noise-to-signal (N/S) ratio. The mean N/S ratio of all neurons (0.43) was significantly lower than that of H value (0.64). Neurons in the caudal PbN had a significantly higher H value than in the rostral PbN. In contrast, mean N/S ratios were not different both mediolaterally and rostrocaudally. These results suggest that although there is overlap in taste quality representation in the mouse PbN, taste-responsive neurons still possessed a topographic organization. Published by Elsevier Ltd.

Neurons in the pigeon caudolateral nidopallium differentiate Pavlovian conditioned stimuli but not their associated reward value in a sign-tracking paradigm

PubMed Central

Kasties, Nils; Starosta, Sarah; Güntürkün, Onur; Stüttgen, Maik C.

2016-01-01

Animals exploit visual information to identify objects, form stimulus-reward associations, and prepare appropriate behavioral responses. The nidopallium caudolaterale (NCL), an associative region of the avian endbrain, contains neurons exhibiting prominent response modulation during presentation of reward-predicting visual stimuli, but it is unclear whether neural activity represents valuation signals, stimulus properties, or sensorimotor contingencies. To test the hypothesis that NCL neurons represent stimulus value, we subjected pigeons to a Pavlovian sign-tracking paradigm in which visual cues predicted rewards differing in magnitude (large vs. small) and delay to presentation (short vs. long). Subjects’ strength of conditioned responding to visual cues reliably differentiated between predicted reward types and thus indexed valuation. The majority of NCL neurons discriminated between visual cues, with discriminability peaking shortly after stimulus onset and being maintained at lower levels throughout the stimulus presentation period. However, while some cells’ firing rates correlated with reward value, such neurons were not more frequent than expected by chance. Instead, neurons formed discernible clusters which differed in their preferred visual cue. We propose that this activity pattern constitutes a prerequisite for using visual information in more complex situations e.g. requiring value-based choices. PMID:27762287

Effect of Anatomically Realistic Full-Head Model on Activation of Cortical Neurons in Subdural Cortical Stimulation—A Computational Study

NASA Astrophysics Data System (ADS)

Seo, Hyeon; Kim, Donghyeon; Jun, Sung Chan

2016-06-01

Electrical brain stimulation (EBS) is an emerging therapy for the treatment of neurological disorders, and computational modeling studies of EBS have been used to determine the optimal parameters for highly cost-effective electrotherapy. Recent notable growth in computing capability has enabled researchers to consider an anatomically realistic head model that represents the full head and complex geometry of the brain rather than the previous simplified partial head model (extruded slab) that represents only the precentral gyrus. In this work, subdural cortical stimulation (SuCS) was found to offer a better understanding of the differential activation of cortical neurons in the anatomically realistic full-head model than in the simplified partial-head models. We observed that layer 3 pyramidal neurons had comparable stimulation thresholds in both head models, while layer 5 pyramidal neurons showed a notable discrepancy between the models; in particular, layer 5 pyramidal neurons demonstrated asymmetry in the thresholds and action potential initiation sites in the anatomically realistic full-head model. Overall, the anatomically realistic full-head model may offer a better understanding of layer 5 pyramidal neuronal responses. Accordingly, the effects of using the realistic full-head model in SuCS are compelling in computational modeling studies, even though this modeling requires substantially more effort.

The Emergence of Single Neurons in Clinical Neurology

PubMed Central

Cash, Sydney S.; Hochberg, Leigh R.

2015-01-01

Summary Single neuron actions and interactions are the sine qua non of brain function, and nearly all diseases and injuries of the central nervous system trace their clinical sequelae to neuronal dysfunction or failure. Remarkably, discussion of neuronal activity is largely absent in clinical neuroscience. Advances in neurotechnology and computational capabilities, accompanied by shifts in theoretical frameworks, have led to renewed interest in the information represented by single neurons. Using direct interfaces with the nervous system, millisecond-scale information will soon be extracted from single neurons in clinical environments, supporting personalized treatment of neurologic and psychiatric disease. In this review we focus on single neuronal activity in restoring communication and motor control in patients suffering from devastating neurological injuries. We also explore the single neuron's role in epilepsy and movement disorders, surgical anesthesia, and in cognitive processes disrupted in neurodegenerative and neuropsychiatric disease. Finally, we speculate on how technological advances will revolutionize neurotherapeutics. PMID:25856488

The emergence of single neurons in clinical neurology.

PubMed

Cash, Sydney S; Hochberg, Leigh R

2015-04-08

Single neuron actions and interactions are the sine qua non of brain function, and nearly all diseases and injuries of the CNS trace their clinical sequelae to neuronal dysfunction or failure. Remarkably, discussion of neuronal activity is largely absent in clinical neuroscience. Advances in neurotechnology and computational capabilities, accompanied by shifts in theoretical frameworks, have led to renewed interest in the information represented by single neurons. Using direct interfaces with the nervous system, millisecond-scale information will soon be extracted from single neurons in clinical environments, supporting personalized treatment of neurologic and psychiatric disease. In this Perspective, we focus on single-neuronal activity in restoring communication and motor control in patients suffering from devastating neurological injuries. We also explore the single neuron's role in epilepsy and movement disorders, surgical anesthesia, and in cognitive processes disrupted in neurodegenerative and neuropsychiatric disease. Finally, we speculate on how technological advances will revolutionize neurotherapeutics. Copyright © 2015 Elsevier Inc. All rights reserved.

The Isolation of Pure Populations of Neurons by Laser Capture Microdissection: Methods and Application in Neuroscience.

PubMed

Morris, Renée; Mehta, Prachi

2018-01-01

In mammals, the central nervous system (CNS) is constituted of various cellular elements, posing a challenge to isolating specific cell types to investigate their expression profile. As a result, tissue homogenization is not amenable to analyses of motor neurons profiling as these represent less than 10% of the total spinal cord cell population. One way to tackle the problem of tissue heterogeneity and obtain meaningful genomic, proteomic, and transcriptomic profiling is to use laser capture microdissection technology (LCM). In this chapter, we describe protocols for the capture of isolated populations of motor neurons from spinal cord tissue sections and for downstream transcriptomic analysis of motor neurons with RT-PCR. We have also included a protocol for the immunological confirmation that the captured neurons are indeed motor neurons. Although focused on spinal cord motor neurons, these protocols can be easily optimized for the isolation of any CNS neurons.

Dorsal Raphe Dopamine Neurons Represent the Experience of Social Isolation

PubMed Central

Matthews, Gillian A.; Nieh, Edward H.; Vander Weele, Caitlin M.; Halbert, Sarah A.; Pradhan, Roma V.; Yosafat, Ariella S.; Glober, Gordon F.; Izadmehr, Ehsan M.; Thomas, Rain E.; Lacy, Gabrielle D.; Wildes, Craig P.; Ungless, Mark A.; Tye, Kay M.

2016-01-01

Summary The motivation to seek social contact may arise from either positive or negative emotional states, as social interaction can be rewarding and social isolation can be aversive. While ventral tegmental area (VTA) dopamine (DA) neurons may mediate social reward, a cellular substrate for the negative affective state of loneliness has remained elusive. Here, we identify a functional role for DA neurons in the dorsal raphe nucleus (DRN), in which we observe synaptic changes following acute social isolation. DRN DA neurons show increased activity upon social contact following isolation, revealed by in vivo calcium imaging. Optogenetic activation of DRN DA neurons increases social preference but causes place avoidance. Furthermore, these neurons are necessary for promoting rebound sociability following an acute period of isolation. Finally, the degree to which these neurons modulate behavior is predicted by social rank, together supporting a role for DRN dopamine neurons in mediating a loneliness-like state. PaperClip PMID:26871628

The ontogenetic origins of mirror neurons: evidence from 'tool-use' and 'audiovisual' mirror neurons.

PubMed

Cook, Richard

2012-10-23

Since their discovery, mirror neurons--units in the macaque brain that discharge both during action observation and execution--have attracted considerable interest. Whether mirror neurons are an innate endowment or acquire their sensorimotor matching properties ontogenetically has been the subject of intense debate. It is widely believed that these units are an innate trait; that we are born with a set of mature mirror neurons because their matching properties conveyed upon our ancestors an evolutionary advantage. However, an alternative view is that mirror neurons acquire their matching properties during ontogeny, through correlated experience of observing and performing actions. The present article re-examines frequently overlooked neurophysiological reports of 'tool-use' and 'audiovisual' mirror neurons within the context of this debate. It is argued that these findings represent compelling evidence that mirror neurons are a product of sensorimotor experience, and not an innate endowment.

Essential roles of mitochondrial depolarization in neuron loss through microglial activation and attraction toward neurons.

PubMed

Nam, Min-Kyung; Shin, Hyun-Ah; Han, Ji-Hye; Park, Dae-Wook; Rhim, Hyangshuk

2013-04-10

As life spans increased, neurodegenerative disorders that affect aging populations have also increased. Progressive neuronal loss in specific brain regions is the most common cause of neurodegenerative disease; however, key determinants mediating neuron loss are not fully understood. Using a model of mitochondrial membrane potential (ΔΨm) loss, we found only 25% cell loss in SH-SY5Y (SH) neuronal mono-cultures, but interestingly, 85% neuronal loss occurred when neurons were co-cultured with BV2 microglia. SH neurons overexpressing uncoupling protein 2 exhibited an increase in neuron-microglia interactions, which represent an early step in microglial phagocytosis of neurons. This result indicates that ΔΨm loss in SH neurons is an important contributor to recruitment of BV2 microglia. Notably, we show that ΔΨm loss in BV2 microglia plays a crucial role in microglial activation and phagocytosis of damaged SH neurons. Thus, our study demonstrates that ΔΨm loss in both neurons and microglia is a critical determinant of neuron loss. These findings also offer new insights into neuroimmunological and bioenergetical aspects of neurodegenerative disease. Copyright © 2013 Elsevier B.V. All rights reserved.

Cell-Specific Cholinergic Modulation of Excitability of Layer 5B Principal Neurons in Mouse Auditory Cortex

PubMed Central

Joshi, Ankur; Kalappa, Bopanna I.; Anderson, Charles T.

2016-01-01

The neuromodulator acetylcholine (ACh) is crucial for several cognitive functions, such as perception, attention, and learning and memory. Whereas, in most cases, the cellular circuits or the specific neurons via which ACh exerts its cognitive effects remain unknown, it is known that auditory cortex (AC) neurons projecting from layer 5B (L5B) to the inferior colliculus, corticocollicular neurons, are required for cholinergic-mediated relearning of sound localization after occlusion of one ear. Therefore, elucidation of the effects of ACh on the excitability of corticocollicular neurons will bridge the cell-specific and cognitive properties of ACh. Because AC L5B contains another class of neurons that project to the contralateral cortex, corticocallosal neurons, to identify the cell-specific mechanisms that enable corticocollicular neurons to participate in sound localization relearning, we investigated the effects of ACh release on both L5B corticocallosal and corticocollicular neurons. Using in vitro electrophysiology and optogenetics in mouse brain slices, we found that ACh generated nicotinic ACh receptor (nAChR)-mediated depolarizing potentials and muscarinic ACh receptor (mAChR)-mediated hyperpolarizing potentials in AC L5B corticocallosal neurons. In corticocollicular neurons, ACh release also generated nAChR-mediated depolarizing potentials. However, in contrast to the mAChR-mediated hyperpolarizing potentials in corticocallosal neurons, ACh generated prolonged mAChR-mediated depolarizing potentials in corticocollicular neurons. These prolonged depolarizing potentials generated persistent firing in corticocollicular neurons, whereas corticocallosal neurons lacking mAChR-mediated depolarizing potentials did not show persistent firing. We propose that ACh-mediated persistent firing in corticocollicular neurons may represent a critical mechanism required for learning-induced plasticity in AC. SIGNIFICANCE STATEMENT Acetylcholine (ACh) is crucial for cognitive functions. Whereas in most cases the cellular circuits or the specific neurons via which ACh exerts its cognitive effects remain unknown, it is known that auditory cortex (AC) corticocollicular neurons projecting from layer 5B to the inferior colliculus are required for cholinergic-mediated relearning of sound localization after occlusion of one ear. Therefore, elucidation of the effects of ACh on the excitability of corticocollicular neurons will bridge the cell-specific and cognitive properties of ACh. Our results suggest that cell-specific ACh-mediated persistent firing in corticocollicular neurons may represent a critical mechanism required for learning-induced plasticity in AC. Moreover, our results provide synaptic mechanisms via which ACh may mediate its effects on AC receptive fields. PMID:27511019

Quantitative neuroanatomy for connectomics in Drosophila

PubMed Central

Schneider-Mizell, Casey M; Gerhard, Stephan; Longair, Mark; Kazimiers, Tom; Li, Feng; Zwart, Maarten F; Champion, Andrew; Midgley, Frank M; Fetter, Richard D; Saalfeld, Stephan; Cardona, Albert

2016-01-01

Neuronal circuit mapping using electron microscopy demands laborious proofreading or reconciliation of multiple independent reconstructions. Here, we describe new methods to apply quantitative arbor and network context to iteratively proofread and reconstruct circuits and create anatomically enriched wiring diagrams. We measured the morphological underpinnings of connectivity in new and existing reconstructions of Drosophila sensorimotor (larva) and visual (adult) systems. Synaptic inputs were preferentially located on numerous small, microtubule-free 'twigs' which branch off a single microtubule-containing 'backbone'. Omission of individual twigs accounted for 96% of errors. However, the synapses of highly connected neurons were distributed across multiple twigs. Thus, the robustness of a strong connection to detailed twig anatomy was associated with robustness to reconstruction error. By comparing iterative reconstruction to the consensus of multiple reconstructions, we show that our method overcomes the need for redundant effort through the discovery and application of relationships between cellular neuroanatomy and synaptic connectivity. DOI: http://dx.doi.org/10.7554/eLife.12059.001 PMID:26990779

Contamination of current-clamp measurement of neuron capacitance by voltage-dependent phenomena

PubMed Central

White, William E.

2013-01-01

Measuring neuron capacitance is important for morphological description, conductance characterization, and neuron modeling. One method to estimate capacitance is to inject current pulses into a neuron and fit the resulting changes in membrane potential with multiple exponentials; if the neuron is purely passive, the amplitude and time constant of the slowest exponential give neuron capacitance (Major G, Evans JD, Jack JJ. Biophys J 65: 423–449, 1993). Golowasch et al. (Golowasch J, Thomas G, Taylor AL, Patel A, Pineda A, Khalil C, Nadim F. J Neurophysiol 102: 2161–2175, 2009) have shown that this is the best method for measuring the capacitance of nonisopotential (i.e., most) neurons. However, prior work has not tested for, or examined how much error would be introduced by, slow voltage-dependent phenomena possibly present at the membrane potentials typically used in such work. We investigated this issue in lobster (Panulirus interruptus) stomatogastric neurons by performing current clamp-based capacitance measurements at multiple membrane potentials. A slow, voltage-dependent phenomenon consistent with residual voltage-dependent conductances was present at all tested membrane potentials (−95 to −35 mV). This phenomenon was the slowest component of the neuron's voltage response, and failure to recognize and exclude it would lead to capacitance overestimates of several hundredfold. Most methods of estimating capacitance depend on the absence of voltage-dependent phenomena. Our demonstration that such phenomena make nonnegligible contributions to neuron responses even at well-hyperpolarized membrane potentials highlights the critical importance of checking for such phenomena in all work measuring neuron capacitance. We show here how to identify such phenomena and minimize their contaminating influence. PMID:23576698

Effect of acute lateral hemisection of the spinal cord on spinal neurons of postural networks.

PubMed

Zelenin, P V; Lyalka, V F; Orlovsky, G N; Deliagina, T G

2016-12-17

In quadrupeds, acute lateral hemisection of the spinal cord (LHS) severely impairs postural functions, which recover over time. Postural limb reflexes (PLRs) represent a substantial component of postural corrections in intact animals. The aim of the present study was to characterize the effects of acute LHS on two populations of spinal neurons (F and E) mediating PLRs. For this purpose, in decerebrate rabbits, responses of individual neurons from L5 to stimulation causing PLRs were recorded before and during reversible LHS (caused by temporal cold block of signal transmission in lateral spinal pathways at L1), as well as after acute surgical LHS at L1. Results obtained after Sur-LHS were compared to control data obtained in our previous study. We found that acute LHS caused disappearance of PLRs on the affected side. It also changed a proportion of different types of neurons on that side. A significant decrease and increase in the proportion of F- and non-modulated neurons, respectively, was found. LHS caused a significant decrease in most parameters of activity in F-neurons located in the ventral horn on the lesioned side and in E-neurons of the dorsal horn on both sides. These changes were caused by a significant decrease in the efficacy of posture-related sensory input from the ipsilateral limb to F-neurons, and from the contralateral limb to both F- and E-neurons. These distortions in operation of postural networks underlie the impairment of postural control after acute LHS, and represent a starting point for the subsequent recovery of postural functions. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Neurons selective to the number of visual items in the corvid songbird endbrain

PubMed Central

Ditz, Helen M.; Nieder, Andreas

2015-01-01

It is unknown whether anatomical specializations in the endbrains of different vertebrates determine the neuronal code to represent numerical quantity. Therefore, we recorded single-neuron activity from the endbrain of crows trained to judge the number of items in displays. Many neurons were tuned for numerosities irrespective of the physical appearance of the items, and their activity correlated with performance outcome. Comparison of both behavioral and neuronal representations of numerosity revealed that the data are best described by a logarithmically compressed scaling of numerical information, as postulated by the Weber–Fechner law. The behavioral and neuronal numerosity representations in the crow reflect surprisingly well those found in the primate association cortex. This finding suggests that distantly related vertebrates with independently developed endbrains adopted similar neuronal solutions to process quantity. PMID:26056278

Association of grey matter changes with stability and flexibility of prediction in akinetic-rigid Parkinson's disease.

PubMed

Trempler, Ima; Binder, Ellen; El-Sourani, Nadiya; Schiffler, Patrick; Tenberge, Jan-Gerd; Schiffer, Anne-Marike; Fink, Gereon R; Schubotz, Ricarda I

2018-06-01

Parkinson's disease (PD), which is caused by degeneration of dopaminergic neurons in the midbrain, results in a heterogeneous clinical picture including cognitive decline. Since the phasic signal of dopamine neurons is proposed to guide learning by signifying mismatches between subjects' expectations and external events, we here investigated whether akinetic-rigid PD patients without mild cognitive impairment exhibit difficulties in dealing with either relevant (requiring flexibility) or irrelevant (requiring stability) prediction errors. Following our previous study on flexibility and stability in prediction (Trempler et al. J Cogn Neurosci 29(2):298-309, 2017), we then assessed whether deficits would correspond with specific structural alterations in dopaminergic regions as well as in inferior frontal cortex, medial prefrontal cortex, and the hippocampus. Twenty-one healthy controls and twenty-one akinetic-rigid PD patients on and off medication performed a task which required to serially predict upcoming items. Switches between predictable sequences had to be indicated via button press, whereas sequence omissions had to be ignored. Independent of the disease, midbrain volume was related to a general response bias to unexpected events, whereas right putamen volume correlated with the ability to discriminate between relevant and irrelevant prediction errors. However, patients compared with healthy participants showed deficits in stabilisation against irrelevant prediction errors, associated with thickness of right inferior frontal gyrus and left medial prefrontal cortex. Flexible updating due to relevant prediction errors was also affected in patients compared with controls and associated with right hippocampus volume. Dopaminergic medication influenced behavioural performance across, but not within the patients. Our exploratory study warrants further research on deficient prediction error processing and its structural correlates as a core of cognitive symptoms occurring already in early stages of the disease.

A neuron-astrocyte transistor-like model for neuromorphic dressed neurons.

PubMed

Valenza, G; Pioggia, G; Armato, A; Ferro, M; Scilingo, E P; De Rossi, D

2011-09-01

Experimental evidences on the role of the synaptic glia as an active partner together with the bold synapse in neuronal signaling and dynamics of neural tissue strongly suggest to investigate on a more realistic neuron-glia model for better understanding human brain processing. Among the glial cells, the astrocytes play a crucial role in the tripartite synapsis, i.e. the dressed neuron. A well-known two-way astrocyte-neuron interaction can be found in the literature, completely revising the purely supportive role for the glia. The aim of this study is to provide a computationally efficient model for neuron-glia interaction. The neuron-glia interactions were simulated by implementing the Li-Rinzel model for an astrocyte and the Izhikevich model for a neuron. Assuming the dressed neuron dynamics similar to the nonlinear input-output characteristics of a bipolar junction transistor, we derived our computationally efficient model. This model may represent the fundamental computational unit for the development of real-time artificial neuron-glia networks opening new perspectives in pattern recognition systems and in brain neurophysiology. Copyright © 2011 Elsevier Ltd. All rights reserved.

Nitric Oxide Exerts Basal and Insulin-Dependent Anorexigenic Actions in POMC Hypothalamic Neurons

PubMed Central

Wellhauser, Leigh; Chalmers, Jennifer A.

2016-01-01

The arcuate nucleus of the hypothalamus represents a key center for the control of appetite and feeding through the regulation of 2 key neuronal populations, notably agouti-related peptide/neuropeptide Y and proopimelanocortin (POMC)/cocaine- and amphetamine-regulated transcript neurons. Altered regulation of these neuronal networks, in particular the dysfunction of POMC neurons upon high-fat consumption, is a major pathogenic mechanism involved in the development of obesity and type 2 diabetes mellitus. Efforts are underway to preserve the integrity or enhance the functionality of POMC neurons in order to prevent or treat these metabolic diseases. Here, we report for the first time that the nitric oxide (NO−) donor, sodium nitroprusside (SNP) mediates anorexigenic actions in both hypothalamic tissue and hypothalamic-derived cell models by mediating the up-regulation of POMC levels. SNP increased POMC mRNA in a dose-dependent manner and enhanced α-melanocortin-secreting hormone production and secretion in mHypoA-POMC/GFP-2 cells. SNP also enhanced insulin-driven POMC expression likely by inhibiting the deacetylase activity of sirtuin 1. Furthermore, SNP enhanced insulin-dependent POMC expression, likely by reducing the transcriptional repression of Foxo1 on the POMC gene. Prolonged SNP exposure prevented the development of insulin resistance. Taken together, the NO− donor SNP enhances the anorexigenic potential of POMC neurons by promoting its transcriptional expression independent and in cooperation with insulin. Thus, increasing cellular NO− levels represents a hormone-independent method of promoting anorexigenic output from the existing POMC neuronal populations and may be advantageous in the fight against these prevalent disorders. PMID:26930171

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.

PubMed

Rueckauer, Bodo; Lungu, Iulia-Alexandra; Hu, Yuhuang; Pfeiffer, Michael; Liu, Shih-Chii

2017-01-01

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications.

Implementation of neural network for color properties of polycarbonates

NASA Astrophysics Data System (ADS)

Saeed, U.; Ahmad, S.; Alsadi, J.; Ross, D.; Rizvi, G.

2014-05-01

In present paper, the applicability of artificial neural networks (ANN) is investigated for color properties of plastics. The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. An optimal design is completed for 10, 12, 14,16,18 & 20 hidden neurons on single hidden layer with five different algorithms: batch gradient descent (GD), batch variable learning rate (GDX), resilient back-propagation (RP), scaled conjugate gradient (SCG), levenberg-marquardt (LM) in the feed forward back-propagation neural network model. The training data for ANN is obtained from experimental measurements. There were twenty two inputs including resins, additives & pigments while three tristimulus color values L*, a* and b* were used as output layer. Statistical analysis in terms of Root-Mean-Squared (RMS), absolute fraction of variance (R squared), as well as mean square error is used to investigate the performance of ANN. LM algorithm with fourteen neurons on hidden layer in Feed Forward Back-Propagation of ANN model has shown best result in the present study. The degree of accuracy of the ANN model in reduction of errors is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a feasible method in error reduction in specific color tristimulus values.

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification

PubMed Central

Rueckauer, Bodo; Lungu, Iulia-Alexandra; Hu, Yuhuang; Pfeiffer, Michael; Liu, Shih-Chii

2017-01-01

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications. PMID:29375284

Neural Representation of Motion-In-Depth in Area MT

PubMed Central

Sanada, Takahisa M.

2014-01-01

Neural processing of 2D visual motion has been studied extensively, but relatively little is known about how visual cortical neurons represent visual motion trajectories that include a component toward or away from the observer (motion in depth). Psychophysical studies have demonstrated that humans perceive motion in depth based on both changes in binocular disparity over time (CD cue) and interocular velocity differences (IOVD cue). However, evidence for neurons that represent motion in depth has been limited, especially in primates, and it is unknown whether such neurons make use of CD or IOVD cues. We show that approximately one-half of neurons in macaque area MT are selective for the direction of motion in depth, and that this selectivity is driven primarily by IOVD cues, with a small contribution from the CD cue. Our results establish that area MT, a central hub of the primate visual motion processing system, contains a 3D representation of visual motion. PMID:25411481

Computing by physical interaction in neurons.

PubMed

Aur, Dorian; Jog, Mandar; Poznanski, Roman R

2011-12-01

The electrodynamics of action potentials represents the fundamental level where information is integrated and processed in neurons. The Hodgkin-Huxley model cannot explain the non-stereotyped spatial charge density dynamics that occur during action potential propagation. Revealed in experiments as spike directivity, the non-uniform charge density dynamics within neurons carry meaningful information and suggest that fragments of information regarding our memories are endogenously stored in structural patterns at a molecular level and are revealed only during spiking activity. The main conceptual idea is that under the influence of electric fields, efficient computation by interaction occurs between charge densities embedded within molecular structures and the transient developed flow of electrical charges. This process of computation underlying electrical interactions and molecular mechanisms at the subcellular level is dissimilar from spiking neuron models that are completely devoid of physical interactions. Computation by interaction describes a more powerful continuous model of computation than the one that consists of discrete steps as represented in Turing machines.

Joint representation of translational and rotational components of optic flow in parietal cortex

PubMed Central

Sunkara, Adhira; DeAngelis, Gregory C.; Angelaki, Dora E.

2016-01-01

Terrestrial navigation naturally involves translations within the horizontal plane and eye rotations about a vertical (yaw) axis to track and fixate targets of interest. Neurons in the macaque ventral intraparietal (VIP) area are known to represent heading (the direction of self-translation) from optic flow in a manner that is tolerant to rotational visual cues generated during pursuit eye movements. Previous studies have also reported that eye rotations modulate the response gain of heading tuning curves in VIP neurons. We tested the hypothesis that VIP neurons simultaneously represent both heading and horizontal (yaw) eye rotation velocity by measuring heading tuning curves for a range of rotational velocities of either real or simulated eye movements. Three findings support the hypothesis of a joint representation. First, we show that rotation velocity selectivity based on gain modulations of visual heading tuning is similar to that measured during pure rotations. Second, gain modulations of heading tuning are similar for self-generated eye rotations and visually simulated rotations, indicating that the representation of rotation velocity in VIP is multimodal, driven by both visual and extraretinal signals. Third, we show that roughly one-half of VIP neurons jointly represent heading and rotation velocity in a multiplicatively separable manner. These results provide the first evidence, to our knowledge, for a joint representation of translation direction and rotation velocity in parietal cortex and show that rotation velocity can be represented based on visual cues, even in the absence of efference copy signals. PMID:27095846

Neuronal activity in primate dorsal anterior cingulate cortex signals task conflict and predicts adjustments in pupil-linked arousal

PubMed Central

Ebitz, R. Becket; Platt, Michael L.

2014-01-01

Summary Whether driving a car, shopping for food, or paying attention in a classroom of boisterous teenagers, it’s often hard to maintain focus on goals in the face of distraction. Brain imaging studies in humans implicate the dorsal anterior cingulate cortex (dACC) in regulating the conflict between goals and distractors. Here we show for the first time that single dACC neurons signal conflict between task goals and distractors in the rhesus macaque, particularly for biologically-relevant social stimuli. For some neurons, task conflict signals predicted subsequent changes in pupil size—a peripheral index of arousal linked to noradrenergic tone—associated with reduced distractor interference. dACC neurons also responded to errors and these signals predicted adjustments in pupil size. These findings provide the first neurophysiological endorsement of the hypothesis that dACC regulates conflict, in part, via modulation of pupil-linked processes such as arousal. PMID:25654259

Design and Application of a Circuit for Measuring Frequency and Duty Cycle of Stimulated Bioelectrical Signal

NASA Astrophysics Data System (ADS)

Tang, Li-Ming; Chang, Ben-Kang; Liu, Tie-Bing; Wu, Min; Ling, Gang

2002-12-01

To design a new type of circuit for measuring frequency & duty cycle of stimulated bioelectrical signal for the project of 'the map of neuron-threshold in human brain and its clinical application'. This circuit was designed according to the character of stimulated bioelectrical signals. It was tested and improved and then used in the neuron -threshold stimulator. The circuit was found to be very accurate for measuring frequency and the error for measuring duty cycle was below 0.2%. This circuit is well-designed, simple, easy to use, and can be applied in many systems.

Comparative mapping of serotonin-immunoreactive neurons in the central nervous systems of nudibranch molluscs.

PubMed

Newcomb, James M; Fickbohm, David J; Katz, Paul S

2006-11-20

The serotonergic systems in nudibranch molluscs were compared by mapping the locations of serotonin-immunoreactive (5-HT-ir) neurons in 11 species representing all four suborders of the nudibranch clade: Dendronotoidea (Tritonia diomedea, Tochuina tetraquetra, Dendronotus iris, Dendronotus frondosus, and Melibe leonina), Aeolidoidea (Hermissenda crassicornis and Flabellina trophina), Arminoidea (Dirona albolineata, Janolus fuscus, and Armina californica), and Doridoidea (Triopha catalinae). A nomenclature is proposed to standardize reports of cell location in species with differing brain morphologies. Certain patterns of 5-HT immunoreactivity were found to be consistent for all species, such as the presence of 5-HT-ir neurons in the pedal and cerebral ganglia. Also, particular clusters of 5-HT-ir neurons in the anterior and posterior regions of the dorsal surface of the cerebral ganglion were always present. However, there were interspecies differences in the number of 5-HT-ir neurons in each cluster, and some clusters even exhibited strong intraspecies variability that was only weakly correlated with brain size. Phylogenetic analysis suggests that the presence of particular classes of 5-HT-ir neurons exhibits a great deal of homoplasy. The conserved features of the nudibranch serotonergic system presumably represent the shared ancestral structure, whereas the derived characters suggest substantial independent evolutionary changes in the number and presence of serotonergic neurons. Although a number of studies have demonstrated phylogenetic variability of peptidergic systems, this study suggests that serotonergic systems may also exhibit a high degree of homoplasy in some groups of organisms.

Neural representation of cost-benefit selections in rat anterior cingulate cortex in self-paced decision making.

PubMed

Wang, Shuai; Shi, Yi; Li, Bao-Ming

2017-03-01

The anterior cingulate cortex (ACC) is crucial for decision making which involves the processing of cost-benefit information. Our previous study has shown that ACC is essential for self-paced decision making. However, it is unclear how ACC neurons represent cost-benefit selections during the decision-making process. In the present study, we trained rats on the same "Do More Get More" (DMGM) task as in our previous work. In each trial, the animals stand upright and perform a sustained nosepoke of their own will to earn a water reward, with the amount of reward positively correlated to the duration of the nosepoke (i.e., longer nosepokes earn larger rewards). We then recorded ACC neuronal activity on well-trained rats while they were performing the DMGM task. Our results show that (1) approximately 3/5 ACC neurons (296/496, 59.7%) exhibited changes in firing frequency that were temporally locked with the main events of the DMGM task; (2) about 1/5 ACC neurons (101/496, 20.4%) or 1/3 of the event-modulated neurons (101/296, 34.1%) showed differential firing rate changes for different cost-benefit selections; and (3) many ACC neurons exhibited linear encoding of the cost-benefit selections in the DMGM task events. These results suggest that ACC neurons are engaged in encoding cost-benefit information, thus represent the selections in self-paced decision making. Copyright © 2016 Elsevier Inc. All rights reserved.

Electronic neural network for dynamic resource allocation

NASA Technical Reports Server (NTRS)

Thakoor, A. P.; Eberhardt, S. P.; Daud, T.

1991-01-01

A VLSI implementable neural network architecture for dynamic assignment is presented. The resource allocation problems involve assigning members of one set (e.g. resources) to those of another (e.g. consumers) such that the global 'cost' of the associations is minimized. The network consists of a matrix of sigmoidal processing elements (neurons), where the rows of the matrix represent resources and columns represent consumers. Unlike previous neural implementations, however, association costs are applied directly to the neurons, reducing connectivity of the network to VLSI-compatible 0 (number of neurons). Each row (and column) has an additional neuron associated with it to independently oversee activations of all the neurons in each row (and each column), providing a programmable 'k-winner-take-all' function. This function simultaneously enforces blocking (excitatory/inhibitory) constraints during convergence to control the number of active elements in each row and column within desired boundary conditions. Simulations show that the network, when implemented in fully parallel VLSI hardware, offers optimal (or near-optimal) solutions within only a fraction of a millisecond, for problems up to 128 resources and 128 consumers, orders of magnitude faster than conventional computing or heuristic search methods.

Spiking neural network model for memorizing sequences with forward and backward recall.

PubMed

Borisyuk, Roman; Chik, David; Kazanovich, Yakov; da Silva Gomes, João

2013-06-01

We present an oscillatory network of conductance based spiking neurons of Hodgkin-Huxley type as a model of memory storage and retrieval of sequences of events (or objects). The model is inspired by psychological and neurobiological evidence on sequential memories. The building block of the model is an oscillatory module which contains excitatory and inhibitory neurons with all-to-all connections. The connection architecture comprises two layers. A lower layer represents consecutive events during their storage and recall. This layer is composed of oscillatory modules. Plastic excitatory connections between the modules are implemented using an STDP type learning rule for sequential storage. Excitatory neurons in the upper layer project star-like modifiable connections toward the excitatory lower layer neurons. These neurons in the upper layer are used to tag sequences of events represented in the lower layer. Computer simulations demonstrate good performance of the model including difficult cases when different sequences contain overlapping events. We show that the model with STDP type or anti-STDP type learning rules can be applied for the simulation of forward and backward replay of neural spikes respectively. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Segmentation of neuronal structures using SARSA (λ)-based boundary amendment with reinforced gradient-descent curve shape fitting.

PubMed

Zhu, Fei; Liu, Quan; Fu, Yuchen; Shen, Bairong

2014-01-01

The segmentation of structures in electron microscopy (EM) images is very important for neurobiological research. The low resolution neuronal EM images contain noise and generally few features are available for segmentation, therefore application of the conventional approaches to identify the neuron structure from EM images is not successful. We therefore present a multi-scale fused structure boundary detection algorithm in this study. In the algorithm, we generate an EM image Gaussian pyramid first, then at each level of the pyramid, we utilize Laplacian of Gaussian function (LoG) to attain structure boundary, we finally assemble the detected boundaries by using fusion algorithm to attain a combined neuron structure image. Since the obtained neuron structures usually have gaps, we put forward a reinforcement learning-based boundary amendment method to connect the gaps in the detected boundaries. We use a SARSA (λ)-based curve traveling and amendment approach derived from reinforcement learning to repair the incomplete curves. Using this algorithm, a moving point starts from one end of the incomplete curve and walks through the image where the decisions are supervised by the approximated curve model, with the aim of minimizing the connection cost until the gap is closed. Our approach provided stable and efficient structure segmentation. The test results using 30 EM images from ISBI 2012 indicated that both of our approaches, i.e., with or without boundary amendment, performed better than six conventional boundary detection approaches. In particular, after amendment, the Rand error and warping error, which are the most important performance measurements during structure segmentation, were reduced to very low values. The comparison with the benchmark method of ISBI 2012 and the recent developed methods also indicates that our method performs better for the accurate identification of substructures in EM images and therefore useful for the identification of imaging features related to brain diseases.

Segmentation of Neuronal Structures Using SARSA (λ)-Based Boundary Amendment with Reinforced Gradient-Descent Curve Shape Fitting

PubMed Central

Zhu, Fei; Liu, Quan; Fu, Yuchen; Shen, Bairong

2014-01-01

The segmentation of structures in electron microscopy (EM) images is very important for neurobiological research. The low resolution neuronal EM images contain noise and generally few features are available for segmentation, therefore application of the conventional approaches to identify the neuron structure from EM images is not successful. We therefore present a multi-scale fused structure boundary detection algorithm in this study. In the algorithm, we generate an EM image Gaussian pyramid first, then at each level of the pyramid, we utilize Laplacian of Gaussian function (LoG) to attain structure boundary, we finally assemble the detected boundaries by using fusion algorithm to attain a combined neuron structure image. Since the obtained neuron structures usually have gaps, we put forward a reinforcement learning-based boundary amendment method to connect the gaps in the detected boundaries. We use a SARSA (λ)-based curve traveling and amendment approach derived from reinforcement learning to repair the incomplete curves. Using this algorithm, a moving point starts from one end of the incomplete curve and walks through the image where the decisions are supervised by the approximated curve model, with the aim of minimizing the connection cost until the gap is closed. Our approach provided stable and efficient structure segmentation. The test results using 30 EM images from ISBI 2012 indicated that both of our approaches, i.e., with or without boundary amendment, performed better than six conventional boundary detection approaches. In particular, after amendment, the Rand error and warping error, which are the most important performance measurements during structure segmentation, were reduced to very low values. The comparison with the benchmark method of ISBI 2012 and the recent developed methods also indicates that our method performs better for the accurate identification of substructures in EM images and therefore useful for the identification of imaging features related to brain diseases. PMID:24625699

Inferring Single Neuron Properties in Conductance Based Balanced Networks

PubMed Central

Pool, Román Rossi; Mato, Germán

2011-01-01

Balanced states in large networks are a usual hypothesis for explaining the variability of neural activity in cortical systems. In this regime the statistics of the inputs is characterized by static and dynamic fluctuations. The dynamic fluctuations have a Gaussian distribution. Such statistics allows to use reverse correlation methods, by recording synaptic inputs and the spike trains of ongoing spontaneous activity without any additional input. By using this method, properties of the single neuron dynamics that are masked by the balanced state can be quantified. To show the feasibility of this approach we apply it to large networks of conductance based neurons. The networks are classified as Type I or Type II according to the bifurcations which neurons of the different populations undergo near the firing onset. We also analyze mixed networks, in which each population has a mixture of different neuronal types. We determine under which conditions the intrinsic noise generated by the network can be used to apply reverse correlation methods. We find that under realistic conditions we can ascertain with low error the types of neurons present in the network. We also find that data from neurons with similar firing rates can be combined to perform covariance analysis. We compare the results of these methods (that do not requite any external input) to the standard procedure (that requires the injection of Gaussian noise into a single neuron). We find a good agreement between the two procedures. PMID:22016730

Time-scale invariance as an emergent property in a perceptron with realistic, noisy neurons

PubMed Central

Buhusi, Catalin V.; Oprisan, Sorinel A.

2013-01-01

In most species, interval timing is time-scale invariant: errors in time estimation scale up linearly with the estimated duration. In mammals, time-scale invariance is ubiquitous over behavioral, lesion, and pharmacological manipulations. For example, dopaminergic drugs induce an immediate, whereas cholinergic drugs induce a gradual, scalar change in timing. Behavioral theories posit that time-scale invariance derives from particular computations, rules, or coding schemes. In contrast, we discuss a simple neural circuit, the perceptron, whose output neurons fire in a clockwise fashion (interval timing) based on the pattern of coincidental activation of its input neurons. We show numerically that time-scale invariance emerges spontaneously in a perceptron with realistic neurons, in the presence of noise. Under the assumption that dopaminergic drugs modulate the firing of input neurons, and that cholinergic drugs modulate the memory representation of the criterion time, we show that a perceptron with realistic neurons reproduces the pharmacological clock and memory patterns, and their time-scale invariance, in the presence of noise. These results suggest that rather than being a signature of higher-order cognitive processes or specific computations related to timing, time-scale invariance may spontaneously emerge in a massively-connected brain from the intrinsic noise of neurons and circuits, thus providing the simplest explanation for the ubiquity of scale invariance of interval timing. PMID:23518297

Evidence that the tri-cellular metabolism of N-acetylaspartate functions as the brain's "operating system": how NAA metabolism supports meaningful intercellular frequency-encoded communications.

PubMed

Baslow, Morris H

2010-11-01

N-acetylaspartate (NAA), an acetylated derivative of L-aspartate (Asp), and N-acetylaspartylglutamate (NAAG), a derivative of NAA and L-glutamate (Glu), are synthesized by neurons in brain. However, neurons cannot catabolize either of these substances, and so their metabolism requires the participation of two other cell types. Neurons release both NAA and NAAG to extra-cellular fluid (ECF) upon stimulation, where astrocytes, the target cells for NAAG, hydrolyze it releasing NAA back into ECF, and oligodendrocytes, the target cells for NAA, hydrolyze it releasing Asp to ECF for recycling to neurons. This sequence is unique as it is the only known amino acid metabolic cycle in brain that requires three cell types for its completion. The results of this cycling are two-fold. First, neuronal metabolic water is transported to ECF for its removal from brain. Second, the rate of neuronal activity is coupled with focal hyperemia, providing stimulated neurons with the energy required for transmission of meaningful frequency-encoded messages. In this paper, it is proposed that the tri-cellular metabolism of NAA functions as the "operating system" of the brain, and is essential for normal cognitive and motor activities. Evidence in support of this hypothesis is provided by the outcomes of two human inborn errors in NAA metabolism.

77 FR 38632 - Findings of Research Misconduct

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-28

... counts of nigrostriatal neurons in brains of several mice and rats by copying a single data file from a... Used Herbicide, Atrazine: Altered Function and Loss of Neurons in Brain Monamine Systems.'' Environ... 2004 and 2006; Falsifying a bar graph representing brain proteasomal activity, by selectively altering...

Effects of hypoglycaemia on neuronal metabolism in the adult brain: role of alternative substrates to glucose.

PubMed

Amaral, Ana I

2013-07-01

Hypoglycaemia is characterized by decreased blood glucose levels and is associated with different pathologies (e.g. diabetes, inborn errors of metabolism). Depending on its severity, it might affect cognitive functions, including impaired judgment and decreased memory capacity, which have been linked to alterations of brain energy metabolism. Glucose is the major cerebral energy substrate in the adult brain and supports the complex metabolic interactions between neurons and astrocytes, which are essential for synaptic activity. Therefore, hypoglycaemia disturbs cerebral metabolism and, consequently, neuronal function. Despite the high vulnerability of neurons to hypoglycaemia, important neurochemical changes enabling these cells to prolong their resistance to hypoglycaemia have been described. This review aims at providing an overview over the main metabolic effects of hypoglycaemia on neurons, covering in vitro and in vivo findings. Recent studies provided evidence that non-glucose substrates including pyruvate, glycogen, ketone bodies, glutamate, glutamine, and aspartate, are metabolized by neurons in the absence of glucose and contribute to prolong neuronal function and delay ATP depletion during hypoglycaemia. One of the pathways likely implicated in the process is the pyruvate recycling pathway, which allows for the full oxidation of glutamate and glutamine. The operation of this pathway in neurons, particularly after hypoglycaemia, has been re-confirmed recently using metabolic modelling tools (i.e. Metabolic Flux Analysis), which allow for a detailed investigation of cellular metabolism in cultured cells. Overall, the knowledge summarized herein might be used for the development of potential therapies targeting neuronal protection in patients vulnerable to hypoglycaemic episodes.

Evidence for a distributed hierarchy of action representation in the brain

PubMed Central

Grafton, Scott T.; de C. Hamilton, Antonia F.

2007-01-01

Complex human behavior is organized around temporally distal outcomes. Behavioral studies based on tasks such as normal prehension, multi-step object use and imitation establish the existence of relative hierarchies of motor control. The retrieval errors in apraxia also support the notion of a hierarchical model for representing action in the brain. In this review, three functional brain imaging studies of action observation using the method of repetition suppression are used to identify a putative neural architecture that supports action understanding at the level of kinematics, object centered goals and ultimately, motor outcomes. These results, based on observation, may match a similar functional anatomic hierarchy for action planning and execution. If this is true, then the findings support a functional anatomic model that is distributed across a set of interconnected brain areas that are differentially recruited for different aspects of goal oriented behavior, rather than a homogeneous mirror neuron system for organizing and understanding all behavior. PMID:17706312

Local probing and stimulation of neuronal cells by optical manipulation

NASA Astrophysics Data System (ADS)

Cojoc, Dan

2014-09-01

During development and in the adult brain, neurons continuously explore the environment searching for guidance cues, leading to the appropriate connections. Elucidating these mechanisms represents a gold goal in neurobiology. Here, I discuss our recent achievements developing new approaches to locally probe the growth cones and stimulate neuronal cell compartments with high spatial and temporal resolution. Optical tweezers force spectroscopy applied in conjunction with metabolic inhibitors reveals new properties of the cytoskeleton dynamics. On the other hand, using optically manipulated microvectors as functionalized beads or filled liposomes, we demonstrate focal stimulation of neurons by small number of signaling molecules.

Feasibility of predicting tumor motion using online data acquired during treatment and a generalized neural network optimized with offline patient tumor trajectories.

PubMed

Teo, Troy P; Ahmed, Syed Bilal; Kawalec, Philip; Alayoubi, Nadia; Bruce, Neil; Lyn, Ethan; Pistorius, Stephen

2018-02-01

The accurate prediction of intrafraction lung tumor motion is required to compensate for system latency in image-guided adaptive radiotherapy systems. The goal of this study was to identify an optimal prediction model that has a short learning period so that prediction and adaptation can commence soon after treatment begins, and requires minimal reoptimization for individual patients. Specifically, the feasibility of predicting tumor position using a combination of a generalized (i.e., averaged) neural network, optimized using historical patient data (i.e., tumor trajectories) obtained offline, coupled with the use of real-time online tumor positions (obtained during treatment delivery) was examined. A 3-layer perceptron neural network was implemented to predict tumor motion for a prediction horizon of 650 ms. A backpropagation algorithm and batch gradient descent approach were used to train the model. Twenty-seven 1-min lung tumor motion samples (selected from a CyberKnife patient dataset) were sampled at a rate of 7.5 Hz (0.133 s) to emulate the frame rate of an electronic portal imaging device (EPID). A sliding temporal window was used to sample the data for learning. The sliding window length was set to be equivalent to the first breathing cycle detected from each trajectory. Performing a parametric sweep, an averaged error surface of mean square errors (MSE) was obtained from the prediction responses of seven trajectories used for the training of the model (Group 1). An optimal input data size and number of hidden neurons were selected to represent the generalized model. To evaluate the prediction performance of the generalized model on unseen data, twenty tumor traces (Group 2) that were not involved in the training of the model were used for the leave-one-out cross-validation purposes. An input data size of 35 samples (4.6 s) and 20 hidden neurons were selected for the generalized neural network. An average sliding window length of 28 data samples was used. The average initial learning period prior to the availability of the first predicted tumor position was 8.53 ± 1.03 s. Average mean absolute error (MAE) of 0.59 ± 0.13 mm and 0.56 ± 0.18 mm were obtained from Groups 1 and 2, respectively, giving an overall MAE of 0.57 ± 0.17 mm. Average root-mean-square-error (RMSE) of 0.67 ± 0.36 for all the traces (0.76 ± 0.34 mm, Group 1 and 0.63 ± 0.36 mm, Group 2), is comparable to previously published results. Prediction errors are mainly due to the irregular periodicities between cycles. Since the errors from Groups 1 and 2 are within the same range, it demonstrates that this model can generalize and predict on unseen data. This is a first attempt to use an averaged MSE error surface (obtained from the prediction of different patients' tumor trajectories) to determine the parameters of a generalized neural network. This network could be deployed as a plug-and-play predictor for tumor trajectory during treatment delivery, eliminating the need for optimizing individual networks with pretreatment patient data. © 2017 American Association of Physicists in Medicine.

Topographical distribution and morphology of NADPH-diaphorase-stained neurons in the human claustrum

PubMed Central

Hinova-Palova, Dimka V.; Edelstein, Lawrence; Landzhov, Boycho; Minkov, Minko; Malinova, Lina; Hristov, Stanislav; Denaro, Frank J.; Alexandrov, Alexandar; Kiriakova, Teodora; Brainova, Ilina; Paloff, Adrian; Ovtscharoff, Wladimir

2014-01-01

We studied the topographical distribution and morphological characteristics of NADPH-diaphorase-positive neurons and fibers in the human claustrum. These neurons were seen to be heterogeneously distributed throughout the claustrum. Taking into account the size and shape of stained perikarya as well as dendritic and axonal characteristics, Nicotinamide adenine dinucleotide phosphate-diaphorase (NADPHd)-positive neurons were categorized by diameter into three types: large, medium and small. Large neurons ranged from 25 to 35 μm in diameter and typically displayed elliptical or multipolar cell bodies. Medium neurons ranged from 20 to 25 μm in diameter and displayed multipolar, bipolar and irregular cell bodies. Small neurons ranged from 14 to 20 μm in diameter and most often displayed oval or elliptical cell bodies. Based on dendritic characteristics, these neurons were divided into spiny and aspiny subtypes. Our findings reveal two populations of NADPHd-positive neurons in the human claustrum—one comprised of large and medium cells consistent with a projection neuron phenotype, the other represented by small cells resembling the interneuron phenotype as defined by previous Golgi impregnation studies. PMID:24904317

Invisible Brain: Knowledge in Research Works and Neuron Activity.

PubMed

Segev, Aviv; Curtis, Dorothy; Jung, Sukhwan; Chae, Suhyun

2016-01-01

If the market has an invisible hand, does knowledge creation and representation have an "invisible brain"? While knowledge is viewed as a product of neuron activity in the brain, can we identify knowledge that is outside the brain but reflects the activity of neurons in the brain? This work suggests that the patterns of neuron activity in the brain can be seen in the representation of knowledge-related activity. Here we show that the neuron activity mechanism seems to represent much of the knowledge learned in the past decades based on published articles, in what can be viewed as an "invisible brain" or collective hidden neural networks. Similar results appear when analyzing knowledge activity in patents. Our work also tries to characterize knowledge increase as neuron network activity growth. The results propose that knowledge-related activity can be seen outside of the neuron activity mechanism. Consequently, knowledge might exist as an independent mechanism.

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.

Invisible Brain: Knowledge in Research Works and Neuron Activity

PubMed Central

Segev, Aviv; Curtis, Dorothy; Jung, Sukhwan; Chae, Suhyun

2016-01-01

If the market has an invisible hand, does knowledge creation and representation have an “invisible brain”? While knowledge is viewed as a product of neuron activity in the brain, can we identify knowledge that is outside the brain but reflects the activity of neurons in the brain? This work suggests that the patterns of neuron activity in the brain can be seen in the representation of knowledge-related activity. Here we show that the neuron activity mechanism seems to represent much of the knowledge learned in the past decades based on published articles, in what can be viewed as an “invisible brain” or collective hidden neural networks. Similar results appear when analyzing knowledge activity in patents. Our work also tries to characterize knowledge increase as neuron network activity growth. The results propose that knowledge-related activity can be seen outside of the neuron activity mechanism. Consequently, knowledge might exist as an independent mechanism. PMID:27439199

Soft chitosan microbeads scaffold for 3D functional neuronal networks.

PubMed

Tedesco, Maria Teresa; Di Lisa, Donatella; Massobrio, Paolo; Colistra, Nicolò; Pesce, Mattia; Catelani, Tiziano; Dellacasa, Elena; Raiteri, Roberto; Martinoia, Sergio; Pastorino, Laura

2018-02-01

The availability of 3D biomimetic in vitro neuronal networks of mammalian neurons represents a pivotal step for the development of brain-on-a-chip experimental models to study neuronal (dys)functions and particularly neuronal connectivity. The use of hydrogel-based scaffolds for 3D cell cultures has been extensively studied in the last years. However, limited work on biomimetic 3D neuronal cultures has been carried out to date. In this respect, here we investigated the use of a widely popular polysaccharide, chitosan (CHI), for the fabrication of a microbead based 3D scaffold to be coupled to primary neuronal cells. CHI microbeads were characterized by optical and atomic force microscopies. The cell/scaffold interaction was deeply characterized by transmission electron microscopy and by immunocytochemistry using confocal microscopy. Finally, a preliminary electrophysiological characterization by micro-electrode arrays was carried out. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Neurogenic Radial Glia-like Cells in Meninges Migrate and Differentiate into Functionally Integrated Neurons in the Neonatal Cortex.

PubMed

Bifari, Francesco; Decimo, Ilaria; Pino, Annachiara; Llorens-Bobadilla, Enric; Zhao, Sheng; Lange, Christian; Panuccio, Gabriella; Boeckx, Bram; Thienpont, Bernard; Vinckier, Stefan; Wyns, Sabine; Bouché, Ann; Lambrechts, Diether; Giugliano, Michele; Dewerchin, Mieke; Martin-Villalba, Ana; Carmeliet, Peter

2017-03-02

Whether new neurons are added in the postnatal cerebral cortex is still debated. Here, we report that the meninges of perinatal mice contain a population of neurogenic progenitors formed during embryonic development that migrate to the caudal cortex and differentiate into Satb2 + neurons in cortical layers II-IV. The resulting neurons are electrically functional and integrated into local microcircuits. Single-cell RNA sequencing identified meningeal cells with distinct transcriptome signatures characteristic of (1) neurogenic radial glia-like cells (resembling neural stem cells in the SVZ), (2) neuronal cells, and (3) a cell type with an intermediate phenotype, possibly representing radial glia-like meningeal cells differentiating to neuronal cells. Thus, we have identified a pool of embryonically derived radial glia-like cells present in the meninges that migrate and differentiate into functional neurons in the neonatal cerebral cortex. Copyright © 2016 Elsevier Inc. All rights reserved.

Role for VGLUT2 in selective vulnerability of midbrain dopamine neurons

PubMed Central

Steinkellner, Thomas; Farino, Zachary J.; Sonders, Mark S.; Villeneuve, Michael; Freyberg, Robin J.; Przedborski, Serge; Lu, Wei; Hnasko, Thomas S.

2018-01-01

Parkinson’s disease is characterized by the loss of dopamine (DA) neurons in the substantia nigra pars compacta (SNc). DA neurons in the ventral tegmental area are more resistant to this degeneration than those in the SNc, though the mechanisms for selective resistance or vulnerability remain poorly understood. A key to elucidating these processes may lie within the subset of DA neurons that corelease glutamate and express the vesicular glutamate transporter VGLUT2. Here, we addressed the potential relationship between VGLUT expression and DA neuronal vulnerability by overexpressing VGLUT in DA neurons of flies and mice. In Drosophila, VGLUT overexpression led to loss of select DA neuron populations. Similarly, expression of VGLUT2 specifically in murine SNc DA neurons led to neuronal loss and Parkinsonian behaviors. Other neuronal cell types showed no such sensitivity, suggesting that DA neurons are distinctively vulnerable to VGLUT2 expression. Additionally, most DA neurons expressed VGLUT2 during development, and coexpression of VGLUT2 with DA markers increased following injury in the adult. Finally, conditional deletion of VGLUT2 made DA neurons more susceptible to Parkinsonian neurotoxins. These data suggest that the balance of VGLUT2 expression is a crucial determinant of DA neuron survival. Ultimately, manipulation of this VGLUT2-dependent process may represent an avenue for therapeutic development. PMID:29337309

Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses.

PubMed

Sudhakar, Shyam Kumar; Torben-Nielsen, Benjamin; De Schutter, Erik

2015-12-01

Neurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it.

Neural Network and Letter Recognition.

NASA Astrophysics Data System (ADS)

Lee, Hue Yeon

Neural net architectures and learning algorithms that recognize hand written 36 alphanumeric characters are studied. The thin line input patterns written in 32 x 32 binary array are used. The system is comprised of two major components, viz. a preprocessing unit and a Recognition unit. The preprocessing unit in turn consists of three layers of neurons; the U-layer, the V-layer, and the C -layer. The functions of the U-layer is to extract local features by template matching. The correlation between the detected local features are considered. Through correlating neurons in a plane with their neighboring neurons, the V-layer would thicken the on-cells or lines that are groups of on-cells of the previous layer. These two correlations would yield some deformation tolerance and some of the rotational tolerance of the system. The C-layer then compresses data through the 'Gabor' transform. Pattern dependent choice of center and wavelengths of 'Gabor' filters is the cause of shift and scale tolerance of the system. Three different learning schemes had been investigated in the recognition unit, namely; the error back propagation learning with hidden units, a simple perceptron learning, and a competitive learning. Their performances were analyzed and compared. Since sometimes the network fails to distinguish between two letters that are inherently similar, additional ambiguity resolving neural nets are introduced on top of the above main neural net. The two dimensional Fourier transform is used as the preprocessing and the perceptron is used as the recognition unit of the ambiguity resolver. One hundred different person's handwriting sets are collected. Some of these are used as the training sets and the remainders are used as the test sets. The correct recognition rate of the system increases with the number of training sets and eventually saturates at a certain value. Similar recognition rates are obtained for the above three different learning algorithms. The minimum error rate, 4.9% is achieved for alphanumeric sets when 50 sets are trained. With the ambiguity resolver, it is reduced to 2.5%. In case that only numeral sets are trained and tested, 2.0% error rate is achieved. When only alphabet sets are considered, the error rate is reduced to 1.1%.

A stereotaxic method of recording from single neurons in the intact in vivo eye of the cat.

PubMed

Molenaar, J; Van de Grind, W A

1980-04-01

A method is described for recording stereotaxically from single retinal neurons in the optically intact in vivo eye of the cat. The method is implemented with the help of a new type of stereotaxic instrument and a specially developed stereotaxic atlas of the cat's eye and retina. The instrument is extremely stable and facilitates intracellular recording from retinal neurons. The microelectrode can be rotated about two mutually perpendicular axes, which intersect in the freely positionable pivot point of the electrode manipulation system. When the pivot point is made to coincide with a small electrode-entrance hole in the sclera of the eye, a large retinal region can be reached through this fixed hole in the immobilized eye. The stereotaxic method makes it possible to choose a target point on the presented eye atlas and predict the settings of the instrument necessary to reach this target. This method also includes the prediction of the corresponding light stimulus position on a tangent screen and the calculation of the projection of the recording electrode on this screen. The sources of error in the method were studied experimentally and a numerical perturbation analysis was carried out to study the influence of each of the sources of error on the final result. The overall accuracy of the method is of the order of 5 degrees of visual angle, which will be sufficient for most purposes.

Dynamic state estimation based on Poisson spike trains—towards a theory of optimal encoding

NASA Astrophysics Data System (ADS)

Susemihl, Alex; Meir, Ron; Opper, Manfred

2013-03-01

Neurons in the nervous system convey information to higher brain regions by the generation of spike trains. An important question in the field of computational neuroscience is how these sensory neurons encode environmental information in a way which may be simply analyzed by subsequent systems. Many aspects of the form and function of the nervous system have been understood using the concepts of optimal population coding. Most studies, however, have neglected the aspect of temporal coding. Here we address this shortcoming through a filtering theory of inhomogeneous Poisson processes. We derive exact relations for the minimal mean squared error of the optimal Bayesian filter and, by optimizing the encoder, obtain optimal codes for populations of neurons. We also show that a class of non-Markovian, smooth stimuli are amenable to the same treatment, and provide results for the filtering and prediction error which hold for a general class of stochastic processes. This sets a sound mathematical framework for a population coding theory that takes temporal aspects into account. It also formalizes a number of studies which discussed temporal aspects of coding using time-window paradigms, by stating them in terms of correlation times and firing rates. We propose that this kind of analysis allows for a systematic study of temporal coding and will bring further insights into the nature of the neural code.

Planning Training Loads for the 400 M Hurdles in Three-Month Mesocycles using Artificial Neural Networks.

PubMed

Przednowek, Krzysztof; Iskra, Janusz; Wiktorowicz, Krzysztof; Krzeszowski, Tomasz; Maszczyk, Adam

2017-12-01

This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes' training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

Planning Training Loads for the 400 M Hurdles in Three-Month Mesocycles using Artificial Neural Networks

PubMed Central

Iskra, Janusz; Wiktorowicz, Krzysztof; Krzeszowski, Tomasz; Maszczyk, Adam

2017-01-01

Abstract This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period. PMID:29339998

Defects formation and spiral waves in a network of neurons in presence of electromagnetic induction.

PubMed

Rostami, Zahra; Jafari, Sajad

2018-04-01

Complex anatomical and physiological structure of an excitable tissue (e.g., cardiac tissue) in the body can represent different electrical activities through normal or abnormal behavior. Abnormalities of the excitable tissue coming from different biological reasons can lead to formation of some defects. Such defects can cause some successive waves that may end up to some additional reorganizing beating behaviors like spiral waves or target waves. In this study, formation of defects and the resulting emitted waves in an excitable tissue are investigated. We have considered a square array network of neurons with nearest-neighbor connections to describe the excitable tissue. Fundamentally, electrophysiological properties of ion currents in the body are responsible for exhibition of electrical spatiotemporal patterns. More precisely, fluctuation of accumulated ions inside and outside of cell causes variable electrical and magnetic field. Considering undeniable mutual effects of electrical field and magnetic field, we have proposed the new Hindmarsh-Rose (HR) neuronal model for the local dynamics of each individual neuron in the network. In this new neuronal model, the influence of magnetic flow on membrane potential is defined. This improved model holds more bifurcation parameters. Moreover, the dynamical behavior of the tissue is investigated in different states of quiescent, spiking, bursting and even chaotic state. The resulting spatiotemporal patterns are represented and the time series of some sampled neurons are displayed, as well.

Energy expenditure: a critical determinant of energy balance with key hypothalamic controls.

PubMed

Richard, D

2007-09-01

Energy stores are regulated through complex neural controls exerted on both food intake and energy expenditure. These controls are insured by interconnected neurons that produce different peptides or classic neurotransmitters, which have been regrouped into anabolic' and catabolic' systems. While the control of energy intake has been addressed in numerous investigations, that of energy expenditure has, as yet, only received a moderate interest, even though energy expenditure represents a key determinant of energy balance. In laboratory rodents, in particular, a strong regulatory control is exerted on brown adipose tissue (BAT), which represent an efficient thermogenic effector. BAT thermogenesis is governed by the sympathetic nervous system (SNS), whose activity is controlled by neurons comprised in various brain regions, which include the paraventricular hypothalamic nucleus (PVH), the arcuate nucleus (ARC) and the lateral hypothalamus (LH). Proopiomelanocortin neurons from the ARC project to the PVH and terminate in the vicinity of the melanocortin-4 receptors, which are concentrated in the descending division of the PVH, which comprise neurons controlling the SNS outflow to BAT. The LH contains neurons producing melanin-concentrating hormone or orexins, which also are important peptides in the control of energy expenditure. These neurons are not only polysynaptically connected to BAT, but also linked to brains regions controlling motivated behaviors and locomotor activity and, consequently, their role in the control of energy expenditure could go beyond BAT thermogenesis.

Dorsal Raphe Dopamine Neurons Represent the Experience of Social Isolation.

PubMed

Matthews, Gillian A; Nieh, Edward H; Vander Weele, Caitlin M; Halbert, Sarah A; Pradhan, Roma V; Yosafat, Ariella S; Glober, Gordon F; Izadmehr, Ehsan M; Thomas, Rain E; Lacy, Gabrielle D; Wildes, Craig P; Ungless, Mark A; Tye, Kay M

2016-02-11

The motivation to seek social contact may arise from either positive or negative emotional states, as social interaction can be rewarding and social isolation can be aversive. While ventral tegmental area (VTA) dopamine (DA) neurons may mediate social reward, a cellular substrate for the negative affective state of loneliness has remained elusive. Here, we identify a functional role for DA neurons in the dorsal raphe nucleus (DRN), in which we observe synaptic changes following acute social isolation. DRN DA neurons show increased activity upon social contact following isolation, revealed by in vivo calcium imaging. Optogenetic activation of DRN DA neurons increases social preference but causes place avoidance. Furthermore, these neurons are necessary for promoting rebound sociability following an acute period of isolation. Finally, the degree to which these neurons modulate behavior is predicted by social rank, together supporting a role for DRN dopamine neurons in mediating a loneliness-like state. PAPERCLIP. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Taotie neurons regulate appetite in Drosophila

PubMed Central

Zhan, Yin Peng; Liu, Li; Zhu, Yan

2016-01-01

The brain has an essential role in maintaining a balance between energy intake and expenditure of the body. Deciphering the processes underlying the decision-making for timely feeding of appropriate amounts may improve our understanding of physiological and psychological disorders related to feeding control. Here, we identify a group of appetite-enhancing neurons in a behavioural screen for flies with increased appetite. Manipulating the activity of these neurons, which we name Taotie neurons, induces bidirectional changes in feeding motivation. Long-term stimulation of Taotie neurons results in flies with highly obese phenotypes. Furthermore, we show that the in vivo activity of Taotie neurons in the neuroendocrine region reflects the hunger/satiety states of un-manipulated animals, and that appetitive-enhancing Taotie neurons control the secretion of insulin, a known regulator of feeding behaviour. Thus, our study reveals a new set of neurons regulating feeding behaviour in the high brain regions that represents physiological hunger states and control feeding behaviour in Drosophila. PMID:27924813

Distributed Representation of Visual Objects by Single Neurons in the Human Brain

PubMed Central

Valdez, André B.; Papesh, Megan H.; Treiman, David M.; Smith, Kris A.; Goldinger, Stephen D.

2015-01-01

It remains unclear how single neurons in the human brain represent whole-object visual stimuli. While recordings in both human and nonhuman primates have shown distributed representations of objects (many neurons encoding multiple objects), recordings of single neurons in the human medial temporal lobe, taken as subjects' discriminated objects during multiple presentations, have shown gnostic representations (single neurons encoding one object). Because some studies suggest that repeated viewing may enhance neural selectivity for objects, we had human subjects discriminate objects in a single, more naturalistic viewing session. We found that, across 432 well isolated neurons recorded in the hippocampus and amygdala, the average fraction of objects encoded was 26%. We also found that more neurons encoded several objects versus only one object in the hippocampus (28 vs 18%, p < 0.001) and in the amygdala (30 vs 19%, p < 0.001). Thus, during realistic viewing experiences, typical neurons in the human medial temporal lobe code for a considerable range of objects, across multiple semantic categories. PMID:25834044

Anatomical Evidence for Classical and Extra-classical Receptive Field Completion Across the Discontinuous Horizontal Meridian Representation of Primate Area V2

PubMed Central

Jeffs, Janelle; Ichida, Jennifer M.; Federer, Frederick

2009-01-01

In primates, a split of the horizontal meridian (HM) representation at the V2 rostral border divides this area into dorsal (V2d) and ventral (V2v) halves (representing lower and upper visual quadrants, respectively), causing retinotopically neighboring loci across the HM to be distant within V2. How is perceptual continuity maintained across this discontinuous HM representation? Injections of neuroanatomical tracers in marmoset V2d demonstrated that cells near the V2d rostral border can maintain retinotopic continuity within their classical and extra-classical receptive field (RF), by making both local and long-range intra- and interareal connections with ventral cortex representing the upper visual quadrant. V2d neurons located <0.9–1.3 mm from the V2d rostral border, whose RFs presumably do not cross the HM, make nonretinotopic horizontal connections with V2v neurons in the supra- and infragranular layers. V2d neurons located <0.6–0.9 mm from the border, whose RFs presumably cross the HM, in addition make retinotopic local connections with V2v neurons in layer 4. V2d neurons also make interareal connections with upper visual field regions of extrastriate cortex, but not of MT or MTc outside the foveal representation. Labeled connections in ventral cortex appear to represent the “missing” portion of the connectional fields in V2d across the HM. We conclude that connections between dorsal and ventral cortex can create visual field continuity within a second-order discontinuous visual topography. PMID:18755777

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

PubMed Central

2017-01-01

Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making. PMID:28961245

Method of estimating natural recharge to the Edwards Aquifer in the San Antonio area, Texas

USGS Publications Warehouse

Puente, Celso

1978-01-01

The principal errors in the estimates of annual recharge are related to errors in estimating runoff in ungaged areas, which represent about 30 percent of the infiltration area. The estimated long-term average annual recharge in each basin, however, is probably representative of the actual recharge because the averaging procedure tends to cancel out the major errors.

Complexity of life via collective mind

NASA Technical Reports Server (NTRS)

Zak, Michail

2004-01-01

e mind is introduced as a set of simple intelligent units (say, neurons, or interacting agents), which can communicate by exchange of information without explicit global control. Incomplete information is compensated by a sequence of random guesses symmetrically distributed around expectations with prescribed variances. Both the expectations and variances are the invariants characterizing the whole class of agents. These invariants are stored as parameters of the collective mind, while they contribute into dynamical formalism of the agents' evolution, and in particular, into the reflective chains of their nested abstract images of the selves and non-selves. The proposed model consists of the system of stochastic differential equations in the Langevin form representing the motor dynamics, and the corresponding Fokker-Planck equation representing the mental dynamics (Motor dynamics describes the motion in physical space, while mental dynamics simulates the evolution of initial errors in terms of the probability density). The main departure of this model from Newtonian and statistical physics is due to a feedback from the mental to the motor dynamics which makes the Fokker-Planck equation nonlinear. Interpretation of this model from mathematical and physical viewpoints, as well as possible interpretation from biological, psychological, and social viewpoints are discussed. The model is illustrated by the dynamics of a dialog.

A Possible Role for End-Stopped V1 Neurons in the Perception of Motion: A Computational Model

PubMed Central

Zarei Eskikand, Parvin; Kameneva, Tatiana; Ibbotson, Michael R.; Burkitt, Anthony N.; Grayden, David B.

2016-01-01

We present a model of the early stages of processing in the visual cortex, in particular V1 and MT, to investigate the potential role of end-stopped V1 neurons in solving the aperture problem. A hierarchical network is used in which the incoming motion signals provided by complex V1 neurons and end-stopped V1 neurons proceed to MT neurons at the next stage. MT neurons are categorized into two types based on their function: integration and segmentation. The role of integration neurons is to propagate unambiguous motion signals arriving from those V1 neurons that emphasize object terminators (e.g. corners). Segmentation neurons detect the discontinuities in the input stimulus to control the activity of integration neurons. Although the activity of the complex V1 neurons at the terminators of the object accurately represents the direction of the motion, their level of activity is less than the activity of the neurons along the edges. Therefore, a model incorporating end-stopped neurons is essential to suppress ambiguous motion signals along the edges of the stimulus. It is shown that the unambiguous motion signals at terminators propagate over the rest of the object to achieve an accurate representation of motion. PMID:27741307

Metabolic Changes Following Perinatal Asphyxia: Role of Astrocytes and Their Interaction with Neurons.

PubMed

Logica, Tamara; Riviere, Stephanie; Holubiec, Mariana I; Castilla, Rocío; Barreto, George E; Capani, Francisco

2016-01-01

Perinatal Asphyxia (PA) represents an important cause of severe neurological deficits including delayed mental and motor development, epilepsy, major cognitive deficits and blindness. The interaction between neurons, astrocytes and endothelial cells plays a central role coupling energy supply with changes in neuronal activity. Traditionally, experimental research focused on neurons, whereas astrocytes have been more related to the damage mechanisms of PA. Astrocytes carry out a number of functions that are critical to normal nervous system function, including uptake of neurotransmitters, regulation of pH and ion concentrations, and metabolic support for neurons. In this work, we aim to review metabolic neuron-astrocyte interactions with the purpose of encourage further research in this area in the context of PA, which is highly complex and its mechanisms and pathways have not been fully elucidated to this day.

Mirror neurons encode the subjective value of an observed action.

PubMed

Caggiano, Vittorio; Fogassi, Leonardo; Rizzolatti, Giacomo; Casile, Antonino; Giese, Martin A; Thier, Peter

2012-07-17

Objects grasped by an agent have a value not only for the acting agent, but also for an individual observing the grasping act. The value that the observer attributes to the object that is grasped can be pivotal for selecting a possible behavioral response. Mirror neurons in area F5 of the monkey premotor cortex have been suggested to play a crucial role in the understanding of action goals. However, it has not been addressed if these neurons are also involved in representing the value of the grasped object. Here we report that observation-related neuronal responses of F5 mirror neurons are indeed modulated by the value that the monkey associates with the grasped object. These findings suggest that during action observation F5 mirror neurons have access to key information needed to shape the behavioral responses of the observer.

Metabolic Changes Following Perinatal Asphyxia: Role of Astrocytes and Their Interaction with Neurons

PubMed Central

Logica, Tamara; Riviere, Stephanie; Holubiec, Mariana I.; Castilla, Rocío; Barreto, George E.; Capani, Francisco

2016-01-01

Perinatal Asphyxia (PA) represents an important cause of severe neurological deficits including delayed mental and motor development, epilepsy, major cognitive deficits and blindness. The interaction between neurons, astrocytes and endothelial cells plays a central role coupling energy supply with changes in neuronal activity. Traditionally, experimental research focused on neurons, whereas astrocytes have been more related to the damage mechanisms of PA. Astrocytes carry out a number of functions that are critical to normal nervous system function, including uptake of neurotransmitters, regulation of pH and ion concentrations, and metabolic support for neurons. In this work, we aim to review metabolic neuron-astrocyte interactions with the purpose of encourage further research in this area in the context of PA, which is highly complex and its mechanisms and pathways have not been fully elucidated to this day. PMID:27445788

Neuronal growth promoting sesquiterpene-neolignans; syntheses and biological studies.

PubMed

Cheng, Xu; Harzdorf, Nicole; Khaing, Zin; Kang, Danby; Camelio, Andrew M; Shaw, Travis; Schmidt, Christine E; Siegel, Dionicio

2012-01-14

The use of small molecules that can promote neuronal growth represents a promising approach to regenerative science. Along these lines we have developed separate short or modular syntheses of the natural products caryolanemagnolol and clovanemagnolol, small molecules previously shown to promote neuronal growth and induce choline acetyltransferase activity. The postulated biosynthetic pathways, potentially leading to the assembly of these molecules in nature, have guided the laboratory syntheses, allowing the preparation of both natural products in as few as two steps. With synthetic access to the compounds as single enantiomers we have examined clovanemagnolol's ability to promote the growth of embryonic hippocampal and cortical neurons. Clovanemagnolol has been shown to be a potent neurotrophic agent, promoting neuronal growth at concentrations of 10 nM.

Human Cerebrospinal Fluid Promotes Neuronal Viability and Activity of Hippocampal Neuronal Circuits In Vitro

PubMed Central

Perez-Alcazar, Marta; Culley, Georgia; Lyckenvik, Tim; Mobarrez, Kristoffer; Bjorefeldt, Andreas; Wasling, Pontus; Seth, Henrik; Asztely, Frederik; Harrer, Andrea; Iglseder, Bernhard; Aigner, Ludwig; Hanse, Eric; Illes, Sebastian

2016-01-01

For decades it has been hypothesized that molecules within the cerebrospinal fluid (CSF) diffuse into the brain parenchyma and influence the function of neurons. However, the functional consequences of CSF on neuronal circuits are largely unexplored and unknown. A major reason for this is the absence of appropriate neuronal in vitro model systems, and it is uncertain if neurons cultured in pure CSF survive and preserve electrophysiological functionality in vitro. In this article, we present an approach to address how human CSF (hCSF) influences neuronal circuits in vitro. We validate our approach by comparing the morphology, viability, and electrophysiological function of single neurons and at the network level in rat organotypic slice and primary neuronal cultures cultivated either in hCSF or in defined standard culture media. Our results demonstrate that rodent hippocampal slices and primary neurons cultured in hCSF maintain neuronal morphology and preserve synaptic transmission. Importantly, we show that hCSF increases neuronal viability and the number of electrophysiologically active neurons in comparison to the culture media. In summary, our data indicate that hCSF represents a physiological environment for neurons in vitro and a superior culture condition compared to the defined standard media. Moreover, this experimental approach paves the way to assess the functional consequences of CSF on neuronal circuits as well as suggesting a novel strategy for central nervous system (CNS) disease modeling. PMID:26973467

What is a grandmother cell? And how would you know if you found one?

NASA Astrophysics Data System (ADS)

Bowers, Jeffrey S.

2011-06-01

The key claim associated with a grandmother cell theory is that single neurons selectively represent one complex 'thing' (e.g. object and face). However, this theory is often mischaracterised in the cognitive and neuroscience literatures. I summarise two common confusions here. First, critics of grandmother cells often fail to distinguish between the selectivity and sparseness of neural firing and, as a result, predict (incorrectly) that one and only one neuron should fire in response to a given input. Second, critics often fail to distinguish between what a neuron responds to and what it represents - as detailed below - and as a result, predict (incorrectly) that a grandmother cell should fire in response to one and only one thing. I argue that these two confusions often lead to the premature rejection of grandmother cell theories.

Pediatric neurological syndromes and inborn errors of purine metabolism.

PubMed

Camici, Marcella; Micheli, Vanna; Ipata, Piero Luigi; Tozzi, Maria Grazia

2010-02-01

This review is devised to gather the presently known inborn errors of purine metabolism that manifest neurological pediatric syndromes. The aim is to draw a comprehensive picture of these rare diseases, characterized by unexpected and often devastating neurological symptoms. Although investigated for many years, most purine metabolism disorders associated to psychomotor dysfunctions still hide the molecular link between the metabolic derangement and the neurological manifestations. This basically indicates that many of the actual functions of nucleosides and nucleotides in the development and function of several organs, in particular central nervous system, are still unknown. Both superactivity and deficiency of phosphoribosylpyrophosphate synthetase cause hereditary disorders characterized, in most cases, by neurological impairments. The deficiency of adenylosuccinate lyase and 5-amino-4-imidazolecarboxamide ribotide transformylase/IMP cyclohydrolase, both belonging to the de novo purine synthesis pathway, is also associated to severe neurological manifestations. Among catabolic enzymes, hyperactivity of ectosolic 5'-nucleotidase, as well as deficiency of purine nucleoside phosphorylase and adenosine deaminase also lead to syndromes affecting the central nervous system. The most severe pathologies are associated to the deficiency of the salvage pathway enzymes hypoxanthine-guanine phosphoribosyltransferase and deoxyguanosine kinase: the former due to an unexplained adverse effect exerted on the development and/or differentiation of dopaminergic neurons, the latter due to a clear impairment of mitochondrial functions. The assessment of hypo- or hyperuricemic conditions is suggestive of purine enzyme dysfunctions, but most disorders of purine metabolism may escape the clinical investigation because they are not associated to these metabolic derangements. This review may represent a starting point stimulating both scientists and physicians involved in the study of neurological dysfunctions caused by inborn errors of purine metabolism with the aim to find novel therapeutical approaches. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

The types of neurons of the somatic oculomotor nucleus in the European bison. Nissl and Golgi studies.

PubMed

Szteyn, S; Robak, A; Równiak, M

1997-01-01

The neuronal structure of the somatic oculomotor nucleus (SON) was studied on the basis of Nissl and Golgi preparations, obtained from mesencephalons of 4 European bisons. We distinguished four types of neurons in the investigated nucleus: 1. The large multipolar nerve cells with 5-8 thick dendritic trunks and a thin axon which emerges directly from the soma. These are the most numerous neurons in the SON. 2. The small multipolar neurons. These cells have 4-6 thick dendritic trunks. An axon arises mostly from initial segment of one of the dendrites. This type represents about 8% neurons of SON. 3. The triangular neurons. From perikaryon 3 thick dendritic trunks emerge. A thin axon arises directly from the cell body. These cells make about 10% neurons of SON. 4. The pear-shaped cells which have 1 or 2 dendritic trunks concentrate at one pole of the neurons. In the SON there are about 2% pear-shaped cells. Their features correspond to the features attributed by many authors to the interneurons.

Spinal interneurons differentiate sequentially from those driving the fastest swimming movements in larval zebrafish to those driving the slowest ones.

PubMed

McLean, David L; Fetcho, Joseph R

2009-10-28

Studies of neuronal networks have revealed few general principles that link patterns of development with later functional roles. While investigating the neural control of movements, we recently discovered a topographic map in the spinal cord of larval zebrafish that relates the position of motoneurons and interneurons to their order of recruitment during swimming. Here, we show that the map reflects an orderly pattern of differentiation of neurons driving different movements. First, we use high-speed filming to show that large-amplitude swimming movements with bending along much of the body appear first, with smaller, regional swimming movements emerging later. Next, using whole-cell patch recordings, we demonstrate that the excitatory circuits that drive large-amplitude, fast swimming movements at larval stages are present and functional early on in embryos. Finally, we systematically assess the orderly emergence of spinal circuits according to swimming speed using transgenic fish expressing the photoconvertible protein Kaede to track neuronal differentiation in vivo. We conclude that a simple principle governs the development of spinal networks in which the neurons driving the fastest, most powerful swimming in larvae develop first with ones that drive increasingly weaker and slower larval movements layered on over time. Because the neurons are arranged by time of differentiation in the spinal cord, the result is a topographic map that represents the speed/strength of movements at which neurons are recruited and the temporal emergence of networks. This pattern may represent a general feature of neuronal network development throughout the brain and spinal cord.

Encoding of Target Detection during Visual Search by Single Neurons in the Human Brain.

PubMed

Wang, Shuo; Mamelak, Adam N; Adolphs, Ralph; Rutishauser, Ueli

2018-06-08

Neurons in the primate medial temporal lobe (MTL) respond selectively to visual categories such as faces, contributing to how the brain represents stimulus meaning. However, it remains unknown whether MTL neurons continue to encode stimulus meaning when it changes flexibly as a function of variable task demands imposed by goal-directed behavior. While classically associated with long-term memory, recent lesion and neuroimaging studies show that the MTL also contributes critically to the online guidance of goal-directed behaviors such as visual search. Do such tasks modulate responses of neurons in the MTL, and if so, do their responses mirror bottom-up input from visual cortices or do they reflect more abstract goal-directed properties? To answer these questions, we performed concurrent recordings of eye movements and single neurons in the MTL and medial frontal cortex (MFC) in human neurosurgical patients performing a memory-guided visual search task. We identified a distinct population of target-selective neurons in both the MTL and MFC whose response signaled whether the currently fixated stimulus was a target or distractor. This target-selective response was invariant to visual category and predicted whether a target was detected or missed behaviorally during a given fixation. The response latencies, relative to fixation onset, of MFC target-selective neurons preceded those in the MTL by ∼200 ms, suggesting a frontal origin for the target signal. The human MTL thus represents not only fixed stimulus identity, but also task-specified stimulus relevance due to top-down goal relevance. Copyright © 2018 Elsevier Ltd. All rights reserved.

Extracellular space preservation aids the connectomic analysis of neural circuits.

PubMed

Pallotto, Marta; Watkins, Paul V; Fubara, Boma; Singer, Joshua H; Briggman, Kevin L

2015-12-09

Dense connectomic mapping of neuronal circuits is limited by the time and effort required to analyze 3D electron microscopy (EM) datasets. Algorithms designed to automate image segmentation suffer from substantial error rates and require significant manual error correction. Any improvement in segmentation error rates would therefore directly reduce the time required to analyze 3D EM data. We explored preserving extracellular space (ECS) during chemical tissue fixation to improve the ability to segment neurites and to identify synaptic contacts. ECS preserved tissue is easier to segment using machine learning algorithms, leading to significantly reduced error rates. In addition, we observed that electrical synapses are readily identified in ECS preserved tissue. Finally, we determined that antibodies penetrate deep into ECS preserved tissue with only minimal permeabilization, thereby enabling correlated light microscopy (LM) and EM studies. We conclude that preservation of ECS benefits multiple aspects of the connectomic analysis of neural circuits.

Common medial frontal mechanisms of adaptive control in humans and rodents

PubMed Central

Frank, Michael J.; Laubach, Mark

2013-01-01

In this report, we describe how common brain networks within the medial frontal cortex facilitate adaptive behavioral control in rodents and humans. We demonstrate that low frequency oscillations below 12 Hz are dramatically modulated after errors in humans over mid-frontal cortex and in rats within prelimbic and anterior cingulate regions of medial frontal cortex. These oscillations were phase-locked between medial frontal cortex and motor areas in both rats and humans. In rats, single neurons that encoded prior behavioral outcomes were phase-coherent with low-frequency field oscillations particularly after errors. Inactivating medial frontal regions in rats led to impaired behavioral adjustments after errors, eliminated the differential expression of low frequency oscillations after errors, and increased low-frequency spike-field coupling within motor cortex. Our results describe a novel mechanism for behavioral adaptation via low-frequency oscillations and elucidate how medial frontal networks synchronize brain activity to guide performance. PMID:24141310

The Error in Total Error Reduction

PubMed Central

Witnauer, James E.; Urcelay, Gonzalo P.; Miller, Ralph R.

2013-01-01

Most models of human and animal learning assume that learning is proportional to the discrepancy between a delivered outcome and the outcome predicted by all cues present during that trial (i.e., total error across a stimulus compound). This total error reduction (TER) view has been implemented in connectionist and artificial neural network models to describe the conditions under which weights between units change. Electrophysiological work has revealed that the activity of dopamine neurons is correlated with the total error signal in models of reward learning. Similar neural mechanisms presumably support fear conditioning, human contingency learning, and other types of learning. Using a computational modelling approach, we compared several TER models of associative learning to an alternative model that rejects the TER assumption in favor of local error reduction (LER), which assumes that learning about each cue is proportional to the discrepancy between the delivered outcome and the outcome predicted by that specific cue on that trial. The LER model provided a better fit to the reviewed data than the TER models. Given the superiority of the LER model with the present data sets, acceptance of TER should be tempered. PMID:23891930

Synchronization in neural nets

NASA Technical Reports Server (NTRS)

Vidal, Jacques J.; Haggerty, John

1988-01-01

The paper presents an artificial neural network concept (the Synchronizable Oscillator Networks) where the instants of individual firings in the form of point processes constitute the only form of information transmitted between joining neurons. In the model, neurons fire spontaneously and regularly in the absence of perturbation. When interaction is present, the scheduled firings are advanced or delayed by the firing of neighboring neurons. Networks of such neurons become global oscillators which exhibit multiple synchronizing attractors. From arbitrary initial states, energy minimization learning procedures can make the network converge to oscillatory modes that satisfy multi-dimensional constraints. Such networks can directly represent routing and scheduling problems that consist of ordering sequences of events.

Extended artificial neural networks: incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels.

PubMed

Mueller, Amy V; Hemond, Harold F

2013-12-15

A novel artificial neural network (ANN) architecture is proposed which explicitly incorporates a priori system knowledge, i.e., relationships between output signals, while preserving the unconstrained non-linear function estimator characteristics of the traditional ANN. A method is provided for architecture layout, disabling training on a subset of neurons, and encoding system knowledge into the neuron structure. The novel architecture is applied to raw readings from a chemical sensor multi-probe (electric tongue), comprised of off-the-shelf ion selective electrodes (ISEs), to estimate individual ion concentrations in solutions at environmentally relevant concentrations and containing environmentally representative ion mixtures. Conductivity measurements and the concept of charge balance are incorporated into the ANN structure, resulting in (1) removal of estimation bias typically seen with use of ISEs in mixtures of unknown composition and (2) improvement of signal estimation by an order of magnitude or more for both major and minor constituents relative to use of ISEs as stand-alone sensors and error reduction by 30-50% relative to use of standard ANN models. This method is suggested as an alternative to parameterization of traditional models (e.g., Nikolsky-Eisenman), for which parameters are strongly dependent on both analyte concentration and temperature, and to standard ANN models which have no mechanism for incorporation of system knowledge. Network architecture and weighting are presented for the base case where the dot product can be used to relate ion concentrations to both conductivity and charge balance as well as for an extension to log-normalized data where the model can no longer be represented in this manner. While parameterization in this case study is analyte-dependent, the architecture is generalizable, allowing application of this method to other environmental problems for which mathematical constraints can be explicitly stated. © 2013 Elsevier B.V. All rights reserved.

Responses of neurons in the medial column of the inferior olive in pigeons to translational and rotational optic flowfields.

PubMed

Winship, I R; Wylie, D R

2001-11-01

The responses of neurons in the medial column of the inferior olive to translational and rotational optic flow were recorded from anaesthetized pigeons. Panoramic translational or rotational flowfields were produced by mechanical devices that projected optic flow patterns onto the walls, ceiling and floor of the room. The axis of rotation/translation could be positioned to any orientation in three-dimensional space such that axis tuning could be determined. Each neuron was assigned a vector representing the axis about/along which the animal would rotate/translate to produce the flowfield that elicited maximal modulation. Both translation-sensitive and rotation-sensitive neurons were found. For neurons responsive to translational optic flow, the preferred axis is described with reference to a standard right-handed coordinate system, where +x, +y and +z represent rightward, upward and forward translation of the animal, respectively (assuming that all recordings were from the right side of the brain). t(+y) neurons were maximally excited in response to a translational optic flowfield that results from self-translation upward along the vertical (y) axis. t(-y) neurons also responded best to translational optic flow along the vertical axis but showed the opposite direction preference. The two remaining groups, t(-x+z) and t(-x-z) neurons, responded best to translational optic flow along horizontal axes that were oriented 45 degrees to the midline. There were two types of neurons responsive to rotational optic flow: rVA neurons preferred rotation about the vertical axis, and rH135c neurons preferred rotation about a horizontal axis at 135 degrees contralateral azimuth. The locations of marking lesions indicated a clear topographical organization of the six response types. In summary, our results reinforce that the olivo-cerebellar system dedicated to the analysis of optic flow is organized according to a reference frame consisting of three approximately orthogonal axes: the vertical axis, and two horizontal axes oriented 45 degrees to either side the midline. Previous research has shown that the eye muscles, vestibular semicircular canals and postural control system all share a similar spatial frame of reference.

Generalized Adaptive Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1993-01-01

Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

A Mechanism for the Formation of Hippocampal Neuronal Firing Patterns that Represent What Happens Where

ERIC Educational Resources Information Center

Tort, Adriano B. L.; Komorowski, Robert; Kopell, Nancy; Eichenbaum, Howard

2011-01-01

The association of specific events with the context in which they occur is a fundamental feature of episodic memory. However, the underlying network mechanisms generating what-where associations are poorly understood. Recently we reported that some hippocampal principal neurons develop representations of specific events occurring in particular…

Anatomical characterization of PDF-tri neurons and peptidergic neurons associated with eclosion behavior in Drosophila.

PubMed

Selcho, Mareike; Mühlbauer, Barbara; Hensgen, Ronja; Shiga, Sakiko; Wegener, Christian; Yasuyama, Kouji

2018-06-01

The peptidergic Pigment-dispersing factor (PDF)-Tri neurons are a group of non-clock neurons that appear transiently around the time of adult ecdysis (=eclosion) in the fruit fly Drosophila melanogaster. This specific developmental pattern points to a function of these neurons in eclosion or other processes that are active around pupal-adult transition. As a first step to understand the role of these neurons, we here characterize the anatomy of the PDF-Tri neurons. In addition, we describe a further set of peptidergic neurons that have been associated with eclosion behavior, eclosion hormone (EH), and crustacean cardioactive peptide (CCAP) neurons, to single cell level in the pharate adult brain. PDF-Tri neurons as well as CCAP neurons co-express a classical transmitter indicated by the occurrence of small clear vesicles in addition to dense-core vesicles containing the peptides. In the tritocerebrum, gnathal ganglion and the superior protocerebrum PDF-Tri neurites contain peptidergic varicosities and both pre- and postsynaptic sites, suggesting that the PDF-Tri neurons represent modulatory rather than pure interneurons that connect the subesophageal zone with the superior protocerebrum. The extensive overlap of PDF-Tri arborizations with neurites of CCAP- and EH-expressing neurons in distinct brain regions provides anatomical evidence for a possible function of the PDF-Tri neurons in eclosion behavior. © 2018 Wiley Periodicals, Inc.

Optogenetic mimicry of the transient activation of dopamine neurons by natural reward is sufficient for operant reinforcement.

PubMed

Kim, Kyung Man; Baratta, Michael V; Yang, Aimei; Lee, Doheon; Boyden, Edward S; Fiorillo, Christopher D

2012-01-01

Activation of dopamine receptors in forebrain regions, for minutes or longer, is known to be sufficient for positive reinforcement of stimuli and actions. However, the firing rate of dopamine neurons is increased for only about 200 milliseconds following natural reward events that are better than expected, a response which has been described as a "reward prediction error" (RPE). Although RPE drives reinforcement learning (RL) in computational models, it has not been possible to directly test whether the transient dopamine signal actually drives RL. Here we have performed optical stimulation of genetically targeted ventral tegmental area (VTA) dopamine neurons expressing Channelrhodopsin-2 (ChR2) in mice. We mimicked the transient activation of dopamine neurons that occurs in response to natural reward by applying a light pulse of 200 ms in VTA. When a single light pulse followed each self-initiated nose poke, it was sufficient in itself to cause operant reinforcement. Furthermore, when optical stimulation was delivered in separate sessions according to a predetermined pattern, it increased locomotion and contralateral rotations, behaviors that are known to result from activation of dopamine neurons. All three of the optically induced operant and locomotor behaviors were tightly correlated with the number of VTA dopamine neurons that expressed ChR2, providing additional evidence that the behavioral responses were caused by activation of dopamine neurons. These results provide strong evidence that the transient activation of dopamine neurons provides a functional reward signal that drives learning, in support of RL theories of dopamine function.

Laboratory errors and patient safety.

PubMed

Miligy, Dawlat A

2015-01-01

Laboratory data are extensively used in medical practice; consequently, laboratory errors have a tremendous impact on patient safety. Therefore, programs designed to identify and reduce laboratory errors, as well as, setting specific strategies are required to minimize these errors and improve patient safety. The purpose of this paper is to identify part of the commonly encountered laboratory errors throughout our practice in laboratory work, their hazards on patient health care and some measures and recommendations to minimize or to eliminate these errors. Recording the encountered laboratory errors during May 2008 and their statistical evaluation (using simple percent distribution) have been done in the department of laboratory of one of the private hospitals in Egypt. Errors have been classified according to the laboratory phases and according to their implication on patient health. Data obtained out of 1,600 testing procedure revealed that the total number of encountered errors is 14 tests (0.87 percent of total testing procedures). Most of the encountered errors lay in the pre- and post-analytic phases of testing cycle (representing 35.7 and 50 percent, respectively, of total errors). While the number of test errors encountered in the analytic phase represented only 14.3 percent of total errors. About 85.7 percent of total errors were of non-significant implication on patients health being detected before test reports have been submitted to the patients. On the other hand, the number of test errors that have been already submitted to patients and reach the physician represented 14.3 percent of total errors. Only 7.1 percent of the errors could have an impact on patient diagnosis. The findings of this study were concomitant with those published from the USA and other countries. This proves that laboratory problems are universal and need general standardization and bench marking measures. Original being the first data published from Arabic countries that evaluated the encountered laboratory errors and launch the great need for universal standardization and bench marking measures to control the laboratory work.

An artificial network model for estimating the network structure underlying partially observed neuronal signals.

PubMed

Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

2014-01-01

Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Neural activity in cortical area V4 underlies fine disparity discrimination.

PubMed

Shiozaki, Hiroshi M; Tanabe, Seiji; Doi, Takahiro; Fujita, Ichiro

2012-03-14

Primates are capable of discriminating depth with remarkable precision using binocular disparity. Neurons in area V4 are selective for relative disparity, which is the crucial visual cue for discrimination of fine disparity. Here, we investigated the contribution of V4 neurons to fine disparity discrimination. Monkeys discriminated whether the center disk of a dynamic random-dot stereogram was in front of or behind its surrounding annulus. We first behaviorally tested the reference frame of the disparity representation used for performing this task. After learning the task with a set of surround disparities, the monkey generalized its responses to untrained surround disparities, indicating that the perceptual decisions were generated from a disparity representation in a relative frame of reference. We then recorded single-unit responses from V4 while the monkeys performed the task. On average, neuronal thresholds were higher than the behavioral thresholds. The most sensitive neurons reached thresholds as low as the psychophysical thresholds. For subthreshold disparities, the monkeys made frequent errors. The variable decisions were predictable from the fluctuation in the neuronal responses. The predictions were based on a decision model in which each V4 neuron transmits the evidence for the disparity it prefers. We finally altered the disparity representation artificially by means of microstimulation to V4. The decisions were systematically biased when microstimulation boosted the V4 responses. The bias was toward the direction predicted from the decision model. We suggest that disparity signals carried by V4 neurons underlie precise discrimination of fine stereoscopic depth.

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.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

PubMed

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

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns

PubMed Central

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

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe. PMID:24223789

Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

PubMed Central

Ebner, Marc; Hameroff, Stuart

2011-01-01

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot”) suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain. PMID:22046178

Multiplex Networks of Cortical and Hippocampal Neurons Revealed at Different Timescales

PubMed Central

Timme, Nicholas; Ito, Shinya; Myroshnychenko, Maxym; Yeh, Fang-Chin; Hiolski, Emma; Hottowy, Pawel; Beggs, John M.

2014-01-01

Recent studies have emphasized the importance of multiplex networks – interdependent networks with shared nodes and different types of connections – in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy – an information theoretic quantity that can be used to measure linear and nonlinear interactions – to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons (“hubs”) were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first systematic study of temporally dependent multiplex networks among individual neurons. PMID:25536059

Lateral information processing by spiking neurons: a theoretical model of the neural correlate of consciousness.

PubMed

Ebner, Marc; Hameroff, Stuart

2011-01-01

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on "autopilot"). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the "conscious pilot") suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious "auto-pilot" cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways "gap junctions" in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain.

Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields.

PubMed

Yildiz, Izzet B; Mesgarani, Nima; Deneve, Sophie

2016-12-07

A primary goal of auditory neuroscience is to identify the sound features extracted and represented by auditory neurons. Linear encoding models, which describe neural responses as a function of the stimulus, have been primarily used for this purpose. Here, we provide theoretical arguments and experimental evidence in support of an alternative approach, based on decoding the stimulus from the neural response. We used a Bayesian normative approach to predict the responses of neurons detecting relevant auditory features, despite ambiguities and noise. We compared the model predictions to recordings from the primary auditory cortex of ferrets and found that: (1) the decoding filters of auditory neurons resemble the filters learned from the statistics of speech sounds; (2) the decoding model captures the dynamics of responses better than a linear encoding model of similar complexity; and (3) the decoding model accounts for the accuracy with which the stimulus is represented in neural activity, whereas linear encoding model performs very poorly. Most importantly, our model predicts that neuronal responses are fundamentally shaped by "explaining away," a divisive competition between alternative interpretations of the auditory scene. Neural responses in the auditory cortex are dynamic, nonlinear, and hard to predict. Traditionally, encoding models have been used to describe neural responses as a function of the stimulus. However, in addition to external stimulation, neural activity is strongly modulated by the responses of other neurons in the network. We hypothesized that auditory neurons aim to collectively decode their stimulus. In particular, a stimulus feature that is decoded (or explained away) by one neuron is not explained by another. We demonstrated that this novel Bayesian decoding model is better at capturing the dynamic responses of cortical neurons in ferrets. Whereas the linear encoding model poorly reflects selectivity of neurons, the decoding model can account for the strong nonlinearities observed in neural data. Copyright © 2016 Yildiz et al.

Spiking Neurons for Analysis of Patterns

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance

2008-01-01

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Theta Coordinated Error Driven Learning in the Hippocampus (Open Access, Publisher’s Version)

DTIC Science & Technology

2013-06-06

assumed to be Hebbian in nature, where individual neurons in an engram join together with synaptic weight increases to support facilitated recall of...together as part of a memory or engram representation, e.g., in the central area CA3 of the hippocampus. With these connections strengthened, the ability

Multiplex visibility graphs to investigate recurrent neural network dynamics

NASA Astrophysics Data System (ADS)

Bianchi, Filippo Maria; Livi, Lorenzo; Alippi, Cesare; Jenssen, Robert

2017-03-01

A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design principled unsupervised methods to derive hyperparameters configurations yielding maximal ESN performance, expressed in terms of prediction error and memory capacity. In particular, we propose to model time series generated by each neuron activations with a horizontal visibility graph, whose topological properties have been shown to be related to the underlying system dynamics. Successively, horizontal visibility graphs associated with all neurons become layers of a larger structure called a multiplex. We show that topological properties of such a multiplex reflect important features of ESN dynamics that can be used to guide the tuning of its hyperparamers. Results obtained on several benchmarks and a real-world dataset of telephone call data records show the effectiveness of the proposed methods.

Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex

PubMed Central

Lapish, Christopher C.; Durstewitz, Daniel; Chandler, L. Judson; Seamans, Jeremy K.

2008-01-01

Successful decision making requires an ability to monitor contexts, actions, and outcomes. The anterior cingulate cortex (ACC) is thought to be critical for these functions, monitoring and guiding decisions especially in challenging situations involving conflict and errors. A number of different single-unit correlates have been observed in the ACC that reflect the diverse cognitive components involved. Yet how ACC neurons function as an integrated network is poorly understood. Here we show, using advanced population analysis of multiple single-unit recordings from the rat ACC during performance of an ecologically valid decision-making task, that ensembles of neurons move through different coherent and dissociable states as the cognitive requirements of the task change. This organization into distinct network patterns with respect to both firing-rate changes and correlations among units broke down during trials with numerous behavioral errors, especially at choice points of the task. These results point to an underlying functional organization into cell assemblies in the ACC that may monitor choices, outcomes, and task contexts, thus tracking the animal's progression through “task space.” PMID:18708525

Multiplex visibility graphs to investigate recurrent neural network dynamics

PubMed Central

Bianchi, Filippo Maria; Livi, Lorenzo; Alippi, Cesare; Jenssen, Robert

2017-01-01

A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design principled unsupervised methods to derive hyperparameters configurations yielding maximal ESN performance, expressed in terms of prediction error and memory capacity. In particular, we propose to model time series generated by each neuron activations with a horizontal visibility graph, whose topological properties have been shown to be related to the underlying system dynamics. Successively, horizontal visibility graphs associated with all neurons become layers of a larger structure called a multiplex. We show that topological properties of such a multiplex reflect important features of ESN dynamics that can be used to guide the tuning of its hyperparamers. Results obtained on several benchmarks and a real-world dataset of telephone call data records show the effectiveness of the proposed methods. PMID:28281563

Mechanosensing is critical for axon growth in the developing brain

PubMed Central

Pillai, Eva K.; Sheridan, Graham K.; Svoboda, Hanno; Viana, Matheus; da F. Costa, Luciano; Guck, Jochen; Holt, Christine E.; Franze, Kristian

2016-01-01

During nervous system development, neurons extend axons along well-defined pathways. The current understanding of axon pathfinding is based mainly on chemical signalling. However, growing neurons interact not only chemically but also mechanically with their environment. Here we identify mechanical signals as important regulators of axon pathfinding. In vitro, substrate stiffness determined growth patterns of Xenopus retinal ganglion cell (RGC) axons. In vivo atomic force microscopy revealed striking stiffness gradient patterns in the embryonic brain. RGC axons grew towards the tissue’s softer side, which was reproduced in vitro in the absence of chemical gradients. To test the importance of mechanical signals for axon growth in vivo, we altered brain stiffness, blocked mechanotransduction pharmacologically, and knocked down the mechanosensitive ion channel Piezo1. All treatments resulted in aberrant axonal growth and pathfinding errors, suggesting that local tissue stiffness–read out by mechanosensitive ion channels–is critically involved in instructing neuronal growth in vivo. PMID:27643431

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning.

PubMed

Franklin, Nicholas T; Frank, Michael J

2015-12-25

Convergent evidence suggests that the basal ganglia support reinforcement learning by adjusting action values according to reward prediction errors. However, adaptive behavior in stochastic environments requires the consideration of uncertainty to dynamically adjust the learning rate. We consider how cholinergic tonically active interneurons (TANs) may endow the striatum with such a mechanism in computational models spanning three Marr's levels of analysis. In the neural model, TANs modulate the excitability of spiny neurons, their population response to reinforcement, and hence the effective learning rate. Long TAN pauses facilitated robustness to spurious outcomes by increasing divergence in synaptic weights between neurons coding for alternative action values, whereas short TAN pauses facilitated stochastic behavior but increased responsiveness to change-points in outcome contingencies. A feedback control system allowed TAN pauses to be dynamically modulated by uncertainty across the spiny neuron population, allowing the system to self-tune and optimize performance across stochastic environments.

Bayesian Integration of Information in Hippocampal Place Cells

PubMed Central

Madl, Tamas; Franklin, Stan; Chen, Ke; Montaldi, Daniela; Trappl, Robert

2014-01-01

Accurate spatial localization requires a mechanism that corrects for errors, which might arise from inaccurate sensory information or neuronal noise. In this paper, we propose that Hippocampal place cells might implement such an error correction mechanism by integrating different sources of information in an approximately Bayes-optimal fashion. We compare the predictions of our model with physiological data from rats. Our results suggest that useful predictions regarding the firing fields of place cells can be made based on a single underlying principle, Bayesian cue integration, and that such predictions are possible using a remarkably small number of model parameters. PMID:24603429

Robust Representation of Stable Object Values in the Oculomotor Basal Ganglia

PubMed Central

Yasuda, Masaharu; Yamamoto, Shinya; Hikosaka, Okihide

2012-01-01

Our gaze tends to be directed to objects previously associated with rewards. Such object values change flexibly or remain stable. Here we present evidence that the monkey substantia nigra pars reticulata (SNr) in the basal ganglia represents stable, rather than flexible, object values. After across-day learning of object–reward association, SNr neurons gradually showed a response bias to surprisingly many visual objects: inhibition to high-valued objects and excitation to low-valued objects. Many of these neurons were shown to project to the ipsilateral superior colliculus. This neuronal bias remained intact even after >100 d without further learning. In parallel with the neuronal bias, the monkeys tended to look at high-valued objects. The neuronal and behavioral biases were present even if no value was associated during testing. These results suggest that SNr neurons bias the gaze toward objects that were consistently associated with high values in one’s history. PMID:23175843

Mirror neurons encode the subjective value of an observed action

PubMed Central

Caggiano, Vittorio; Fogassi, Leonardo; Rizzolatti, Giacomo; Casile, Antonino; Giese, Martin A.; Thier, Peter

2012-01-01

Objects grasped by an agent have a value not only for the acting agent, but also for an individual observing the grasping act. The value that the observer attributes to the object that is grasped can be pivotal for selecting a possible behavioral response. Mirror neurons in area F5 of the monkey premotor cortex have been suggested to play a crucial role in the understanding of action goals. However, it has not been addressed if these neurons are also involved in representing the value of the grasped object. Here we report that observation-related neuronal responses of F5 mirror neurons are indeed modulated by the value that the monkey associates with the grasped object. These findings suggest that during action observation F5 mirror neurons have access to key information needed to shape the behavioral responses of the observer. PMID:22753471

Striatal action-value neurons reconsidered.

PubMed

Elber-Dorozko, Lotem; Loewenstein, Yonatan

2018-05-31

It is generally believed that during economic decisions, striatal neurons represent the values associated with different actions. This hypothesis is based on studies, in which the activity of striatal neurons was measured while the subject was learning to prefer the more rewarding action. Here we show that these publications are subject to at least one of two critical confounds. First, we show that even weak temporal correlations in the neuronal data may result in an erroneous identification of action-value representations. Second, we show that experiments and analyses designed to dissociate action-value representation from the representation of other decision variables cannot do so. We suggest solutions to identifying action-value representation that are not subject to these confounds. Applying one solution to previously identified action-value neurons in the basal ganglia we fail to detect action-value representations. We conclude that the claim that striatal neurons encode action-values must await new experiments and analyses. © 2018, Elber-Dorozko et al.

Central auditory neurons have composite receptive fields.

PubMed

Kozlov, Andrei S; Gentner, Timothy Q

2016-02-02

High-level neurons processing complex, behaviorally relevant signals are sensitive to conjunctions of features. Characterizing the receptive fields of such neurons is difficult with standard statistical tools, however, and the principles governing their organization remain poorly understood. Here, we demonstrate multiple distinct receptive-field features in individual high-level auditory neurons in a songbird, European starling, in response to natural vocal signals (songs). We then show that receptive fields with similar characteristics can be reproduced by an unsupervised neural network trained to represent starling songs with a single learning rule that enforces sparseness and divisive normalization. We conclude that central auditory neurons have composite receptive fields that can arise through a combination of sparseness and normalization in neural circuits. Our results, along with descriptions of random, discontinuous receptive fields in the central olfactory neurons in mammals and insects, suggest general principles of neural computation across sensory systems and animal classes.

Study on algorithm of process neural network for soft sensing in sewage disposal system

NASA Astrophysics Data System (ADS)

Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

2006-11-01

A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

Potential clinical relevance of the 'little brain' on the mammalian heart.

PubMed

Armour, J A

2008-02-01

It is hypothesized that the heart possesses a nervous system intrinsic to it that represents the final relay station for the co-ordination of regional cardiac indices. This 'little brain' on the heart is comprised of spatially distributed sensory (afferent), interconnecting (local circuit) and motor (adrenergic and cholinergic efferent) neurones that communicate with others in intrathoracic extracardiac ganglia, all under the tonic influence of central neuronal command and circulating catecholamines. Neurones residing from the level of the heart to the insular cortex form temporally dependent reflexes that control overlapping, spatially determined cardiac indices. The emergent properties that most of its components display depend primarily on sensory transduction of the cardiovascular milieu. It is further hypothesized that the stochastic nature of such neuronal interactions represents a stabilizing feature that matches cardiac output to normal corporal blood flow demands. Thus, with regard to cardiac disease states, one must consider not only cardiac myocyte dysfunction but also the fact that components within this neuroaxis may interact abnormally to alter myocyte function. This review emphasizes the stochastic behaviour displayed by most peripheral cardiac neurones, which appears to be a consequence of their predominant cardiac chemosensory inputs, as well as their complex functional interconnectivity. Despite our limited understanding of the whole, current data indicate that the emergent properties displayed by most neurones comprising the cardiac neuroaxis will have to be taken into consideration when contemplating the targeting of its individual components if predictable, long-term therapeutic benefits are to accrue.

A Communication Theoretical Modeling of Axonal Propagation in Hippocampal Pyramidal Neurons.

PubMed

Ramezani, Hamideh; Akan, Ozgur B

2017-06-01

Understanding the fundamentals of communication among neurons, known as neuro-spike communication, leads to reach bio-inspired nanoscale communication paradigms. In this paper, we focus on a part of neuro-spike communication, known as axonal transmission, and propose a realistic model for it. The shape of the spike during axonal transmission varies according to previously applied stimulations to the neuron, and these variations affect the amount of information communicated between neurons. Hence, to reach an accurate model for neuro-spike communication, the memory of axon and its effect on the axonal transmission should be considered, which are not studied in the existing literature. In this paper, we extract the important factors on the memory of axon and define memory states based on these factors. We also describe the transition among these states and the properties of axonal transmission in each of them. Finally, we demonstrate that the proposed model can follow changes in the axonal functionality properly by simulating the proposed model and reporting the root mean square error between simulation results and experimental data.

Minimum energy control for a two-compartment neuron to extracellular electric fields

NASA Astrophysics Data System (ADS)

Yi, Guo-Sheng; Wang, Jiang; Li, Hui-Yan; Wei, Xi-Le; Deng, Bin

2016-11-01

The energy optimization of extracellular electric field (EF) stimulus for a neuron is considered in this paper. We employ the optimal control theory to design a low energy EF input for a reduced two-compartment model. It works by driving the neuron to closely track a prescriptive spike train. A cost function is introduced to balance the contradictory objectives, i.e., tracking errors and EF stimulus energy. By using the calculus of variations, we transform the minimization of cost function to a six-dimensional two-point boundary value problem (BVP). Through solving the obtained BVP in the cases of three fundamental bifurcations, it is shown that the control method is able to provide an optimal EF stimulus of reduced energy for the neuron to effectively track a prescriptive spike train. Further, the feasibility of the adopted method is interpreted from the point of view of the biophysical basis of spike initiation. These investigations are conducive to designing stimulating dose for extracellular neural stimulation, which are also helpful to interpret the effects of extracellular field on neural activity.

High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level

PubMed Central

Gong, Hui; Xu, Dongli; Yuan, Jing; Li, Xiangning; Guo, Congdi; Peng, Jie; Li, Yuxin; Schwarz, Lindsay A.; Li, Anan; Hu, Bihe; Xiong, Benyi; Sun, Qingtao; Zhang, Yalun; Liu, Jiepeng; Zhong, Qiuyuan; Xu, Tonghui; Zeng, Shaoqun; Luo, Qingming

2016-01-01

The precise annotation and accurate identification of neural structures are prerequisites for studying mammalian brain function. The orientation of neurons and neural circuits is usually determined by mapping brain images to coarse axial-sampling planar reference atlases. However, individual differences at the cellular level likely lead to position errors and an inability to orient neural projections at single-cell resolution. Here, we present a high-throughput precision imaging method that can acquire a co-localized brain-wide data set of both fluorescent-labelled neurons and counterstained cell bodies at a voxel size of 0.32 × 0.32 × 2.0 μm in 3 days for a single mouse brain. We acquire mouse whole-brain imaging data sets of multiple types of neurons and projections with anatomical annotation at single-neuron resolution. The results show that the simultaneous acquisition of labelled neural structures and cytoarchitecture reference in the same brain greatly facilitates precise tracing of long-range projections and accurate locating of nuclei. PMID:27374071

A cellular and regulatory map of the GABAergic nervous system of C. elegans

PubMed Central

Gendrel, Marie; Atlas, Emily G; Hobert, Oliver

2016-01-01

Neurotransmitter maps are important complements to anatomical maps and represent an invaluable resource to understand nervous system function and development. We report here a comprehensive map of neurons in the C. elegans nervous system that contain the neurotransmitter GABA, revealing twice as many GABA-positive neuron classes as previously reported. We define previously unknown glia-like cells that take up GABA, as well as 'GABA uptake neurons' which do not synthesize GABA but take it up from the extracellular environment, and we map the expression of previously uncharacterized ionotropic GABA receptors. We use the map of GABA-positive neurons for a comprehensive analysis of transcriptional regulators that define the GABA phenotype. We synthesize our findings of specification of GABAergic neurons with previous reports on the specification of glutamatergic and cholinergic neurons into a nervous system-wide regulatory map which defines neurotransmitter specification mechanisms for more than half of all neuron classes in C. elegans. DOI: http://dx.doi.org/10.7554/eLife.17686.001 PMID:27740909

Dopamine neurons learn relative chosen value from probabilistic rewards

PubMed Central

Lak, Armin; Stauffer, William R; Schultz, Wolfram

2016-01-01

Economic theories posit reward probability as one of the factors defining reward value. Individuals learn the value of cues that predict probabilistic rewards from experienced reward frequencies. Building on the notion that responses of dopamine neurons increase with reward probability and expected value, we asked how dopamine neurons in monkeys acquire this value signal that may represent an economic decision variable. We found in a Pavlovian learning task that reward probability-dependent value signals arose from experienced reward frequencies. We then assessed neuronal response acquisition during choices among probabilistic rewards. Here, dopamine responses became sensitive to the value of both chosen and unchosen options. Both experiments showed also the novelty responses of dopamine neurones that decreased as learning advanced. These results show that dopamine neurons acquire predictive value signals from the frequency of experienced rewards. This flexible and fast signal reflects a specific decision variable and could update neuronal decision mechanisms. DOI: http://dx.doi.org/10.7554/eLife.18044.001 PMID:27787196

Concomitant inhibition of prolyl hydroxylases and ROCK initiates differentiation of mesenchymal stem cells and PC12 towards the neuronal lineage.

PubMed

Pacary, Emilie; Petit, Edwige; Bernaudin, Myriam

2008-12-12

This study demonstrates that a prolyl hydroxylase inhibitor, FG-0041, is able, in combination with the ROCK inhibitor, Y-27632, to initiate differentiation of mesenchymal stem cells (MSCs) into neuron-like cells. FG-0041/Y-27632 co-treatment provokes morphological changes into neuron-like cells, increases neuronal marker expression and provokes modifications of cell cycle-related gene expression consistent with a cell cycle arrest of MSC, three events showing the engagement of MSC towards the neuronal lineage. Moreover, as we observed in our previous studies with cobalt chloride and desferroxamine, the activation of HIF-1 by this prolyl hydroxylase inhibitor is potentiated by Y-27632 which could explain at least in part the effect of this co-treatment on MSC neuronal differentiation. In addition, we show that this co-treatment enhances neurite outgrowth and tyrosine hydroxylase expression in PC12 cells. Altogether, these results evidence that concomitant inhibition of prolyl hydroxylases and ROCK represents a relevant protocol to initiate neuronal differentiation.

Divergent Hox Coding and Evasion of Retinoid Signaling Specifies Motor Neurons Innervating Digit Muscles

PubMed Central

Mendelsohn, Alana I.; Dasen, Jeremy S.; Jessell, Thomas M.

2017-01-01

Summary The establishment of spinal motor neuron subclass diversity is achieved through developmental programs that are aligned with the organization of muscle targets in the limb. The evolutionary emergence of digits represents a specialized adaptation of limb morphology, yet it remains unclear how the specification of digit-innervating motor neuron subtypes parallels the elaboration of digits. We show that digit-innervating motor neurons can be defined by selective gene markers and distinguished from other LMC neurons by the expression of a variant Hox gene repertoire and by the failure to express a key enzyme involved in retinoic acid synthesis. This divergent developmental program is sufficient to induce the specification of digit-innervating motor neurons, emphasizing the specialized status of digit control in the evolution of skilled motor behaviors. Our findings suggest that the emergence of digits in the limb is matched by distinct mechanisms for specifying motor neurons that innervate digit muscles. PMID:28190640

Pathological effects of chronic myocardial infarction on peripheral neurons mediating cardiac neurotransmission.

PubMed

Nakamura, Keijiro; Ajijola, Olujimi A; Aliotta, Eric; Armour, J Andrew; Ardell, Jeffrey L; Shivkumar, Kalyanam

2016-05-01

To determine whether chronic myocardial infarction (MI) induces structural and neurochemical changes in neurons within afferent and efferent ganglia mediating cardiac neurotransmission. Neuronal somata in i) right atrial (RAGP) and ii) ventral interventricular ganglionated plexi (VIVGP), iii) stellate ganglia (SG) and iv) T1-2 dorsal root ganglia (DRG) bilaterally derived from normal (n=8) vs. chronic MI (n=8) porcine subjects were studied. We examined whether the morphology and neuronal nitric oxide synthase (nNOS) expression in soma of RAGP, VIVGP, DRG and SG neurons were altered as a consequence of chronic MI. In DRG, we also examined immunoreactivity of calcitonin gene related peptide (CGRP), a marker of afferent neurons. Chronic MI increased neuronal size and nNOS immunoreactivity in VIVGP (but not RAGP), as well as in the SG bilaterally. Across these ganglia, the increase in neuronal size was more pronounced in nNOS immunoreactive neurons. In the DRG, chronic MI also caused neuronal enlargement, and increased CGRP immunoreactivity. Further, DRG neurons expressing both nNOS and CGRP were increased in MI animals compared to controls, and represented a shift from double negative neurons. Chronic MI impacts diverse elements within the peripheral cardiac neuraxis. That chronic MI imposes such widespread, diverse remodeling of the peripheral cardiac neuraxis must be taken into consideration when contemplating neuronal regulation of the ischemic heart. Copyright © 2016 Elsevier B.V. All rights reserved.

PATHOLOGICAL EFFECTS OF CHRONIC MYOCARDIAL INFARCTION ON PERIPHERAL NEURONS MEDIATING CARDIAC NEUROTRANSMISSION

PubMed Central

Nakamura, Keijiro; Ajijola, Olujimi A.; Aliotta, Eric; Armour, J. Andrew; Ardell, Jeffrey L.; Shivkumar, Kalyanam

2016-01-01

Objective To determine whether chronic myocardial infarction (MI) induces structural and neurochemical changes in neurons within afferent and efferent ganglia mediating cardiac neurotransmission. Methods Neuronal somata in i) right atrial (RAGP) and ii) ventral interventricular ganglionated plexi (VIVGP), iii) stellate ganglia (SG) and iv) T1-2 dorsal root ganglia (DRG) bilaterally derived from normal (n = 8) vs. chronic MI (n = 8) porcine subjects were studied. We examined whether the morphology and neuronal nitric oxide synthase (nNOS) expression in soma of RAGP, VIVGP, DRG and SG neurons were altered as a consequence of chronic MI. In DRG, we also examined immunoreactivity of calcitonin gene related peptide (CGRP), a marker of afferent neurons. Results Chronic MI increased neuronal size and nNOS immunoreactivity in VIVGP (but not RAGP), as well as in the SG bilaterally. Across these ganglia, the increase in neuronal size was more pronounced in nNOS immunoreacitive neurons. In the DRG, chronic MI also caused neuronal enlargement, and increased CGRP immunoreactivity. Further, DRG neurons expressing both nNOS and CGRP were increased in MI animals compared to controls, and represented a shift from double negative neurons. Conclusions Chronic MI impacts diverse elements within the peripheral cardiac neuraxis. That chronic MI imposes such widespread, diverse remodeling of the peripheral cardiac neuraxis must be taken into consideration when contemplating neuronal regulation of the ischemic heart. PMID:27209472

Human iPSC-derived neurons and lymphoblastoid cells for personalized medicine research in neuropsychiatric disorders.

PubMed

Gurwitz, David

2016-09-01

The development and clinical implementation of personalized medicine crucially depends on the availability of high-quality human biosamples; animal models, although capable of modeling complex human diseases, cannot reflect the large variation in the human genome, epigenome, transcriptome, proteome, and metabolome. Although the biosamples available from public biobanks that store human tissues and cells may represent the large human diversity for most diseases, these samples are not always sufficient for developing biomarkers for patient-tailored therapies for neuropsychiatric disorders. Postmortem human tissues are available from many biobanks; nevertheless, collections of neuronal human cells from large patient cohorts representing the human diversity remain scarce. Two tools are gaining popularity for personalized medicine research on neuropsychiatric disorders: human induced pluripotent stem cell-derived neurons and human lymphoblastoid cell lines. This review examines and contrasts the advantages and limitations of each tool for personalized medicine research.

Morphological evidence for novel enteric neuronal circuitry in guinea pig distal colon.

PubMed

Smolilo, D J; Costa, M; Hibberd, T J; Wattchow, D A; Spencer, Nick J

2018-07-01

The gastrointestinal (GI) tract is unique compared to all other internal organs; it is the only organ with its own nervous system and its own population of intrinsic sensory neurons, known as intrinsic primary afferent neurons (IPANs). How these IPANs form neuronal circuits with other functional classes of neurons in the enteric nervous system (ENS) is incompletely understood. We used a combination of light microscopy, immunohistochemistry and confocal microscopy to examine the topographical distribution of specific classes of neurons in the myenteric plexus of guinea-pig colon, including putative IPANs, with other classes of enteric neurons. These findings were based on immunoreactivity to the neuronal markers, calbindin, calretinin and nitric oxide synthase. We then correlated the varicose outputs formed by putative IPANs with subclasses of excitatory interneurons and motor neurons. We revealed that calbindin-immunoreactive varicosities form specialized structures resembling 'baskets' within the majority of myenteric ganglia, which were arranged in clusters around calretinin-immunoreactive neurons. These calbindin baskets directly arose from projections of putative IPANs and represent morphological evidence of preferential input from sensory neurons directly to a select group of calretinin neurons. Our findings uncovered that these neurons are likely to be ascending excitatory interneurons and excitatory motor neurons. Our study reveals for the first time in the colon, a novel enteric neural circuit, whereby calbindin-immunoreactive putative sensory neurons form specialized varicose structures that likely direct synaptic outputs to excitatory interneurons and motor neurons. This circuit likely forms the basis of polarized neuronal pathways underlying motility. © 2018 Wiley Periodicals, Inc.

Minimization of model representativity errors in identification of point source emission from atmospheric concentration measurements

NASA Astrophysics Data System (ADS)

Sharan, Maithili; Singh, Amit Kumar; Singh, Sarvesh Kumar

2017-11-01

Estimation of an unknown atmospheric release from a finite set of concentration measurements is considered an ill-posed inverse problem. Besides ill-posedness, the estimation process is influenced by the instrumental errors in the measured concentrations and model representativity errors. The study highlights the effect of minimizing model representativity errors on the source estimation. This is described in an adjoint modelling framework and followed in three steps. First, an estimation of point source parameters (location and intensity) is carried out using an inversion technique. Second, a linear regression relationship is established between the measured concentrations and corresponding predicted using the retrieved source parameters. Third, this relationship is utilized to modify the adjoint functions. Further, source estimation is carried out using these modified adjoint functions to analyse the effect of such modifications. The process is tested for two well known inversion techniques, called renormalization and least-square. The proposed methodology and inversion techniques are evaluated for a real scenario by using concentrations measurements from the Idaho diffusion experiment in low wind stable conditions. With both the inversion techniques, a significant improvement is observed in the retrieval of source estimation after minimizing the representativity errors.

Practical guidance on representing the heteroscedasticity of residual errors of hydrological predictions

NASA Astrophysics Data System (ADS)

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Kuczera, George

2016-04-01

Appropriate representation of residual errors in hydrological modelling is essential for accurate and reliable probabilistic streamflow predictions. In particular, residual errors of hydrological predictions are often heteroscedastic, with large errors associated with high runoff events. Although multiple approaches exist for representing this heteroscedasticity, few if any studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating a range of approaches for representing heteroscedasticity in residual errors. These approaches include the 'direct' weighted least squares approach and 'transformational' approaches, such as logarithmic, Box-Cox (with and without fitting the transformation parameter), logsinh and the inverse transformation. The study reports (1) theoretical comparison of heteroscedasticity approaches, (2) empirical evaluation of heteroscedasticity approaches using a range of multiple catchments / hydrological models / performance metrics and (3) interpretation of empirical results using theory to provide practical guidance on the selection of heteroscedasticity approaches. Importantly, for hydrological practitioners, the results will simplify the choice of approaches to represent heteroscedasticity. This will enhance their ability to provide hydrological probabilistic predictions with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality).

Distributed representation of visual objects by single neurons in the human brain.

PubMed

Valdez, André B; Papesh, Megan H; Treiman, David M; Smith, Kris A; Goldinger, Stephen D; Steinmetz, Peter N

2015-04-01

It remains unclear how single neurons in the human brain represent whole-object visual stimuli. While recordings in both human and nonhuman primates have shown distributed representations of objects (many neurons encoding multiple objects), recordings of single neurons in the human medial temporal lobe, taken as subjects' discriminated objects during multiple presentations, have shown gnostic representations (single neurons encoding one object). Because some studies suggest that repeated viewing may enhance neural selectivity for objects, we had human subjects discriminate objects in a single, more naturalistic viewing session. We found that, across 432 well isolated neurons recorded in the hippocampus and amygdala, the average fraction of objects encoded was 26%. We also found that more neurons encoded several objects versus only one object in the hippocampus (28 vs 18%, p < 0.001) and in the amygdala (30 vs 19%, p < 0.001). Thus, during realistic viewing experiences, typical neurons in the human medial temporal lobe code for a considerable range of objects, across multiple semantic categories. Copyright © 2015 the authors 0270-6474/15/355180-07$15.00/0.

Peripheral Nervous System Genes Expressed in Central Neurons Induce Growth on Inhibitory Substrates

PubMed Central

Buchser, William J.; Smith, Robin P.; Pardinas, Jose R.; Haddox, Candace L.; Hutson, Thomas; Moon, Lawrence; Hoffman, Stanley R.; Bixby, John L.; Lemmon, Vance P.

2012-01-01

Trauma to the spinal cord and brain can result in irreparable loss of function. This failure of recovery is in part due to inhibition of axon regeneration by myelin and chondroitin sulfate proteoglycans (CSPGs). Peripheral nervous system (PNS) neurons exhibit increased regenerative ability compared to central nervous system neurons, even in the presence of inhibitory environments. Previously, we identified over a thousand genes differentially expressed in PNS neurons relative to CNS neurons. These genes represent intrinsic differences that may account for the PNS’s enhanced regenerative ability. Cerebellar neurons were transfected with cDNAs for each of these PNS genes to assess their ability to enhance neurite growth on inhibitory (CSPG) or permissive (laminin) substrates. Using high content analysis, we evaluated the phenotypic profile of each neuron to extract meaningful data for over 1100 genes. Several known growth associated proteins potentiated neurite growth on laminin. Most interestingly, novel genes were identified that promoted neurite growth on CSPGs (GPX3, EIF2B5, RBMX). Bioinformatic approaches also uncovered a number of novel gene families that altered neurite growth of CNS neurons. PMID:22701605

Neurons in the Amygdala with Response-Selectivity for Anxiety in Two Ethologically Based Tests

PubMed Central

Wang, Dong V.; Wang, Fang; Liu, Jun; Zhang, Lu; Wang, Zhiru; Lin, Longnian

2011-01-01

The amygdala is a key area in the brain for detecting potential threats or dangers, and further mediating anxiety. However, the neuronal mechanisms of anxiety in the amygdala have not been well characterized. Here we report that in freely-behaving mice, a group of neurons in the basolateral amygdala (BLA) fires tonically under anxiety conditions in both open-field and elevated plus-maze tests. The firing patterns of these neurons displayed a characteristic slow onset and progressively increased firing rates. Specifically, these firing patterns were correlated to a gradual development of anxiety-like behaviors in the open-field test. Moreover, these neurons could be activated by any impoverished environment similar to an open-field; and introduction of both comfortable and uncomfortable stimuli temporarily suppressed the activity of these BLA neurons. Importantly, the excitability of these BLA neurons correlated well with levels of anxiety. These results demonstrate that this type of BLA neuron is likely to represent anxiety and/or emotional values of anxiety elicited by anxiogenic environmental stressors. PMID:21494567

Primary cell culture of LHRH neurones from embryonic olfactory placode in the sheep (Ovis aries).

PubMed

Duittoz, A H; Batailler, M; Caldani, M

1997-09-01

The aim of this study was to establish an in vitro model of ovine luteinizing hormone-releasing hormone (LHRH) neurones. Olfactory placodes from 26 day-old sheep embryos (E26) were used for explant culture. Cultures were maintained successfully up to 35 days, but were usually used at 17 days for immunocytochemistry. LHRH and neuronal markers such as neurofilament (NF) were detected by immunocytochemistry within and/or outside the explant. Three main types of LHRH positive cells are described: (1) neuroblastic LHRH and NF immunoreactive cells with round cell body and very short neurites found mainly within the explant, (2) migrating LHRH bipolar neurones with an fusiform cell body, found outside the explant, (3) network LHRH neuron, bipolar or multipolar with long neurites connecting other LHRH neurons. Cell morphology was very similar to that which has been described in the adult sheep brain. These results strongly suggest that LHRH neurones in the sheep originate from the olfactory placode. This mode may represent a useful tool to study LHRH neurones directly in the sheep.

Space Weather Activities of IONOLAB Group: TEC Mapping

NASA Astrophysics Data System (ADS)

Arikan, F.; Yilmaz, A.; Arikan, O.; Sayin, I.; Gurun, M.; Akdogan, K. E.; Yildirim, S. A.

2009-04-01

Being a key player in Space Weather, ionospheric variability affects the performance of both communication and navigation systems. To improve the performance of these systems, ionosphere has to be monitored. Total Electron Content (TEC), line integral of the electron density along a ray path, is an important parameter to investigate the ionospheric variability. A cost-effective way of obtaining TEC is by using dual-frequency GPS receivers. Since these measurements are sparse in space, accurate and robust interpolation techniques are needed to interpolate (or map) the TEC distribution for a given region in space. However, the TEC data derived from GPS measurements contain measurement noise, model and computational errors. Thus, it is necessary to analyze the interpolation performance of the techniques on synthetic data sets that can represent various ionospheric states. By this way, interpolation performance of the techniques can be compared over many parameters that can be controlled to represent the desired ionospheric states. In this study, Multiquadrics, Inverse Distance Weighting (IDW), Cubic Splines, Ordinary and Universal Kriging, Random Field Priors (RFP), Multi-Layer Perceptron Neural Network (MLP-NN), and Radial Basis Function Neural Network (RBF-NN) are employed as the spatial interpolation algorithms. These mapping techniques are initially tried on synthetic TEC surfaces for parameter and coefficient optimization and determination of error bounds. Interpolation performance of these methods are compared on synthetic TEC surfaces over the parameters of sampling pattern, number of samples, the variability of the surface and the trend type in the TEC surfaces. By examining the performance of the interpolation methods, it is observed that both Kriging, RFP and NN have important advantages and possible disadvantages depending on the given constraints. It is also observed that the determining parameter in the error performance is the trend in the Ionosphere. Optimization of the algorithms in terms of their performance parameters (like the choice of the semivariogram function for Kriging algorithms and the hidden layer and neuron numbers for MLP-NN) mostly depend on the behavior of the ionosphere at that given time instant for the desired region. The sampling pattern and number of samples are the other important parameters that may contribute to the higher errors in reconstruction. For example, for all of the above listed algorithms, hexagonal regular sampling of the ionosphere provides the lowest reconstruction error and the performance significantly degrades as the samples in the region become sparse and clustered. The optimized models and coefficients are applied to regional GPS-TEC mapping using the IONOLAB-TEC data (www.ionolab.org). Both Kriging combined with Kalman Filter and dynamic modeling of NN are also implemented as first trials of TEC and space weather predictions.

Prospect theory does not describe the feedback-related negativity value function.

PubMed

Sambrook, Thomas D; Roser, Matthew; Goslin, Jeremy

2012-12-01

Humans handle uncertainty poorly. Prospect theory accounts for this with a value function in which possible losses are overweighted compared to possible gains, and the marginal utility of rewards decreases with size. fMRI studies have explored the neural basis of this value function. A separate body of research claims that prediction errors are calculated by midbrain dopamine neurons. We investigated whether the prospect theoretic effects shown in behavioral and fMRI studies were present in midbrain prediction error coding by using the feedback-related negativity, an ERP component believed to reflect midbrain prediction errors. Participants' stated satisfaction with outcomes followed prospect theory but their feedback-related negativity did not, instead showing no effect of marginal utility and greater sensitivity to potential gains than losses. Copyright © 2012 Society for Psychophysiological Research.

Detecting wrong notes in advance: neuronal correlates of error monitoring in pianists.

PubMed

Ruiz, María Herrojo; Jabusch, Hans-Christian; Altenmüller, Eckart

2009-11-01

Music performance is an extremely rapid process with low incidence of errors even at the fast rates of production required. This is possible only due to the fast functioning of the self-monitoring system. Surprisingly, no specific data about error monitoring have been published in the music domain. Consequently, the present study investigated the electrophysiological correlates of executive control mechanisms, in particular error detection, during piano performance. Our target was to extend the previous research efforts on understanding of the human action-monitoring system by selecting a highly skilled multimodal task. Pianists had to retrieve memorized music pieces at a fast tempo in the presence or absence of auditory feedback. Our main interest was to study the interplay between auditory and sensorimotor information in the processes triggered by an erroneous action, considering only wrong pitches as errors. We found that around 70 ms prior to errors a negative component is elicited in the event-related potentials and is generated by the anterior cingulate cortex. Interestingly, this component was independent of the auditory feedback. However, the auditory information did modulate the processing of the errors after their execution, as reflected in a larger error positivity (Pe). Our data are interpreted within the context of feedforward models and the auditory-motor coupling.

A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.

PubMed

Pillow, Jonathan W; Shlens, Jonathon; Chichilnisky, E J; Simoncelli, Eero P

2013-01-01

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings

PubMed Central

Chichilnisky, E. J.; Simoncelli, Eero P.

2013-01-01

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call “binary pursuit”. The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth. PMID:23671583

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.

Differential distribution of neurons in the gyral white matter of the human cerebral cortex.

PubMed

García-Marín, V; Blazquez-Llorca, L; Rodriguez, J R; Gonzalez-Soriano, J; DeFelipe, J

2010-12-01

The neurons in the cortical white matter (WM neurons) originate from the first set of postmitotic neurons that migrates from the ventricular zone. In particular, they arise in the subplate that contains the earliest cells generated in the telencephalon, prior to the appearance of neurons in gray matter cortical layers. These cortical WM neurons are very numerous during development, when they are thought to participate in transient synaptic networks, although many of these cells later die, and relatively few cells survive as WM neurons in the adult. We used light and electron microscopy to analyze the distribution and density of WM neurons in various areas of the adult human cerebral cortex. Furthermore, we examined the perisomatic innervation of these neurons and estimated the density of synapses in the white matter. Finally, we examined the distribution and neurochemical nature of interneurons that putatively innervate the somata of WM neurons. From the data obtained, we can draw three main conclusions: first, the density of WM neurons varies depending on the cortical areas; second, calretinin-immunoreactive neurons represent the major subpopulation of GABAergic WM neurons; and, third, the somata of WM neurons are surrounded by both glutamatergic and GABAergic axon terminals, although only symmetric axosomatic synapses were found. By contrast, both symmetric and asymmetric axodendritic synapses were observed in the neuropil. We discuss the possible functional implications of these findings in terms of cortical circuits. © 2010 Wiley-Liss, Inc.

Dual Leucine Zipper Kinase Inhibitors for the Treatment of Neurodegeneration.

PubMed

Siu, Michael; Sengupta Ghosh, Arundhati; Lewcock, Joseph W

2018-06-04

Dual leucine zipper kinase (DLK, MAP3K12) is an essential driver of the neuronal stress response that regulates neurodegeneration in models of acute neuronal injury and chronic neurodegenerative diseases such as Alzheimer's, Parkinson's, and ALS. In this review, we provide an overview of DLK signaling mechanisms and describe selected small molecules that have been utilized to inhibit DLK kinase activity in vivo. These compounds represent valuable tools for understanding the role of DLK signaling and evaluating the potential for DLK inhibition as a therapeutic strategy to prevent neuronal degeneration.

The mathematical research for the Kuramoto model of the describing neuronal synchrony in the brain

NASA Astrophysics Data System (ADS)

Lin, Chang; Lin, Mai-mai

2009-08-01

The Kuramoto model of the describing neuronal synchrony is mathematically investigated in the brain. A general analytical solutions (the most sententious description) for the Kuramoto model, incorporating the inclusion of a Ki,j (t) term to represent time-varying coupling strengths, have been obtained by using the precise mathematical approach. We derive an exact analytical expression, opening out the connotative and latent linear relation, for the mathematical character of the phase configurations in the Kuramoto model of the describing neuronal synchrony in the brain.

Learning Recruits Neurons Representing Previously Established Associations in the Corvid Endbrain.

PubMed

Veit, Lena; Pidpruzhnykova, Galyna; Nieder, Andreas

2017-10-01

Crows quickly learn arbitrary associations. As a neuronal correlate of this behavior, single neurons in the corvid endbrain area nidopallium caudolaterale (NCL) change their response properties during association learning. In crows performing a delayed association task that required them to map both familiar and novel sample pictures to the same two choice pictures, NCL neurons established a common, prospective code for associations. Here, we report that neuronal tuning changes during learning were not distributed equally in the recorded population of NCL neurons. Instead, such learning-related changes relied almost exclusively on neurons which were already encoding familiar associations. Only in such neurons did behavioral improvements during learning of novel associations coincide with increasing selectivity over the learning process. The size and direction of selectivity for familiar and newly learned associations were highly correlated. These increases in selectivity for novel associations occurred only late in the delay period. Moreover, NCL neurons discriminated correct from erroneous trial outcome based on feedback signals at the end of the trial, particularly in newly learned associations. Our results indicate that task-relevant changes during association learning are not distributed within the population of corvid NCL neurons but rather are restricted to a specific group of association-selective neurons. Such association neurons in the multimodal cognitive integration area NCL likely play an important role during highly flexible behavior in corvids.

Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task

PubMed Central

Wang, Xiao-Jing

2016-01-01

Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a “Stop” process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception. PMID:27551824

Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task.

PubMed

Lo, Chung-Chuan; Wang, Xiao-Jing

2016-08-01

Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a "Stop" process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception.

Sleep is related to neuron numbers in the ventrolateral preoptic/intermediate nucleus in older adults with and without Alzheimer’s disease

PubMed Central

Lim, Andrew S. P.; Ellison, Brian A.; Wang, Joshua L.; Yu, Lei; Schneider, Julie A.; Buchman, Aron S.; Bennett, David A.

2014-01-01

Fragmented sleep is a common and troubling symptom in ageing and Alzheimer’s disease; however, its neurobiological basis in many patients is unknown. In rodents, lesions of the hypothalamic ventrolateral preoptic nucleus cause fragmented sleep. We previously proposed that the intermediate nucleus in the human hypothalamus, which has a similar location and neurotransmitter profile, is the homologue of the ventrolateral preoptic nucleus, but physiological data in humans were lacking. We hypothesized that if the intermediate nucleus is important for human sleep, then intermediate nucleus cell loss may contribute to fragmentation and loss of sleep in ageing and Alzheimer’s disease. We studied 45 older adults (mean age at death 89.2 years; 71% female; 12 with Alzheimer’s disease) from the Rush Memory and Aging Project, a community-based study of ageing and dementia, who had at least 1 week of wrist actigraphy proximate to death. Upon death a median of 15.5 months later, we used immunohistochemistry and stereology to quantify the number of galanin-immunoreactive intermediate nucleus neurons in each individual, and related this to ante-mortem sleep fragmentation. Individuals with Alzheimer’s disease had fewer galaninergic intermediate nucleus neurons than those without (estimate −2872, standard error = 829, P = 0.001). Individuals with more galanin-immunoreactive intermediate nucleus neurons had less fragmented sleep, after adjusting for age and sex, and this association was strongest in those for whom the lag between actigraphy and death was <1 year (estimate −0.0013, standard error = 0.0005, P = 0.023). This association did not differ between individuals with and without Alzheimer’s disease, and similar associations were not seen for two other cell populations near the intermediate nucleus. These data are consistent with the intermediate nucleus being the human homologue of the ventrolateral preoptic nucleus. Moreover, they demonstrate that a paucity of galanin-immunoreactive intermediate nucleus neurons is accompanied by sleep fragmentation in older adults with and without Alzheimer’s disease. PMID:25142380

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.

Frequent coexistence of Lewy bodies and neurofibrillary tangles in the same neurons of patients with diffuse Lewy body disease.

PubMed

Iseki, E; Marui, W; Kosaka, K; Uéda, K

1999-04-09

We examined the frequency of neurons with coexistent Lewy bodies (LB) and neurofibrillary tangles (NFT) in diffuse Lewy body disease brains, by a double-immunostaining method using MDV2 and Human tau. Double-positive neurons were frequently observed in the limbic areas. These neurons mostly revealed the feature of intermingled MDV2- and Human tau-positive substances. Immunoelectron microscopically, the MDV2-positive components were not in continuity with the MDV2-negative paired helical filaments (PHF). The MDV2-positive LB were surrounded by the small PHF bundles, frequently accompanied by the randomly oriented PHF within LB. In the intermingled neurons, MDV2-positive non-filamentous components without LB were found among the large PHF bundles. These non-filamentous components may represent the early stage of LB formation.

Recording axonal conduction to evaluate the integration of pluripotent cell-derived neurons into a neuronal network.

PubMed

Shimba, Kenta; Sakai, Koji; Takayama, Yuzo; Kotani, Kiyoshi; Jimbo, Yasuhiko

2015-10-01

Stem cell transplantation is a promising therapy to treat neurodegenerative disorders, and a number of in vitro models have been developed for studying interactions between grafted neurons and the host neuronal network to promote drug discovery. However, methods capable of evaluating the process by which stem cells integrate into the host neuronal network are lacking. In this study, we applied an axonal conduction-based analysis to a co-culture study of primary and differentiated neurons. Mouse cortical neurons and neuronal cells differentiated from P19 embryonal carcinoma cells, a model for early neural differentiation of pluripotent stem cells, were co-cultured in a microfabricated device. The somata of these cells were separated by the co-culture device, but their axons were able to elongate through microtunnels and then form synaptic contacts. Propagating action potentials were recorded from these axons by microelectrodes embedded at the bottom of the microtunnels and sorted into clusters representing individual axons. While the number of axons of cortical neurons increased until 14 days in vitro and then decreased, those of P19 neurons increased throughout the culture period. Network burst analysis showed that P19 neurons participated in approximately 80% of the bursting activity after 14 days in vitro. Interestingly, the axonal conduction delay of P19 neurons was significantly greater than that of cortical neurons, suggesting that there are some physiological differences in their axons. These results suggest that our method is feasible to evaluate the process by which stem cell-derived neurons integrate into a host neuronal network.

Detecting higher-order interactions among the spiking events in a group of neurons.

PubMed

Martignon, L; Von Hasseln, H; Grün, S; Aertsen, A; Palm, G

1995-06-01

We propose a formal framework for the description of interactions among groups of neurons. This framework is not restricted to the common case of pair interactions, but also incorporates higher-order interactions, which cannot be reduced to lower-order ones. We derive quantitative measures to detect the presence of such interactions in experimental data, by statistical analysis of the frequency distribution of higher-order correlations in multiple neuron spike train data. Our first step is to represent a frequency distribution as a Markov field on the minimal graph it induces. We then show the invariance of this graph with regard to changes of state. Clearly, only linear Markov fields can be adequately represented by graphs. Higher-order interdependencies, which are reflected by the energy expansion of the distribution, require more complex graphical schemes, like constellations or assembly diagrams, which we introduce and discuss. The coefficients of the energy expansion not only point to the interactions among neurons but are also a measure of their strength. We investigate the statistical meaning of detected interactions in an information theoretic sense and propose minimum relative entropy approximations as null hypotheses for significance tests. We demonstrate the various steps of our method in the situation of an empirical frequency distribution on six neurons, extracted from data on simultaneous multineuron recordings from the frontal cortex of a behaving monkey and close with a brief outlook on future work.

Tectonigral Projections in the Primate: A Pathway for Pre-Attentive Sensory Input to Midbrain Dopaminergic Neurons

PubMed Central

May, Paul J.; McHaffie, John G.; Stanford, Terrence R.; Jiang, Huai; Costello, M. Gabriela; Coizet, Veronique; Hayes, Lauren M.; Haber, Suzanne N.; Redgrave, Peter

2010-01-01

Much of the evidence linking the short-latency phasic signaling of midbrain dopaminergic neurons with reward-prediction errors used in learning and habit formation comes from recording the visual responses of monkey dopaminergic neurons. However, the information encoded by dopaminergic neuron activity is constrained by the qualities of the afferent visual signals made available to these cells. Recent evidence from rats and cats indicates the primary source of this visual input originates subcortically, via a direct tectonigral projection. The present anatomical study sought to establish whether a direct tectonigral projection is a significant feature of the primate brain. Injections of anterograde tracers into the superior colliculus of macaque monkeys labelled terminal arbors throughout the substantia nigra, with the densest terminations in the dorsal tier. Labelled boutons were found in close association (possibly indicative of synaptic contact) with ventral midbrain neurons staining positively for the dopaminergic marker tyrosine hydroxylase. Injections of retrograde tracer confined to the macaque substantia nigra retrogradely labelled small to medium sized neurons in the intermediate and deep layers of the superior colliculus. Together, these data indicate that a direct tectonigral projection is also a feature of the monkey brain, and therefore likely to have been conserved throughout mammalian evolution. Insofar as the superior colliculus is configured to detect unpredicted, biologically salient, sensory events, it may be safer to regard the phasic responses of midbrain dopaminergic neurons as ‘sensory prediction errors’ rather than ‘reward prediction errors’, in which case, dopamine-based theories of reinforcement learning will require revision. PMID:19175405

In search for a gold-standard procedure to count motor neurons in the spinal cord.

PubMed

Ferrucci, Michela; Lazzeri, Gloria; Flaibani, Marina; Biagioni, Francesca; Cantini, Federica; Madonna, Michele; Bucci, Domenico; Limanaqi, Fiona; Soldani, Paola; Fornai, Francesco

2018-03-14

Counting motor neurons within the spinal cord and brainstem represents a seminal step to comprehend the anatomy and physiology of the final common pathway sourcing from the CNS. Motor neuron loss allows to assess the severity of motor neuron disorders while providing a tool to assess disease modifying effects. Counting motor neurons at first implies gold standard identification methods. In fact, motor neurons may occur within mixed nuclei housing a considerable amount of neurons other than motor neurons. In the present review, we analyse various approaches to count motor neurons emphasizing both the benefits and bias of each protocol. A special emphasis is placed on discussing automated stereology. When automated stereology does not take into account site-specificity and does not distinguish between heterogeneous neuronal populations, it may confound data making such a procedure a sort of "guide for the perplex". Thus, if on the one hand automated stereology improves our ability to quantify neuronal populations, it may also hide false positives/negatives in neuronal counts. For instance, classic staining for antigens such as SMI-32, SMN and ChAT, which are routinely considered to be specific for motor neurons, may also occur in other neuronal types of the spinal cord. Even site specificity within Lamina IX may be misleading due to neuronal populations having a size and shape typical of motor neurons. This is the case of spinal border cells, which often surpass the border of Lamina VII and intermingle with motor neurons of Lamina IX. The present article discusses the need to join automated stereology with a dedicated knowledge of each specific neuroanatomical setting.

Population responses in primary auditory cortex simultaneously represent the temporal envelope and periodicity features in natural speech.

PubMed

Abrams, Daniel A; Nicol, Trent; White-Schwoch, Travis; Zecker, Steven; Kraus, Nina

2017-05-01

Speech perception relies on a listener's ability to simultaneously resolve multiple temporal features in the speech signal. Little is known regarding neural mechanisms that enable the simultaneous coding of concurrent temporal features in speech. Here we show that two categories of temporal features in speech, the low-frequency speech envelope and periodicity cues, are processed by distinct neural mechanisms within the same population of cortical neurons. We measured population activity in primary auditory cortex of anesthetized guinea pig in response to three variants of a naturally produced sentence. Results show that the envelope of population responses closely tracks the speech envelope, and this cortical activity more closely reflects wider bandwidths of the speech envelope compared to narrow bands. Additionally, neuronal populations represent the fundamental frequency of speech robustly with phase-locked responses. Importantly, these two temporal features of speech are simultaneously observed within neuronal ensembles in auditory cortex in response to clear, conversation, and compressed speech exemplars. Results show that auditory cortical neurons are adept at simultaneously resolving multiple temporal features in extended speech sentences using discrete coding mechanisms. Copyright © 2017 Elsevier B.V. All rights reserved.

Cajal and the discovery of a new artistic world: the neuronal forest.

PubMed

DeFelipe, Javier

2013-01-01

The introduction of the staining method of Camillo Golgi in 1873 represented a giant step for neuroscience. Prior to this development, the visualization of neurons with the available histological techniques had been incomplete; it was only feasible to observe the cell body and the proximal portions of the dendrites and axon. However, with the Golgi method it was possible to observe neurons and glia with all their parts (cell body, dendrites, and axon in the case of neurons; cell body and processes in the case of glia). Due to the advantages of this method, all of a sudden it was possible to begin studying one of the great mysteries and critical issues of the organization of the nervous system-the tracing of the connections between neurons. Nevertheless, this method was not fully exploited until Santiago Ramón y Cajal arrived on the scene in 1888. It should be noted that, in Cajal's day, drawing was the most common method of describing microscopic images in the absence of the highly developed microphotography and other imaging techniques commonly available in today's laboratories. As a consequence, most scientific figures presented by the early neuroanatomists were their own drawings, providing an outlet for these scientists to express and develop their artistic talent. In the hands of Cajal, the Golgi method represented not only the principal tool that was to change the course of the history of neuroscience but also the discovery of a new artistic world, the neuronal forest. © 2013 Elsevier B.V. All rights reserved.

The Ste20 Family Kinases MAP4K4, MINK1, and TNIK Converge to Regulate Stress-Induced JNK Signaling in Neurons.

PubMed

Larhammar, Martin; Huntwork-Rodriguez, Sarah; Rudhard, York; Sengupta-Ghosh, Arundhati; Lewcock, Joseph W

2017-11-15

The c-Jun- N -terminal kinase (JNK) signaling pathway regulates nervous system development, axon regeneration, and neuronal degeneration after acute injury or in chronic neurodegenerative disease. Dual leucine zipper kinase (DLK) is required for stress-induced JNK signaling in neurons, yet the factors that initiate DLK/JNK pathway activity remain poorly defined. In the present study, we identify the Ste20 kinases MAP4K4, misshapen-like kinase 1 (MINK1 or MAP4K6) and TNIK Traf2- and Nck-interacting kinase (TNIK or MAP4K7), as upstream regulators of DLK/JNK signaling in neurons. Using a trophic factor withdrawal-based model of neurodegeneration in both male and female embryonic mouse dorsal root ganglion neurons, we show that MAP4K4, MINK1, and TNIK act redundantly to regulate DLK activation and downstream JNK-dependent phosphorylation of c-Jun in response to stress. Targeting MAP4K4, MINK1, and TNIK, but not any of these kinases individually, is sufficient to protect neurons potently from degeneration. Pharmacological inhibition of MAP4Ks blocks stabilization and phosphorylation of DLK within axons and subsequent retrograde translocation of the JNK signaling complex to the nucleus. These results position MAP4Ks as important regulators of the DLK/JNK signaling pathway. SIGNIFICANCE STATEMENT Neuronal degeneration occurs in disparate circumstances: during development to refine neuronal connections, after injury to clear damaged neurons, or pathologically during disease. The dual leucine zipper kinase (DLK)/c-Jun- N -terminal kinase (JNK) pathway represents a conserved regulator of neuronal injury signaling that drives both neurodegeneration and axon regeneration, yet little is known about the factors that initiate DLK activity. Here, we uncover a novel role for a subfamily of MAP4 kinases consisting of MAP4K4, Traf2- and Nck-interacting kinase (TNIK or MAP4K7), and misshapen-like kinase 1 (MINK1 or MAP4K6) in regulating DLK/JNK signaling in neurons. Inhibition of these MAP4Ks blocks stress-induced retrograde JNK signaling and protects from neurodegeneration, suggesting that these kinases may represent attractive therapeutic targets. Copyright © 2017 the authors 0270-6474/17/3711074-11$15.00/0.

The error in total error reduction.

PubMed

Witnauer, James E; Urcelay, Gonzalo P; Miller, Ralph R

2014-02-01

Most models of human and animal learning assume that learning is proportional to the discrepancy between a delivered outcome and the outcome predicted by all cues present during that trial (i.e., total error across a stimulus compound). This total error reduction (TER) view has been implemented in connectionist and artificial neural network models to describe the conditions under which weights between units change. Electrophysiological work has revealed that the activity of dopamine neurons is correlated with the total error signal in models of reward learning. Similar neural mechanisms presumably support fear conditioning, human contingency learning, and other types of learning. Using a computational modeling approach, we compared several TER models of associative learning to an alternative model that rejects the TER assumption in favor of local error reduction (LER), which assumes that learning about each cue is proportional to the discrepancy between the delivered outcome and the outcome predicted by that specific cue on that trial. The LER model provided a better fit to the reviewed data than the TER models. Given the superiority of the LER model with the present data sets, acceptance of TER should be tempered. Copyright © 2013 Elsevier Inc. All rights reserved.

Electronic implementation of associative memory based on neural network models

NASA Technical Reports Server (NTRS)

Moopenn, A.; Lambe, John; Thakoor, A. P.

1987-01-01

An electronic embodiment of a neural network based associative memory in the form of a binary connection matrix is described. The nature of false memory errors, their effect on the information storage capacity of binary connection matrix memories, and a novel technique to eliminate such errors with the help of asymmetrical extra connections are discussed. The stability of the matrix memory system incorporating a unique local inhibition scheme is analyzed in terms of local minimization of an energy function. The memory's stability, dynamic behavior, and recall capability are investigated using a 32-'neuron' electronic neural network memory with a 1024-programmable binary connection matrix.

Phasic dopamine signals: from subjective reward value to formal economic utility

PubMed Central

Schultz, Wolfram; Carelli, Regina M; Wightman, R Mark

2015-01-01

Although rewards are physical stimuli and objects, their value for survival and reproduction is subjective. The phasic, neurophysiological and voltammetric dopamine reward prediction error response signals subjective reward value. The signal incorporates crucial reward aspects such as amount, probability, type, risk, delay and effort. Differences of dopamine release dynamics with temporal delay and effort in rodents may derive from methodological issues and require further study. Recent designs using concepts and behavioral tools from experimental economics allow to formally characterize the subjective value signal as economic utility and thus to establish a neuronal value function. With these properties, the dopamine response constitutes a utility prediction error signal. PMID:26719853

Ctip2-, Satb2-, Prox1-, and GAD65-Expressing Neurons in Rat Cultures: Preponderance of Single- and Double-Positive Cells, and Cell Type-Specific Expression of Neuron-Specific Gene Family Members, Nsg-1 (NEEP21) and Nsg-2 (P19).

PubMed

Digilio, Laura; Yap, Chan Choo; Winckler, Bettina

2015-01-01

The brain consists of many distinct neuronal cell types, but which cell types are present in widely used primary cultures of embryonic rodent brain is often not known. We characterized how abundantly four cell type markers (Ctip2, Satb2, Prox1, GAD65) were represented in cultured rat neurons, how easily neurons expressing different markers can be transfected with commonly used plasmids, and whether neuronal-enriched endosomal proteins Nsg-1 (NEEP21) and Nsg-2 (P19) are ubiquitously expressed in all types of cultured neurons. We found that cultured neurons stably maintain cell type identities that are reflective of cell types in vivo. This includes neurons maintaining simultaneous expression of two transcription factors, such as Ctip2+/Satb2+ or Prox1+/Ctip2+ double-positive cells, which have also been described in vivo. Secondly, we established the superior efficiency of CAG promoters for both Lipofectamine-mediated transfection as well as for electroporation. Thirdly, we discovered that Nsg-1 and Nsg-2 were not expressed equally in all neurons: whereas high levels of both Nsg-1 and Nsg-2 were found in Satb2-, Ctip2-, and GAD65-positive neurons, Prox1-positive neurons in hippocampal cultures expressed low levels of both. Our findings thus highlight the importance of identifying neuronal cell types for doing cell biology in cultured neurons: Keeping track of neuronal cell type might uncover effects in assays that might otherwise be masked by the mixture of responsive and non-responsive neurons in the dish.

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface

PubMed Central

Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

2014-01-01

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them. PMID:24567704

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.

PubMed

Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

2014-01-01

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.

Distinct Neural Properties in the Low-Frequency Region of the Chicken Cochlear Nucleus Magnocellularis

PubMed Central

2017-01-01

Abstract Topography in the avian cochlear nucleus magnocellularis (NM) is represented as gradually increasing characteristic frequency (CF) along the caudolateral-to-rostromedial axis. In this study, we characterized the organization and cell biophysics of the caudolateral NM (NMc) in chickens (Gallus gallus). Examination of cellular and dendritic architecture first revealed that NMc contains small neurons and extensive dendritic processes, in contrast to adendritic, large neurons located more rostromedially. Individual dye-filling study further demonstrated that NMc is divided into two subregions, with NMc2 neurons having larger and more complex dendritic fields than NMc1. Axonal tract tracing studies confirmed that NMc1 and NMc2 neurons receive afferent inputs from the auditory nerve and the superior olivary nucleus, similar to the adendritic NM. However, the auditory axons synapse with NMc neurons via small bouton-like terminals, unlike the large end bulb synapses on adendritic NM neurons. Immunocytochemistry demonstrated that most NMc2 neurons express cholecystokinin but not calretinin, distinct from NMc1 and adendritic NM neurons that are cholecystokinin negative and mostly calretinin positive. Finally, whole-cell current clamp recordings revealed that NMc neurons require significantly lower threshold current for action potential generation than adendritic NM neurons. Moreover, in contrast to adendritic NM neurons that generate a single-onset action potential, NMc neurons generate multiple action potentials to suprathreshold sustained depolarization. Taken together, our data indicate that NMc contains multiple neuron types that are structurally, connectively, molecularly, and physiologically different from traditionally defined NM neurons, emphasizing specialized neural properties for processing low-frequency sounds. PMID:28413822

A meta-cognitive learning algorithm for a Fully Complex-valued Relaxation Network.

PubMed

Savitha, R; Suresh, S; Sundararajan, N

2012-08-01

This paper presents a meta-cognitive learning algorithm for a single hidden layer complex-valued neural network called "Meta-cognitive Fully Complex-valued Relaxation Network (McFCRN)". McFCRN has two components: a cognitive component and a meta-cognitive component. A Fully Complex-valued Relaxation Network (FCRN) with a fully complex-valued Gaussian like activation function (sech) in the hidden layer and an exponential activation function in the output layer forms the cognitive component. The meta-cognitive component contains a self-regulatory learning mechanism which controls the learning ability of FCRN by deciding what-to-learn, when-to-learn and how-to-learn from a sequence of training data. The input parameters of cognitive components are chosen randomly and the output parameters are estimated by minimizing a logarithmic error function. The problem of explicit minimization of magnitude and phase errors in the logarithmic error function is converted to system of linear equations and output parameters of FCRN are computed analytically. McFCRN starts with zero hidden neuron and builds the number of neurons required to approximate the target function. The meta-cognitive component selects the best learning strategy for FCRN to acquire the knowledge from training data and also adapts the learning strategies to implement best human learning components. Performance studies on a function approximation and real-valued classification problems show that proposed McFCRN performs better than the existing results reported in the literature. Copyright © 2012 Elsevier Ltd. All rights reserved.

Macroscopic self-oscillations and aging transition in a network of synaptically coupled quadratic integrate-and-fire neurons.

PubMed

Ratas, Irmantas; Pyragas, Kestutis

2016-09-01

We analyze the dynamics of a large network of coupled quadratic integrate-and-fire neurons, which represent the canonical model for class I neurons near the spiking threshold. The network is heterogeneous in that it includes both inherently spiking and excitable neurons. The coupling is global via synapses that take into account the finite width of synaptic pulses. Using a recently developed reduction method based on the Lorentzian ansatz, we derive a closed system of equations for the neuron's firing rate and the mean membrane potential, which are exact in the infinite-size limit. The bifurcation analysis of the reduced equations reveals a rich scenario of asymptotic behavior, the most interesting of which is the macroscopic limit-cycle oscillations. It is shown that the finite width of synaptic pulses is a necessary condition for the existence of such oscillations. The robustness of the oscillations against aging damage, which transforms spiking neurons into nonspiking neurons, is analyzed. The validity of the reduced equations is confirmed by comparing their solutions with the solutions of microscopic equations for the finite-size networks.

Induction of superficial cortical layer neurons from mouse embryonic stem cells by valproic acid.

PubMed

Juliandi, Berry; Abematsu, Masahiko; Sanosaka, Tsukasa; Tsujimura, Keita; Smith, Austin; Nakashima, Kinichi

2012-01-01

Within the developing mammalian cortex, neural progenitors first generate deep-layer neurons and subsequently more superficial-layer neurons, in an inside-out manner. It has been reported recently that mouse embryonic stem cells (mESCs) can, to some extent, recapitulate cortical development in vitro, with the sequential appearance of neurogenesis markers resembling that in the developing cortex. However, mESCs can only recapitulate early corticogenesis; superficial-layer neurons, which are normally produced in later developmental periods in vivo, are under-represented. This failure of mESCs to reproduce later corticogenesis in vitro implies the existence of crucial factor(s) that are absent or uninduced in existing culture systems. Here we show that mESCs can give rise to superficial-layer neurons efficiently when treated with valproic acid (VPA), a histone deacetylase inhibitor. VPA treatment increased the production of Cux1-positive superficial-layer neurons, and decreased that of Ctip2-positive deep-layer neurons. These results shed new light on the mechanisms of later corticogenesis. Copyright © 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Precise auditory-vocal mirroring in neurons for learned vocal communication.

PubMed

Prather, J F; Peters, S; Nowicki, S; Mooney, R

2008-01-17

Brain mechanisms for communication must establish a correspondence between sensory and motor codes used to represent the signal. One idea is that this correspondence is established at the level of single neurons that are active when the individual performs a particular gesture or observes a similar gesture performed by another individual. Although neurons that display a precise auditory-vocal correspondence could facilitate vocal communication, they have yet to be identified. Here we report that a certain class of neurons in the swamp sparrow forebrain displays a precise auditory-vocal correspondence. We show that these neurons respond in a temporally precise fashion to auditory presentation of certain note sequences in this songbird's repertoire and to similar note sequences in other birds' songs. These neurons display nearly identical patterns of activity when the bird sings the same sequence, and disrupting auditory feedback does not alter this singing-related activity, indicating it is motor in nature. Furthermore, these neurons innervate striatal structures important for song learning, raising the possibility that singing-related activity in these cells is compared to auditory feedback to guide vocal learning.

Conditional expression of Pomc in the Lepr-positive subpopulation of POMC neurons is sufficient for normal energy homeostasis and metabolism.

PubMed

Lam, Daniel D; Attard, Courtney A; Mercer, Aaron J; Myers, Martin G; Rubinstein, Marcelo; Low, Malcolm J

2015-04-01

Peptides derived from the proopiomelanocortin (POMC) precursor are critical for the normal regulation of many physiological parameters, and POMC deficiency results in severe obesity and metabolic dysfunction. Conversely, augmentation of central nervous system melanocortin function is a promising therapeutic avenue for obesity and diabetes but is confounded by detrimental cardiovascular effects including hypertension. Because the hypothalamic population of POMC-expressing neurons is neurochemically and neuroanatomically heterogeneous, there is interest in the possible dissociation of functionally distinct POMC neuron subpopulations. We used a Cre recombinase-dependent and hypothalamus-specific reactivatable PomcNEO allele to restrict Pomc expression to hypothalamic neurons expressing leptin receptor (Lepr) in mice. In contrast to mice with total hypothalamic Pomc deficiency, which are severely obese, mice with Lepr-restricted Pomc expression displayed fully normal body weight, food consumption, glucose homeostasis, and locomotor activity. Thus, Lepr+ POMC neurons, which constitute approximately two-thirds of the total POMC neuron population, are sufficient for normal regulation of these parameters. This functional dissociation approach represents a promising avenue for isolating therapeutically relevant POMC neuron subpopulations.

Control of proliferation rate of N27 dopaminergic neurons using Transcranial Magnetic Stimulation orientation

NASA Astrophysics Data System (ADS)

Meng, Yiwen; Hadimani, Ravi; Anantharam, Vellareddy; Kanthasamy, Anumantha; Jiles, David

2015-03-01

Transcranial magnetic stimulation (TMS) has been used to investigate possible treatments for a variety of neurological disorders. However, the effect that magnetic fields have on neurons has not been well documented in the literature. We have investigated the effect of different orientation of magnetic field generated by TMS coils with a monophasic stimulator on the proliferation rate of N27 neuronal cells cultured in flasks and multi-well plates. The proliferation rate of neurons would increase by exposed horizontally adherent N27 cells to a magnetic field pointing upward through the neuronal proliferation layer compared with the control group. On the other hand, proliferation rate would decrease in cells exposed to a magnetic field pointing downward through the neuronal growth layer compared with the control group. We confirmed results obtained from the Trypan-blue and automatic cell counting methods with those from the CyQuant and MTS cell viability assays. Our findings could have important implications for the preclinical development of TMS treatments of neurological disorders and represents a new method to control the proliferation rate of neuronal cells.

Pure state consciousness and its local reduction to neuronal space

NASA Astrophysics Data System (ADS)

Duggins, A. J.

2013-01-01

The single neuronal state can be represented as a vector in a complex space, spanned by an orthonormal basis of integer spike counts. In this model a scalar element of experience is associated with the instantaneous firing rate of a single sensory neuron over repeated stimulus presentations. Here the model is extended to composite neural systems that are tensor products of single neuronal vector spaces. Depiction of the mental state as a vector on this tensor product space is intended to capture the unity of consciousness. The density operator is introduced as its local reduction to the single neuron level, from which the firing rate can again be derived as the objective correlate of a subjective element. However, the relational structure of perceptual experience only emerges when the non-local mental state is considered. A metric of phenomenal proximity between neuronal elements of experience is proposed, based on the cross-correlation function of neurophysiology, but constrained by the association of theoretical extremes of correlation/anticorrelation in inseparable 2-neuron states with identical and opponent elements respectively.

Hopf bifurcation of an (n + 1) -neuron bidirectional associative memory neural network model with delays.

PubMed

Xiao, Min; Zheng, Wei Xing; Cao, Jinde

2013-01-01

Recent studies on Hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two, three, four, five, or six neurons. It is well known that neural networks are complex and large-scale nonlinear dynamical systems, so the dynamics of the delayed neural networks are very rich and complicated. Although discussing the dynamics of networks with a few neurons may help us to understand large-scale networks, there are inevitably some complicated problems that may be overlooked if simplified networks are carried over to large-scale networks. In this paper, a general delayed bidirectional associative memory neural network model with n + 1 neurons is considered. By analyzing the associated characteristic equation, the local stability of the trivial steady state is examined, and then the existence of the Hopf bifurcation at the trivial steady state is established. By applying the normal form theory and the center manifold reduction, explicit formulae are derived to determine the direction and stability of the bifurcating periodic solution. Furthermore, the paper highlights situations where the Hopf bifurcations are particularly critical, in the sense that the amplitude and the period of oscillations are very sensitive to errors due to tolerances in the implementation of neuron interconnections. It is shown that the sensitivity is crucially dependent on the delay and also significantly influenced by the feature of the number of neurons. Numerical simulations are carried out to illustrate the main results.

Spatiotemporal encoding of search strategies by prefrontal neurons.

PubMed

Chiang, Feng-Kuei; Wallis, Joni D

2018-05-08

Working memory is capacity-limited. In everyday life we rarely notice this limitation, in part because we develop behavioral strategies that help mitigate the capacity limitation. How behavioral strategies are mediated at the neural level is unclear, but a likely locus is lateral prefrontal cortex (LPFC). Neurons in LPFC play a prominent role in working memory and have been shown to encode behavioral strategies. To examine the role of LPFC in overcoming working-memory limitations, we recorded the activity of LPFC neurons in animals trained to perform a serial self-ordered search task. This task measured the ability to prospectively plan the selection of unchosen spatial search targets while retrospectively tracking which targets were previously visited. We found that individual LPFC neurons encoded the spatial location of the current search target but also encoded the spatial location of targets up to several steps away in the search sequence. Neurons were more likely to encode prospective than retrospective targets. When subjects used a behavioral strategy of stereotyped target selection, mitigating the working-memory requirements of the task, not only did the number of selection errors decrease but there was a significant reduction in the strength of spatial encoding in LFPC. These results show that LPFC neurons have spatiotemporal mnemonic fields, in that their firing rates are modulated both by the spatial location of future selection behaviors and the temporal organization of that behavior. Furthermore, the strength of this tuning can be dynamically modulated by the demands of the task.

Exact subthreshold integration with continuous spike times in discrete-time neural network simulations.

PubMed

Morrison, Abigail; Straube, Sirko; Plesser, Hans Ekkehard; Diesmann, Markus

2007-01-01

Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.

Selective neuronal lapses precede human cognitive lapses following sleep deprivation.

PubMed

Nir, Yuval; Andrillon, Thomas; Marmelshtein, Amit; Suthana, Nanthia; Cirelli, Chiara; Tononi, Giulio; Fried, Itzhak

2017-12-01

Sleep deprivation is a major source of morbidity with widespread health effects, including increased risk of hypertension, diabetes, obesity, heart attack, and stroke. Moreover, sleep deprivation brings about vehicle accidents and medical errors and is therefore an urgent topic of investigation. During sleep deprivation, homeostatic and circadian processes interact to build up sleep pressure, which results in slow behavioral performance (cognitive lapses) typically attributed to attentional thalamic and frontoparietal circuits, but the underlying mechanisms remain unclear. Recently, through study of electroencephalograms (EEGs) in humans and local field potentials (LFPs) in nonhuman primates and rodents it was found that, during sleep deprivation, regional 'sleep-like' slow and theta (slow/theta) waves co-occur with impaired behavioral performance during wakefulness. Here we used intracranial electrodes to record single-neuron activities and LFPs in human neurosurgical patients performing a face/nonface categorization psychomotor vigilance task (PVT) over multiple experimental sessions, including a session after full-night sleep deprivation. We find that, just before cognitive lapses, the selective spiking responses of individual neurons in the medial temporal lobe (MTL) are attenuated, delayed, and lengthened. These 'neuronal lapses' are evident on a trial-by-trial basis when comparing the slowest behavioral PVT reaction times to the fastest. Furthermore, during cognitive lapses, LFPs exhibit a relative local increase in slow/theta activity that is correlated with degraded single-neuron responses and with baseline theta activity. Our results show that cognitive lapses involve local state-dependent changes in neuronal activity already present in the MTL.

PERSPECTIVE: Electrical activity enhances neuronal survival and regeneration

NASA Astrophysics Data System (ADS)

Corredor, Raul G.; Goldberg, Jeffrey L.

2009-10-01

The failure of regeneration in the central nervous system (CNS) remains an enormous scientific and clinical challenge. After injury or in degenerative diseases, neurons in the adult mammalian CNS fail to regrow their axons and reconnect with their normal targets, and furthermore the neurons frequently die and are not normally replaced. While significant progress has been made in understanding the molecular basis for this lack of regenerative ability, a second approach has gained momentum: replacing lost neurons or lost connections with artificial electrical circuits that interface with the nervous system. In the visual system, gene therapy-based 'optogenetics' prostheses represent a competing technology. Now, the two approaches are converging, as recent data suggest that electrical activity itself, via the molecular signaling pathways such activity stimulates, is sufficient to induce neuronal survival and regeneration, particularly in retinal ganglion cells. Here, we review these data, discuss the effects of electrical activity on neurons' molecular signaling pathways and propose specific mechanisms by which exogenous electrical activity may be acting to enhance survival and regeneration.

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.

Dissecting neural pathways for forgetting in Drosophila olfactory aversive memory

PubMed Central

Shuai, Yichun; Hirokawa, Areekul; Ai, Yulian; Zhang, Min; Li, Wanhe; Zhong, Yi

2015-01-01

Recent studies have identified molecular pathways driving forgetting and supported the notion that forgetting is a biologically active process. The circuit mechanisms of forgetting, however, remain largely unknown. Here we report two sets of Drosophila neurons that account for the rapid forgetting of early olfactory aversive memory. We show that inactivating these neurons inhibits memory decay without altering learning, whereas activating them promotes forgetting. These neurons, including a cluster of dopaminergic neurons (PAM-β′1) and a pair of glutamatergic neurons (MBON-γ4>γ1γ2), terminate in distinct subdomains in the mushroom body and represent parallel neural pathways for regulating forgetting. Interestingly, although activity of these neurons is required for memory decay over time, they are not required for acute forgetting during reversal learning. Our results thus not only establish the presence of multiple neural pathways for forgetting in Drosophila but also suggest the existence of diverse circuit mechanisms of forgetting in different contexts. PMID:26627257

Texture coarseness responsive neurons and their mapping in layer 2–3 of the rat barrel cortex in vivo

PubMed Central

Garion, Liora; Dubin, Uri; Rubin, Yoav; Khateb, Mohamed; Schiller, Yitzhak; Azouz, Rony; Schiller, Jackie

2014-01-01

Texture discrimination is a fundamental function of somatosensory systems, yet the manner by which texture is coded and spatially represented in the barrel cortex are largely unknown. Using in vivo two-photon calcium imaging in the rat barrel cortex during artificial whisking against different surface coarseness or controlled passive whisker vibrations simulating different coarseness, we show that layer 2–3 neurons within barrel boundaries differentially respond to specific texture coarsenesses, while only a minority of neurons responded monotonically with increased or decreased surface coarseness. Neurons with similar preferred texture coarseness were spatially clustered. Multi-contact single unit recordings showed a vertical columnar organization of texture coarseness preference in layer 2–3. These findings indicate that layer 2–3 neurons perform high hierarchical processing of tactile information, with surface coarseness embodied by distinct neuronal subpopulations that are spatially mapped onto the barrel cortex. DOI: http://dx.doi.org/10.7554/eLife.03405.001 PMID:25233151

Simultaneous submicrometric 3D imaging of the micro-vascular network and the neuronal system in a mouse spinal cord

PubMed Central

Fratini, Michela; Bukreeva, Inna; Campi, Gaetano; Brun, Francesco; Tromba, Giuliana; Modregger, Peter; Bucci, Domenico; Battaglia, Giuseppe; Spanò, Raffaele; Mastrogiacomo, Maddalena; Requardt, Herwig; Giove, Federico; Bravin, Alberto; Cedola, Alessia

2015-01-01

Faults in vascular (VN) and neuronal networks of spinal cord are responsible for serious neurodegenerative pathologies. Because of inadequate investigation tools, the lacking knowledge of the complete fine structure of VN and neuronal system represents a crucial problem. Conventional 2D imaging yields incomplete spatial coverage leading to possible data misinterpretation, whereas standard 3D computed tomography imaging achieves insufficient resolution and contrast. We show that X-ray high-resolution phase-contrast tomography allows the simultaneous visualization of three-dimensional VN and neuronal systems of ex-vivo mouse spinal cord at scales spanning from millimeters to hundreds of nanometers, with nor contrast agent nor sectioning and neither destructive sample-preparation. We image both the 3D distribution of micro-capillary network and the micrometric nerve fibers, axon-bundles and neuron soma. Our approach is very suitable for pre-clinical investigation of neurodegenerative pathologies and spinal-cord-injuries, in particular to resolve the entangled relationship between VN and neuronal system. PMID:25686728

The influence of hubs in the structure of a neuronal network during an epileptic seizure

NASA Astrophysics Data System (ADS)

Rodrigues, Abner Cardoso; Cerdeira, Hilda A.; Machado, Birajara Soares

2016-02-01

In this work, we propose changes in the structure of a neuronal network with the intention to provoke strong synchronization to simulate episodes of epileptic seizure. Starting with a network of Izhikevich neurons we slowly increase the number of connections in selected nodes in a controlled way, to produce (or not) hubs. We study how these structures alter the synchronization on the spike firings interval, on individual neurons as well as on mean values, as a function of the concentration of connections for random and non-random (hubs) distribution. We also analyze how the post-ictal signal varies for the different distributions. We conclude that a network with hubs is more appropriate to represent an epileptic state.

Modeling of inter-neuronal coupling medium and its impact on neuronal synchronization

PubMed Central

Iqbal, Muhammad; Hong, Keum-Shik

2017-01-01

In this paper, modeling of the coupling medium between two neurons, the effects of the model parameters on the synchronization of those neurons, and compensation of coupling strength deficiency in synchronization are studied. Our study exploits the inter-neuronal coupling medium and investigates its intrinsic properties in order to get insight into neuronal-information transmittance and, there from, brain-information processing. A novel electrical model of the coupling medium that represents a well-known RLC circuit attributable to the coupling medium’s intrinsic resistive, inductive, and capacitive properties is derived. Surprisingly, the integration of such properties reveals the existence of a natural three-term control strategy, referred to in the literature as the proportional integral derivative (PID) controller, which can be responsible for synchronization between two neurons. Consequently, brain-information processing can rely on a large number of PID controllers based on the coupling medium properties responsible for the coherent behavior of neurons in a neural network. Herein, the effects of the coupling model (or natural PID controller) parameters are studied and, further, a supervisory mechanism is proposed that follows a learning and adaptation policy based on the particle swarm optimization algorithm for compensation of the coupling strength deficiency. PMID:28486505

Chemical Structure and Morphology of Dorsal Root Ganglion Neurons from Naive and Inflamed Mice*

PubMed Central

Barabas, Marie E.; Mattson, Eric C.; Aboualizadeh, Ebrahim; Hirschmugl, Carol J.; Stucky, Cheryl L.

2014-01-01

Fourier transform infrared spectromicroscopy provides label-free imaging to detect the spatial distribution of the characteristic functional groups in proteins, lipids, phosphates, and carbohydrates simultaneously in individual DRG neurons. We have identified ring-shaped distributions of lipid and/or carbohydrate enrichment in subpopulations of neurons which has never before been reported. These distributions are ring-shaped within the cytoplasm and are likely representative of the endoplasmic reticulum. The prevalence of chemical ring subtypes differs between large- and small-diameter neurons. Peripheral inflammation increased the relative lipid content specifically in small-diameter neurons, many of which are nociceptive. Because many small-diameter neurons express an ion channel involved in inflammatory pain, transient receptor potential ankyrin 1 (TRPA1), we asked whether this increase in lipid content occurs in TRPA1-deficient (knock-out) neurons. No statistically significant change in lipid content occurred in TRPA1-deficient neurons, indicating that the inflammation-mediated increase in lipid content is largely dependent on TRPA1. Because TRPA1 is known to mediate mechanical and cold sensitization that accompanies peripheral inflammation, our findings may have important implications for a potential role of lipids in inflammatory pain. PMID:25271163

Astrocyte and Neuronal Plasticity in the Somatosensory System

PubMed Central

Sims, Robert E.; Butcher, John B.; Parri, H. Rheinallt; Glazewski, Stanislaw

2015-01-01

Changing the whisker complement on a rodent's snout can lead to two forms of experience-dependent plasticity (EDP) in the neurons of the barrel cortex, where whiskers are somatotopically represented. One form, termed coding plasticity, concerns changes in synaptic transmission and connectivity between neurons. This is thought to underlie learning and memory processes and so adaptation to a changing environment. The second, called homeostatic plasticity, serves to maintain a restricted dynamic range of neuronal activity thus preventing its saturation or total downregulation. Current explanatory models of cortical EDP are almost exclusively neurocentric. However, in recent years, increasing evidence has emerged on the role of astrocytes in brain function, including plasticity. Indeed, astrocytes appear as necessary partners of neurons at the core of the mechanisms of coding and homeostatic plasticity recorded in neurons. In addition to neuronal plasticity, several different forms of astrocytic plasticity have recently been discovered. They extend from changes in receptor expression and dynamic changes in morphology to alteration in gliotransmitter release. It is however unclear how astrocytic plasticity contributes to the neuronal EDP. Here, we review the known and possible roles for astrocytes in the barrel cortex, including its plasticity. PMID:26345481

Extracellular space preservation aids the connectomic analysis of neural circuits

PubMed Central

Pallotto, Marta; Watkins, Paul V; Fubara, Boma; Singer, Joshua H; Briggman, Kevin L

2015-01-01

Dense connectomic mapping of neuronal circuits is limited by the time and effort required to analyze 3D electron microscopy (EM) datasets. Algorithms designed to automate image segmentation suffer from substantial error rates and require significant manual error correction. Any improvement in segmentation error rates would therefore directly reduce the time required to analyze 3D EM data. We explored preserving extracellular space (ECS) during chemical tissue fixation to improve the ability to segment neurites and to identify synaptic contacts. ECS preserved tissue is easier to segment using machine learning algorithms, leading to significantly reduced error rates. In addition, we observed that electrical synapses are readily identified in ECS preserved tissue. Finally, we determined that antibodies penetrate deep into ECS preserved tissue with only minimal permeabilization, thereby enabling correlated light microscopy (LM) and EM studies. We conclude that preservation of ECS benefits multiple aspects of the connectomic analysis of neural circuits. DOI: http://dx.doi.org/10.7554/eLife.08206.001 PMID:26650352

The orbitofrontal cortex and beyond: from affect to decision-making.

PubMed

Rolls, Edmund T; Grabenhorst, Fabian

2008-11-01

The orbitofrontal cortex represents the reward or affective value of primary reinforcers including taste, touch, texture, and face expression. It learns to associate other stimuli with these to produce representations of the expected reward value for visual, auditory, and abstract stimuli including monetary reward value. The orbitofrontal cortex thus plays a key role in emotion, by representing the goals for action. The learning process is stimulus-reinforcer association learning. Negative reward prediction error neurons are related to this affective learning. Activations in the orbitofrontal cortex correlate with the subjective emotional experience of affective stimuli, and damage to the orbitofrontal cortex impairs emotion-related learning, emotional behaviour, and subjective affective state. With an origin from beyond the orbitofrontal cortex, top-down attention to affect modulates orbitofrontal cortex representations, and attention to intensity modulates representations in earlier cortical areas of the physical properties of stimuli. Top-down word-level cognitive inputs can bias affective representations in the orbitofrontal cortex, providing a mechanism for cognition to influence emotion. Whereas the orbitofrontal cortex provides a representation of reward or affective value on a continuous scale, areas beyond the orbitofrontal cortex such as the medial prefrontal cortex area 10 are involved in binary decision-making when a choice must be made. For this decision-making, the orbitofrontal cortex provides a representation of each specific reward in a common currency.

Weather forecasting based on hybrid neural model

NASA Astrophysics Data System (ADS)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Prediction of Film Cooling Effectiveness on a Gas Turbine Blade Leading Edge Using ANN and CFD

NASA Astrophysics Data System (ADS)

Dávalos, J. O.; García, J. C.; Urquiza, G.; Huicochea, A.; De Santiago, O.

2018-05-01

In this work, the area-averaged film cooling effectiveness (AAFCE) on a gas turbine blade leading edge was predicted by employing an artificial neural network (ANN) using as input variables: hole diameter, injection angle, blowing ratio, hole and columns pitch. The database used to train the network was built using computational fluid dynamics (CFD) based on a two level full factorial design of experiments. The CFD numerical model was validated with an experimental rig, where a first stage blade of a gas turbine was represented by a cylindrical specimen. The ANN architecture was composed of three layers with four neurons in hidden layer and Levenberg-Marquardt was selected as ANN optimization algorithm. The AAFCE was successfully predicted by the ANN with a regression coefficient R2<0.99 and a root mean square error RMSE=0.0038. The ANN weight coefficients were used to estimate the relative importance of the input parameters. Blowing ratio was the most influential parameter with relative importance of 40.36 % followed by hole diameter. Additionally, by using the ANN model, the relationship between input parameters was analyzed.

Neuropeptide Y-immunoreactive neurons in the cerebral cortex of humans and other haplorrhine primates

PubMed Central

Raghanti, Mary Ann; Conley, Tiffini; Sudduth, Jessica; Erwin, Joseph M.; Stimpson, Cheryl D.; Hof, Patrick R.; Sherwood, Chet C.

2012-01-01

We examined the distribution of neurons immunoreactive for neuropeptide Y (NPY) in the posterior part of the superior temporal cortex (Brodmann's area 22 or area Tpt) of humans and nonhuman haplorrhine primates. NPY has been implicated in learning and memory and the density of NPY-expressing cortical neurons and axons is reduced in depression, bipolar disorder, schizophrenia, and Alzheimer's disease. Due to the role that NPY plays in both cognition and neurodegenerative diseases, we tested the hypothesis that the density of cortical and interstitial neurons expressing NPY was increased in humans relative to other primate species. The study sample included great apes (chimpanzee and gorilla), Old World monkeys (pigtailed macaque, moor macaque, and baboon) and New World monkeys (squirrel monkey and capuchin). Stereologic methods were used to estimate the density of NPY-immunoreactive (-ir) neurons in layers I-VI of area Tpt and the subjacent white matter. Adjacent Nissl-stained sections were used to calculate local densities of all neurons. The ratio of NPY-ir neurons to total neurons within area Tpt and the total density of NPY-ir neurons within the white matter were compared among species. Overall, NPY-ir neurons represented only an average of 0.006% of the total neuron population. While there were significant differences among species, phylogenetic trends in NPY-ir neuron distributions were not observed and humans did not differ from other primates. However, variation among species warrants further investigation into the distribution of this neuromodulator system. PMID:23042407

Dendrites of medial olivocochlear neurons in mouse.

PubMed

Brown, M C; Levine, J L

2008-06-12

Stains for acetylcholinesterase (AChE) and retrograde labeling with Fluorogold (FG) were used to study olivocochlear neurons and their dendritic patterns in mice. The two methods gave similar results for location and number of somata. The total number of medial olivocochlear (MOC) neurons in the ventral nucleus of the trapezoid body (VNTB) is about 170 per side. An additional dozen large olivocochlear neurons are located in the dorsal periolivary nucleus (DPO). Dendrites of all of these neurons are long and extend in all directions from the cell bodies, a pattern that contrasts with the sharp frequency tuning of their responses. For VNTB neurons, there were greater numbers of dendrites directed medially than laterally and those directed medially were longer (on average, 25-50% longer). Dendrite extensions were most pronounced for neurons located in the rostral portion of the VNTB. When each dendrite from a single neuron was represented as a vector, and all the vectors summed, the result was also skewed toward the medial direction. DPO neurons, however, had more symmetric dendrites that projected into more dorsal parts of the trapezoid body, suggesting that this small group of olivocochlear neurons has very different physiological properties. Dendrites of both types of neurons were somewhat elongated rostrally, about 20% longer than those directed caudally. These results can be interpreted as extensions of dendrites of olivocochlear neurons toward their synaptic inputs: medially to meet crossing fibers from the cochlear nucleus that are part of the MOC reflex pathway, and rostrally to meet descending inputs from higher centers.

A wire length minimization approach to ocular dominance patterns in mammalian visual cortex

NASA Astrophysics Data System (ADS)

Chklovskii, Dmitri B.; Koulakov, Alexei A.

2000-09-01

The primary visual area (V1) of the mammalian brain is a thin sheet of neurons. Because each neuron is dominated by either right or left eye one can treat V1 as a binary mixture of neurons. The spatial arrangement of neurons dominated by different eyes is known as the ocular dominance (OD) pattern. We propose a theory for OD patterns based on the premise that they are evolutionary adaptations to minimize the length of intra-cortical connections. Thus, the existing OD patterns are obtained by solving a wire length minimization problem. We divide all the neurons into two classes: right- and left-eye dominated. We find that if the number of connections of each neuron with the neurons of the same class differs from that with the other class, the segregation of neurons into monocular regions indeed reduces the wire length. The shape of the regions depends on the relative number of neurons in the two classes. If both classes are equally represented we find that the optimal OD pattern consists of alternating stripes. If one class is less numerous than the other, the optimal OD pattern consists of patches of the underrepresented (ipsilateral) eye dominated neurons surrounded by the neurons of the other class. We predict the transition from stripes to patches when the fraction of neurons dominated by the ipsilateral eye is about 40%. This prediction agrees with the data in macaque and Cebus monkeys. Our theory can be applied to other binary cortical systems.

Can simple rules control development of a pioneer vertebrate neuronal network generating behavior?

PubMed

Roberts, Alan; Conte, Deborah; Hull, Mike; Merrison-Hort, Robert; al Azad, Abul Kalam; Buhl, Edgar; Borisyuk, Roman; Soffe, Stephen R

2014-01-08

How do the pioneer networks in the axial core of the vertebrate nervous system first develop? Fundamental to understanding any full-scale neuronal network is knowledge of the constituent neurons, their properties, synaptic interconnections, and normal activity. Our novel strategy uses basic developmental rules to generate model networks that retain individual neuron and synapse resolution and are capable of reproducing correct, whole animal responses. We apply our developmental strategy to young Xenopus tadpoles, whose brainstem and spinal cord share a core vertebrate plan, but at a tractable complexity. Following detailed anatomical and physiological measurements to complete a descriptive library of each type of spinal neuron, we build models of their axon growth controlled by simple chemical gradients and physical barriers. By adding dendrites and allowing probabilistic formation of synaptic connections, we reconstruct network connectivity among up to 2000 neurons. When the resulting "network" is populated by model neurons and synapses, with properties based on physiology, it can respond to sensory stimulation by mimicking tadpole swimming behavior. This functioning model represents the most complete reconstruction of a vertebrate neuronal network that can reproduce the complex, rhythmic behavior of a whole animal. The findings validate our novel developmental strategy for generating realistic networks with individual neuron- and synapse-level resolution. We use it to demonstrate how early functional neuronal connectivity and behavior may in life result from simple developmental "rules," which lay out a scaffold for the vertebrate CNS without specific neuron-to-neuron recognition.

A latent low-dimensional common input drives a pool of motor neurons: a probabilistic latent state-space model.

PubMed

Feeney, Daniel F; Meyer, François G; Noone, Nicholas; Enoka, Roger M

2017-10-01

Motor neurons appear to be activated with a common input signal that modulates the discharge activity of all neurons in the motor nucleus. It has proven difficult for neurophysiologists to quantify the variability in a common input signal, but characterization of such a signal may improve our understanding of how the activation signal varies across motor tasks. Contemporary methods of quantifying the common input to motor neurons rely on compiling discrete action potentials into continuous time series, assuming the motor pool acts as a linear filter, and requiring signals to be of sufficient duration for frequency analysis. We introduce a space-state model in which the discharge activity of motor neurons is modeled as inhomogeneous Poisson processes and propose a method to quantify an abstract latent trajectory that represents the common input received by motor neurons. The approach also approximates the variation in synaptic noise in the common input signal. The model is validated with four data sets: a simulation of 120 motor units, a pair of integrate-and-fire neurons with a Renshaw cell providing inhibitory feedback, the discharge activity of 10 integrate-and-fire neurons, and the discharge times of concurrently active motor units during an isometric voluntary contraction. The simulations revealed that a latent state-space model is able to quantify the trajectory and variability of the common input signal across all four conditions. When compared with the cumulative spike train method of characterizing common input, the state-space approach was more sensitive to the details of the common input current and was less influenced by the duration of the signal. The state-space approach appears to be capable of detecting rather modest changes in common input signals across conditions. NEW & NOTEWORTHY We propose a state-space model that explicitly delineates a common input signal sent to motor neurons and the physiological noise inherent in synaptic signal transmission. This is the first application of a deterministic state-space model to represent the discharge characteristics of motor units during voluntary contractions. Copyright © 2017 the American Physiological Society.

Topography of sound level representation in the FM sweep selective region of the pallid bat auditory cortex.

PubMed

Measor, Kevin; Yarrow, Stuart; Razak, Khaleel A

2018-05-26

Sound level processing is a fundamental function of the auditory system. To determine how the cortex represents sound level, it is important to quantify how changes in level alter the spatiotemporal structure of cortical ensemble activity. This is particularly true for echolocating bats that have control over, and often rapidly adjust, call level to actively change echo level. To understand how cortical activity may change with sound level, here we mapped response rate and latency changes with sound level in the auditory cortex of the pallid bat. The pallid bat uses a 60-30 kHz downward frequency modulated (FM) sweep for echolocation. Neurons tuned to frequencies between 30 and 70 kHz in the auditory cortex are selective for the properties of FM sweeps used in echolocation forming the FM sweep selective region (FMSR). The FMSR is strongly selective for sound level between 30 and 50 dB SPL. Here we mapped the topography of level selectivity in the FMSR using downward FM sweeps and show that neurons with more monotonic rate level functions are located in caudomedial regions of the FMSR overlapping with high frequency (50-60 kHz) neurons. Non-monotonic neurons dominate the FMSR, and are distributed across the entire region, but there is no evidence for amplitopy. We also examined how first spike latency of FMSR neurons change with sound level. The majority of FMSR neurons exhibit paradoxical latency shift wherein the latency increases with sound level. Moreover, neurons with paradoxical latency shifts are more strongly level selective and are tuned to lower sound level than neurons in which latencies decrease with level. These data indicate a clustered arrangement of neurons according to monotonicity, with no strong evidence for finer scale topography, in the FMSR. The latency analysis suggests mechanisms for strong level selectivity that is based on relative timing of excitatory and inhibitory inputs. Taken together, these data suggest how the spatiotemporal spread of cortical activity may represent sound level. Copyright © 2018. Published by Elsevier B.V.

The Role of Binocular Disparity in Stereoscopic Images of Objects in the Macaque Anterior Intraparietal Area

PubMed Central

Romero, Maria C.; Van Dromme, Ilse C. L.; Janssen, Peter

2013-01-01

Neurons in the macaque Anterior Intraparietal area (AIP) encode depth structure in random-dot stimuli defined by gradients of binocular disparity, but the importance of binocular disparity in real-world objects for AIP neurons is unknown. We investigated the effect of binocular disparity on the responses of AIP neurons to images of real-world objects during passive fixation. We presented stereoscopic images of natural and man-made objects in which the disparity information was congruent or incongruent with disparity gradients present in the real-world objects, and images of the same objects where such gradients were absent. Although more than half of the AIP neurons were significantly affected by binocular disparity, the great majority of AIP neurons remained image selective even in the absence of binocular disparity. AIP neurons tended to prefer stimuli in which the depth information derived from binocular disparity was congruent with the depth information signaled by monocular depth cues, indicating that these monocular depth cues have an influence upon AIP neurons. Finally, in contrast to neurons in the inferior temporal cortex, AIP neurons do not represent images of objects in terms of categories such as animate-inanimate, but utilize representations based upon simple shape features including aspect ratio. PMID:23408970

Activation of oral trigeminal neurons by fatty acids is dependent upon intracellular calcium.

PubMed

Yu, Tian; Shah, Bhavik P; Hansen, Dane R; Park-York, MieJung; Gilbertson, Timothy A

2012-08-01

The chemoreception of dietary fat in the oral cavity has largely been attributed to activation of the somatosensory system that conveys the textural properties of fat. However, the ability of fatty acids, which are believed to represent the proximate stimulus for fat taste, to stimulate rat trigeminal neurons has remained unexplored. Here, we found that several free fatty acids are capable of activating trigeminal neurons with different kinetics. Further, a polyunsaturated fatty acid, linoleic acid (LA), activates trigeminal neurons by increasing intracellular calcium concentration and generating depolarizing receptor potentials. Ion substitution and pharmacological approaches reveal that intracellular calcium store depletion is crucial for LA-induced signaling in a subset of trigeminal neurons. Using pseudorabies virus (PrV) as a live cell tracer, we identified a subset of lingual nerve-innervated trigeminal neurons that respond to different subsets of fatty acids. Quantitative real-time PCR of several transient receptor potential channel markers in individual neurons validated that PrV labeled a subset but not the entire population of lingual-innervated trigeminal neurons. We further confirmed that the LA-induced intracellular calcium rise is exclusively coming from the release of calcium stores from the endoplasmic reticulum in this subset of lingual nerve-innervated trigeminal neurons.

Quantitative 3D investigation of Neuronal network in mouse spinal cord model

NASA Astrophysics Data System (ADS)

Bukreeva, I.; Campi, G.; Fratini, M.; Spanò, R.; Bucci, D.; Battaglia, G.; Giove, F.; Bravin, A.; Uccelli, A.; Venturi, C.; Mastrogiacomo, M.; Cedola, A.

2017-01-01

The investigation of the neuronal network in mouse spinal cord models represents the basis for the research on neurodegenerative diseases. In this framework, the quantitative analysis of the single elements in different districts is a crucial task. However, conventional 3D imaging techniques do not have enough spatial resolution and contrast to allow for a quantitative investigation of the neuronal network. Exploiting the high coherence and the high flux of synchrotron sources, X-ray Phase-Contrast multiscale-Tomography allows for the 3D investigation of the neuronal microanatomy without any aggressive sample preparation or sectioning. We investigated healthy-mouse neuronal architecture by imaging the 3D distribution of the neuronal-network with a spatial resolution of 640 nm. The high quality of the obtained images enables a quantitative study of the neuronal structure on a subject-by-subject basis. We developed and applied a spatial statistical analysis on the motor neurons to obtain quantitative information on their 3D arrangement in the healthy-mice spinal cord. Then, we compared the obtained results with a mouse model of multiple sclerosis. Our approach paves the way to the creation of a “database” for the characterization of the neuronal network main features for a comparative investigation of neurodegenerative diseases and therapies.

Activation of Oral Trigeminal Neurons by Fatty Acids is Dependent upon Intracellular Calcium

PubMed Central

Yu, Tian; Shah, Bhavik P.; Hansen, Dane R.; Park-York, MieJung; Gilbertson, Timothy A.

2012-01-01

The chemoreception of dietary fat in the oral cavity has largely been attributed to activation of the somatosensory system that conveys the textural properties of fat. However, the ability of fatty acids, which are believed to represent the proximate stimulus for fat taste, to stimulate rat trigeminal neurons has remained unexplored. Here, we found that several free fatty acids are capable of activating trigeminal neurons with different kinetics. Further, a polyunsaturated fatty acid, linoleic acid (LA), activates trigeminal neurons by increasing intracellular calcium concentration and generating depolarizing receptor potentials. Ion substitution and pharmacological approaches reveal that intracellular calcium store depletion is crucial for LA-induced signaling in a subset of trigeminal neurons. Using pseudorabies virus (PrV) as a live cell tracer, we identified a subset of lingual nerve-innervated trigeminal neurons that respond to different subsets of fatty acids. Quantitative real-time PCR of several transient receptor potential (TRP) channel markers in individual neurons validated that PrV labeled a subset but not the entire population of lingual-innervated trigeminal neurons. We further confirmed that the LA-induced intracellular calcium rise is exclusively coming from the release of calcium stores from the endoplasmic reticulum in this subset of lingual nerve-innervated trigeminal neurons. PMID:22644615

[Mirror neurons].

PubMed

Rubia Vila, Francisco José

2011-01-01

Mirror neurons were recently discovered in frontal brain areas of the monkey. They are activated when the animal makes a specific movement, but also when the animal observes the same movement in another animal. Some of them also respond to the emotional expression of other animals of the same species. These mirror neurons have also been found in humans. They respond to or "reflect" actions of other individuals in the brain and are thought to represent the basis for imitation and empathy and hence the neurobiological substrate for "theory of mind", the potential origin of language and the so-called moral instinct.

Symmetry breaking in two interacting populations of quadratic integrate-and-fire neurons.

PubMed

Ratas, Irmantas; Pyragas, Kestutis

2017-10-01

We analyze the dynamics of two coupled identical populations of quadratic integrate-and-fire neurons, which represent the canonical model for class I neurons near the spiking threshold. The populations are heterogeneous; they include both inherently spiking and excitable neurons. The coupling within and between the populations is global via synapses that take into account the finite width of synaptic pulses. Using a recently developed reduction method based on the Lorentzian ansatz, we derive a closed system of equations for the neuron's firing rates and the mean membrane potentials in both populations. The reduced equations are exact in the infinite-size limit. The bifurcation analysis of the equations reveals a rich variety of nonsymmetric patterns, including a splay state, antiphase periodic oscillations, chimera-like states, and chaotic oscillations as well as bistabilities between various states. The validity of the reduced equations is confirmed by direct numerical simulations of the finite-size networks.

Symmetry breaking in two interacting populations of quadratic integrate-and-fire neurons

NASA Astrophysics Data System (ADS)

Ratas, Irmantas; Pyragas, Kestutis

2017-10-01

We analyze the dynamics of two coupled identical populations of quadratic integrate-and-fire neurons, which represent the canonical model for class I neurons near the spiking threshold. The populations are heterogeneous; they include both inherently spiking and excitable neurons. The coupling within and between the populations is global via synapses that take into account the finite width of synaptic pulses. Using a recently developed reduction method based on the Lorentzian ansatz, we derive a closed system of equations for the neuron's firing rates and the mean membrane potentials in both populations. The reduced equations are exact in the infinite-size limit. The bifurcation analysis of the equations reveals a rich variety of nonsymmetric patterns, including a splay state, antiphase periodic oscillations, chimera-like states, and chaotic oscillations as well as bistabilities between various states. The validity of the reduced equations is confirmed by direct numerical simulations of the finite-size networks.

Altered cortical GABA neurotransmission in schizophrenia: insights into novel therapeutic strategies.

PubMed

Stan, Ana D; Lewis, David A

2012-06-01

Altered markers of cortical GABA neurotransmission are among the most consistently observed abnormalities in postmortem studies of schizophrenia. The altered markers are particularly evident between the chandelier class of GABA neurons and their synaptic targets, the axon initial segment (AIS) of pyramidal neurons. For example, in the dorsolateral prefrontal cortex of subjects with schizophrenia immunoreactivity for the GABA membrane transporter is decreased in presynaptic chandelier neuron axon terminals, whereas immunoreactivity for the GABAA receptor α2 subunit is increased in postsynaptic AIS. Both of these molecular changes appear to be compensatory responses to a presynaptic deficit in GABA synthesis, and thus could represent targets for novel therapeutic strategies intended to augment the brain's own compensatory mechanisms. Recent findings that GABA inputs from neocortical chandelier neurons can be powerfully excitatory provide new ideas about the role of these neurons in the pathophysiology of cortical dysfunction in schizophrenia, and consequently in the design of pharmacological interventions.

Slack channels expressed in sensory neurons control neuropathic pain in mice.

PubMed

Lu, Ruirui; Bausch, Anne E; Kallenborn-Gerhardt, Wiebke; Stoetzer, Carsten; Debruin, Natasja; Ruth, Peter; Geisslinger, Gerd; Leffler, Andreas; Lukowski, Robert; Schmidtko, Achim

2015-01-21

Slack (Slo2.2) is a sodium-activated potassium channel that regulates neuronal firing activities and patterns. Previous studies identified Slack in sensory neurons, but its contribution to acute and chronic pain in vivo remains elusive. Here we generated global and sensory neuron-specific Slack mutant mice and analyzed their behavior in various animal models of pain. Global ablation of Slack led to increased hypersensitivity in models of neuropathic pain, whereas the behavior in models of inflammatory and acute nociceptive pain was normal. Neuropathic pain behaviors were also exaggerated after ablation of Slack selectively in sensory neurons. Notably, the Slack opener loxapine ameliorated persisting neuropathic pain behaviors. In conclusion, Slack selectively controls the sensory input in neuropathic pain states, suggesting that modulating its activity might represent a novel strategy for management of neuropathic pain. Copyright © 2015 the authors 0270-6474/15/351125-11$15.00/0.

Strengths and weaknesses of temporal stability analysis for monitoring and estimating grid-mean soil moisture in a high-intensity irrigated agricultural landscape

NASA Astrophysics Data System (ADS)

Ran, Youhua; Li, Xin; Jin, Rui; Kang, Jian; Cosh, Michael H.

2017-01-01

Monitoring and estimating grid-mean soil moisture is very important for assessing many hydrological, biological, and biogeochemical processes and for validating remotely sensed surface soil moisture products. Temporal stability analysis (TSA) is a valuable tool for identifying a small number of representative sampling points to estimate the grid-mean soil moisture content. This analysis was evaluated and improved using high-quality surface soil moisture data that were acquired by a wireless sensor network in a high-intensity irrigated agricultural landscape in an arid region of northwestern China. The performance of the TSA was limited in areas where the representative error was dominated by random events, such as irrigation events. This shortcoming can be effectively mitigated by using a stratified TSA (STSA) method, proposed in this paper. In addition, the following methods were proposed for rapidly and efficiently identifying representative sampling points when using TSA. (1) Instantaneous measurements can be used to identify representative sampling points to some extent; however, the error resulting from this method is significant when validating remotely sensed soil moisture products. Thus, additional representative sampling points should be considered to reduce this error. (2) The calibration period can be determined from the time span of the full range of the grid-mean soil moisture content during the monitoring period. (3) The representative error is sensitive to the number of calibration sampling points, especially when only a few representative sampling points are used. Multiple sampling points are recommended to reduce data loss and improve the likelihood of representativeness at two scales.

Control of food intake and energy expenditure by Nos1 neurons of the paraventricular hypothalamus.

PubMed

Sutton, Amy K; Pei, Hongjuan; Burnett, Korri H; Myers, Martin G; Rhodes, Christopher J; Olson, David P

2014-11-12

The paraventricular nucleus of the hypothalamus (PVH) contains a heterogeneous cluster of Sim1-expressing cell types that comprise a major autonomic output nucleus and play critical roles in the control of food intake and energy homeostasis. The roles of specific PVH neuronal subtypes in energy balance have yet to be defined, however. The PVH contains nitric oxide synthase-1 (Nos1)-expressing (Nos1(PVH)) neurons of unknown function; these represent a subset of the larger population of Sim1-expressing PVH (Sim1(PVH)) neurons. To determine the role of Nos1(PVH) neurons in energy balance, we used Cre-dependent viral vectors to both map their efferent projections and test their functional output in mice. Here we show that Nos1(PVH) neurons project to hindbrain and spinal cord regions important for food intake and energy expenditure control. Moreover, pharmacogenetic activation of Nos1(PVH) neurons suppresses feeding to a similar extent as Sim1(PVH) neurons, and increases energy expenditure and activity. Furthermore, we found that oxytocin-expressing PVH neurons (OXT(PVH)) are a subset of Nos1(PVH) neurons. OXT(PVH) cells project to preganglionic, sympathetic neurons in the thoracic spinal cord and increase energy expenditure upon activation, though not to the same extent as Nos1(PVH) neurons; their activation fails to alter feeding, however. Thus, Nos1(PVH) neurons promote negative energy balance through changes in feeding and energy expenditure, whereas OXT(PVH) neurons regulate energy expenditure alone, suggesting a crucial role for non-OXT Nos1(PVH) neurons in feeding regulation. Copyright © 2014 the authors 0270-6474/14/3415306-13$15.00/0.

Cerebral hierarchies: predictive processing, precision and the pulvinar

PubMed Central

Kanai, Ryota; Komura, Yutaka; Shipp, Stewart; Friston, Karl

2015-01-01

This paper considers neuronal architectures from a computational perspective and asks what aspects of neuroanatomy and neurophysiology can be disclosed by the nature of neuronal computations? In particular, we extend current formulations of the brain as an organ of inference—based upon hierarchical predictive coding—and consider how these inferences are orchestrated. In other words, what would the brain require to dynamically coordinate and contextualize its message passing to optimize its computational goals? The answer that emerges rests on the delicate (modulatory) gain control of neuronal populations that select and coordinate (prediction error) signals that ascend cortical hierarchies. This is important because it speaks to a hierarchical anatomy of extrinsic (between region) connections that form two distinct classes, namely a class of driving (first-order) connections that are concerned with encoding the content of neuronal representations and a class of modulatory (second-order) connections that establish context—in the form of the salience or precision ascribed to content. We explore the implications of this distinction from a formal perspective (using simulations of feature–ground segregation) and consider the neurobiological substrates of the ensuing precision-engineered dynamics, with a special focus on the pulvinar and attention. PMID:25823866

The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction.

PubMed

Xue, Fangzheng; Li, Qian; Li, Xiumin

2017-01-01

Recently, echo state network (ESN) has attracted a great deal of attention due to its high accuracy and efficient learning performance. Compared with the traditional random structure and classical sigmoid units, simple circle topology and leaky integrator neurons have more advantages on reservoir computing of ESN. In this paper, we propose a new model of ESN with both circle reservoir structure and leaky integrator units. By comparing the prediction capability on Mackey-Glass chaotic time series of four ESN models: classical ESN, circle ESN, traditional leaky integrator ESN, circle leaky integrator ESN, we find that our circle leaky integrator ESN shows significantly better performance than other ESNs with roughly 2 orders of magnitude reduction of the predictive error. Moreover, this model has stronger ability to approximate nonlinear dynamics and resist noise than conventional ESN and ESN with only simple circle structure or leaky integrator neurons. Our results show that the combination of circle topology and leaky integrator neurons can remarkably increase dynamical diversity and meanwhile decrease the correlation of reservoir states, which contribute to the significant improvement of computational performance of Echo state network on time series prediction.

Active inference and cognitive-emotional interactions in the brain.

PubMed

Pezzulo, Giovanni; Barca, Laura; Friston, Karl J

2015-01-01

All organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the "affective tuning" of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.

Hippocampal Erk Mechanisms Linking Prediction Error to Fear Extinction: Roles of Shock Expectancy and Contextual Aversive Valence

ERIC Educational Resources Information Center

Huh, Kyu Hwan; Guzman, Yomayra F.; Tronson, Natalie C.; Guedea, Anita L.; Gao, Can; Radulovic, Jelena

2009-01-01

Extinction of fear requires learning that anticipated aversive events no longer occur. Animal models reveal that sustained phosphorylation of the extracellular signal-regulated kinase (Erk) in hippocampal CA1 neurons plays an important role in this process. However, the key signals triggering and regulating the activity of Erk are not known. By…

Optimization of Aimpoints for Coordinate Seeking Weapons

DTIC Science & Technology

2015-09-01

aiming) and independent ( ballistic ) errors are taken into account, before utilizing each of the three damage functions representing the weapon. A Monte...characteristics such as the radius of the circle containing the weapon aimpoint, impact angle, dependent (aiming) and independent ( ballistic ) errors are taken...Dependent (Aiming) Error .................................8 2. Single Weapon Independent ( Ballistic ) Error .............................9 3

Predictive Coding or Evidence Accumulation? False Inference and Neuronal Fluctuations

PubMed Central

Friston, Karl J.; Kleinschmidt, Andreas

2010-01-01

Perceptual decisions can be made when sensory input affords an inference about what generated that input. Here, we report findings from two independent perceptual experiments conducted during functional magnetic resonance imaging (fMRI) with a sparse event-related design. The first experiment, in the visual modality, involved forced-choice discrimination of coherence in random dot kinematograms that contained either subliminal or periliminal motion coherence. The second experiment, in the auditory domain, involved free response detection of (non-semantic) near-threshold acoustic stimuli. We analysed fluctuations in ongoing neural activity, as indexed by fMRI, and found that neuronal activity in sensory areas (extrastriate visual and early auditory cortex) biases perceptual decisions towards correct inference and not towards a specific percept. Hits (detection of near-threshold stimuli) were preceded by significantly higher activity than both misses of identical stimuli or false alarms, in which percepts arise in the absence of appropriate sensory input. In accord with predictive coding models and the free-energy principle, this observation suggests that cortical activity in sensory brain areas reflects the precision of prediction errors and not just the sensory evidence or prediction errors per se. PMID:20369004

Mapping social behavior-induced brain activation at cellular resolution in the mouse

PubMed Central

Kim, Yongsoo; Venkataraju, Kannan Umadevi; Pradhan, Kith; Mende, Carolin; Taranda, Julian; Turaga, Srinivas C.; Arganda-Carreras, Ignacio; Ng, Lydia; Hawrylycz, Michael J.; Rockland, Kathleen; Seung, H. Sebastian; Osten, Pavel

2014-01-01

Understanding how brain activation mediates behaviors is a central goal of systems neuroscience. Here we apply an automated method for mapping brain activation in the mouse in order to probe how sex-specific social behaviors are represented in the male brain. Our method uses the immediate early gene c-fos, a marker of neuronal activation, visualized by serial two-photon tomography: the c-fos-GFP-positive neurons are computationally detected, their distribution is registered to a reference brain and a brain atlas, and their numbers are analyzed by statistical tests. Our results reveal distinct and shared female and male interaction-evoked patterns of male brain activation representing sex discrimination and social recognition. We also identify brain regions whose degree of activity correlates to specific features of social behaviors and estimate the total numbers and the densities of activated neurons per brain areas. Our study opens the door to automated screening of behavior-evoked brain activation in the mouse. PMID:25558063

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.

Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution

PubMed Central

Herculano-Houzel, Suzana

2011-01-01

It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans). The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum). These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution. PMID:21390261

Temporal-pattern recognition by single neurons in a sensory pathway devoted to social communication behavior

PubMed Central

Carlson, Bruce A.

2010-01-01

Sensory systems often encode stimulus information into the temporal pattern of action potential activity. However, little is known about how the information contained within these patterns is extracted by postsynaptic neurons. Similar to temporal coding by sensory neurons, social information in mormyrid fish is encoded into the temporal patterning of an electric organ discharge (EOD). In the current study, sensitivity to temporal patterns of electrosensory stimuli was found to arise within the midbrain posterior exterolateral nucleus (ELp). Whole-cell patch recordings from ELp neurons in vivo revealed three patterns of interpulse interval (IPI) tuning: low-pass neurons tuned to long intervals, high-pass neurons tuned to short intervals and band-pass neurons tuned to intermediate intervals. Many neurons within each class also responded preferentially to either increasing or decreasing IPIs. Playback of electric signaling patterns recorded from freely behaving fish revealed that the IPI and direction tuning of ELp neurons resulted in selective responses to particular social communication displays characterized by distinct IPI patterns. The postsynaptic potential responses of many neurons indicated a combination of excitatory and inhibitory synaptic input, and the IPI tuning of ELp neurons was directly related to rate-dependent changes in the direction and amplitude of postsynaptic potentials. These results suggest that differences in the dynamics of short-term synaptic plasticity in excitatory and inhibitory pathways may tune central sensory neurons to particular temporal patterns of presynaptic activity. This may represent a general mechanism for the processing of behaviorally-relevant stimulus information encoded into temporal patterns of activity by sensory neurons. PMID:19641105

Temporal-pattern recognition by single neurons in a sensory pathway devoted to social communication behavior.

PubMed

Carlson, Bruce A

2009-07-29

Sensory systems often encode stimulus information into the temporal pattern of action potential activity. However, little is known about how the information contained within these patterns is extracted by postsynaptic neurons. Similar to temporal coding by sensory neurons, social information in mormyrid fish is encoded into the temporal patterning of an electric organ discharge. In the current study, sensitivity to temporal patterns of electrosensory stimuli was found to arise within the midbrain posterior exterolateral nucleus (ELp). Whole-cell patch recordings from ELp neurons in vivo revealed three patterns of interpulse interval (IPI) tuning: low-pass neurons tuned to long intervals, high-pass neurons tuned to short intervals, and bandpass neurons tuned to intermediate intervals. Many neurons within each class also responded preferentially to either increasing or decreasing IPIs. Playback of electric signaling patterns recorded from freely behaving fish revealed that the IPI and direction tuning of ELp neurons resulted in selective responses to particular social communication displays characterized by distinct IPI patterns. The postsynaptic potential responses of many neurons indicated a combination of excitatory and inhibitory synaptic input, and the IPI tuning of ELp neurons was directly related to rate-dependent changes in the direction and amplitude of postsynaptic potentials. These results suggest that differences in the dynamics of short-term synaptic plasticity in excitatory and inhibitory pathways may tune central sensory neurons to particular temporal patterns of presynaptic activity. This may represent a general mechanism for the processing of behaviorally relevant stimulus information encoded into temporal patterns of activity by sensory neurons.

Activation of the Basal Forebrain by the Orexin/Hypocretin Neurons: Orexin International Symposium

PubMed Central

Arrigoni, Elda; Mochizuki, Takatoshi; Scammell, Thomas E.

2010-01-01

The orexin neurons play an essential role in driving arousal and in maintaining normal wakefulness. Lack of orexin neurotransmission produces a chronic state of hypoarousal characterized by excessive sleepiness, frequent transitions between wake and sleep, and episodes of cataplexy. A growing body of research now suggests that the basal forebrain (BF) may be a key site through which the orexin-producing neurons promote arousal. Here we review anatomical, pharmacological and electrophysiological studies on how the orexin neurons may promote arousal by exciting cortically-projecting neurons of the BF. Orexin fibers synapse on BF cholinergic neurons and orexin-A is released in the BF during waking. Local application of orexins excites BF cholinergic neurons, induces cortical release of acetylcholine, and promotes wakefulness. The orexin neurons also contain and probably co-release the inhibitory neuropeptide dynorphin. We found that orexin-A and dynorphin have specific effects on different classes of BF neurons that project to the cortex. Cholinergic neurons were directly excited by orexin-A, but did not respond to dynorphin. Non-cholinergic BF neurons that project to the cortex seem to comprise at least two populations with some directly excited by orexin that may represent wake-active, GABAergic neurons, whereas others did not respond to orexin but were inhibited by dynorphin and may be sleep-active, GABAergic neurons. This evidence suggests that the BF is a key site through which orexins activate the cortex and promotes behavioral arousal. In addition, orexins and dynorphin may act synergistically in the BF to promote arousal and improve cognitive performance. PMID:19723027

Scaling of brain metabolism with a fixed energy budget per neuron: implications for neuronal activity, plasticity and evolution.

PubMed

Herculano-Houzel, Suzana

2011-03-01

It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans). The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum). These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution.

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.

Galanin-Expressing GABA Neurons in the Lateral Hypothalamus Modulate Food Reward and Noncompulsive Locomotion

PubMed Central

Hoang, John; Bruce-Keller, Annadora; Berthoud, Hans-Rudolf; Morrison, Christopher D.

2017-01-01

The lateral hypothalamus (LHA) integrates reward and appetitive behavior and is composed of many overlapping neuronal populations. Recent studies associated LHA GABAergic neurons (LHAGABA), which densely innervate the ventral tegmental area (VTA), with modulation of food reward and consumption; yet, LHAGABA projections to the VTA exclusively modulated food consumption, not reward. We identified a subpopulation of LHAGABA neurons that coexpress the neuropeptide galanin (LHAGal). These LHAGal neurons also modulate food reward, but lack direct VTA innervation. We hypothesized that LHAGal neurons may represent a subpopulation of LHAGABA neurons that mediates food reward independent of direct VTA innervation. We used chemogenetic activation of LHAGal or LHAGABA neurons in mice to compare their role in feeding behavior. We further analyzed locomotor behavior to understand how differential VTA connectivity and transmitter release in these LHA neurons influences this behavior. LHAGal or LHAGABA neuronal activation both increased operant food-seeking behavior, but only activation of LHAGABA neurons increased overall chow consumption. Additionally, LHAGal or LHAGABA neuronal activation similarly induced locomotor activity, but with striking differences in modality. Activation of LHAGABA neurons induced compulsive-like locomotor behavior; while LHAGal neurons induced locomotor activity without compulsivity. Thus, LHAGal neurons define a subpopulation of LHAGABA neurons without direct VTA innervation that mediate noncompulsive food-seeking behavior. We speculate that the striking difference in compulsive-like locomotor behavior is also based on differential VTA innervation. The downstream neural network responsible for this behavior and a potential role for galanin as neuromodulator remains to be identified. SIGNIFICANCE STATEMENT The lateral hypothalamus (LHA) regulates motivated feeding behavior via GABAergic LHA neurons. The molecular identity of LHAGABA neurons is heterogeneous and largely undefined. Here we introduce LHAGal neurons as a subset of LHAGABA neurons that lack direct innervation of the ventral tegmental area (VTA). LHAGal neurons are sufficient to drive motivated feeding and locomotor activity similar to LHAGABA neurons, but without inducing compulsive-like behaviors, which we propose to require direct VTA innervation. Our study integrates galanin-expressing LHA neurons into our current understanding of the neuronal circuits and molecular mechanisms of the LHA that contribute to motivated feeding behaviors. PMID:28539422

Hyperexcitable neurons and altered non-neuronal cells in the compressed spinal ganglion

PubMed Central

LaMotte, Robert H.; Chao, MA

2009-01-01

The cell body or soma in the dosal root ganglion (DRG) is normally excitable and this excitability can increase and persist after an injury of peripheral sensory neurons. In a rat model of radicular pain, an intraforaminal implantation of a rod that chronically compressed the lumbar DRG (“CCD” model) resulted in neuronal somal hyperexcitability and spontaneous activity that was accompanied by hyperalgesia in the ipsilateral hind paw. By the 5th day after onset of CCD, there was a novel upregulation in neuronal expression of the chemokine, monocyte chemoattractant protein-1 (MCP-1 or CCL2) and also its receptor, CCR2. The neurons developed, in response to topically applied MCP-1, an excitatory response that they normally do not have. CCD also activated non-neuronal cells including, for example, the endothelial cells as evidenced by angiogenesis in the form of an increased number of capillaries in the DRG after 7 days. A working hypothesis is that the CCD induced changes in neurons and non-neuronal cells that may act together to promote the survival of the injured tissue. The release of ligands such as CCL2, in addition to possibly activating nociceptive neurons (maintaining the pain), may also act to preserve injured cells in the face of ischemia and hypoxia, for example, by promoting angiogenesis. Thus, somal hyperexcitability, as often said of inflammation, may represent a double edged sword. PMID:18958366

Modeling gravity-dependent plasticity of the angular vestibuloocular reflex with a physiologically based neural network.

PubMed

Xiang, Yongqing; Yakushin, Sergei B; Cohen, Bernard; Raphan, Theodore

2006-12-01

A neural network model was developed to explain the gravity-dependent properties of gain adaptation of the angular vestibuloocular reflex (aVOR). Gain changes are maximal at the head orientation where the gain is adapted and decrease as the head is tilted away from that position and can be described by the sum of gravity-independent and gravity-dependent components. The adaptation process was modeled by modifying the weights and bias values of a three-dimensional physiologically based neural network of canal-otolith-convergent neurons that drive the aVOR. Model parameters were trained using experimental vertical aVOR gain values. The learning rule aimed to reduce the error between eye velocities obtained from experimental gain values and model output in the position of adaptation. Although the model was trained only at specific head positions, the model predicted the experimental data at all head positions in three dimensions. Altering the relative learning rates of the weights and bias improved the model-data fits. Model predictions in three dimensions compared favorably with those of a double-sinusoid function, which is a fit that minimized the mean square error at every head position and served as the standard by which we compared the model predictions. The model supports the hypothesis that gravity-dependent adaptation of the aVOR is realized in three dimensions by a direct otolith input to canal-otolith neurons, whose canal sensitivities are adapted by the visual-vestibular mismatch. The adaptation is tuned by how the weights from otolith input to the canal-otolith-convergent neurons are adapted for a given head orientation.

Parietal neurons encode expected gains in instrumental information

PubMed Central

Foley, Nicholas C.; Kelly, Simon P.; Mhatre, Himanshu; Gottlieb, Jacqueline

2017-01-01

In natural behavior, animals have access to multiple sources of information, but only a few of these sources are relevant for learning and actions. Beyond choosing an appropriate action, making good decisions entails the ability to choose the relevant information, but fundamental questions remain about the brain’s information sampling policies. Recent studies described the neural correlates of seeking information about a reward, but it remains unknown whether, and how, neurons encode choices of instrumental information, in contexts in which the information guides subsequent actions. Here we show that parietal cortical neurons involved in oculomotor decisions encode, before an information sampling saccade, the reduction in uncertainty that the saccade is expected to bring for a subsequent action. These responses were distinct from the neurons’ visual and saccadic modulations and from signals of expected reward or reward prediction errors. Therefore, even in an instrumental context when information and reward gains are closely correlated, individual cells encode decision variables that are based on informational factors and can guide the active sampling of action-relevant cues. PMID:28373569

Planning activity for internally generated reward goals in monkey amygdala neurons

PubMed Central

Schultz, Wolfram

2015-01-01

The best rewards are often distant and can only be achieved by planning and decision-making over several steps. We designed a multi-step choice task in which monkeys followed internal plans to save rewards towards self-defined goals. During this self-controlled behavior, amygdala neurons showed future-oriented activity that reflected the animal’s plan to obtain specific rewards several trials ahead. This prospective activity encoded crucial components of the animal’s plan, including value and length of the planned choice sequence. It began on initial trials when a plan would be formed, reappeared step-by-step until reward receipt, and readily updated with a new sequence. It predicted performance, including errors, and typically disappeared during instructed behavior. Such prospective activity could underlie the formation and pursuit of internal plans characteristic for goal-directed behavior. The existence of neuronal planning activity in the amygdala suggests an important role for this structure in guiding behavior towards internally generated, distant goals. PMID:25622146

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning

PubMed Central

Franklin, Nicholas T; Frank, Michael J

2015-01-01

Convergent evidence suggests that the basal ganglia support reinforcement learning by adjusting action values according to reward prediction errors. However, adaptive behavior in stochastic environments requires the consideration of uncertainty to dynamically adjust the learning rate. We consider how cholinergic tonically active interneurons (TANs) may endow the striatum with such a mechanism in computational models spanning three Marr's levels of analysis. In the neural model, TANs modulate the excitability of spiny neurons, their population response to reinforcement, and hence the effective learning rate. Long TAN pauses facilitated robustness to spurious outcomes by increasing divergence in synaptic weights between neurons coding for alternative action values, whereas short TAN pauses facilitated stochastic behavior but increased responsiveness to change-points in outcome contingencies. A feedback control system allowed TAN pauses to be dynamically modulated by uncertainty across the spiny neuron population, allowing the system to self-tune and optimize performance across stochastic environments. DOI: http://dx.doi.org/10.7554/eLife.12029.001 PMID:26705698

Assessing the use of cognitive heuristic representativeness in clinical reasoning.

PubMed

Payne, Velma L; Crowley, Rebecca S; Crowley, Rebecca

2008-11-06

We performed a pilot study to investigate use of the cognitive heuristic Representativeness in clinical reasoning. We tested a set of tasks and assessments to determine whether subjects used the heuristics in reasoning, to obtain initial frequencies of heuristic use and related cognitive errors, and to collect cognitive process data using think-aloud techniques. The study investigates two aspects of the Representativeness heuristic - judging by perceived frequency and representativeness as causal beliefs. Results show that subjects apply both aspects of the heuristic during reasoning, and make errors related to misapplication of these heuristics. Subjects in this study rarely used base rates, showed significant variability in their recall of base rates, demonstrated limited ability to use provided base rates, and favored causal data in diagnosis. We conclude that the tasks and assessments we have developed provide a suitable test-bed to study the cognitive processes underlying heuristic errors.

Assessing Use of Cognitive Heuristic Representativeness in Clinical Reasoning

PubMed Central

Payne, Velma L.; Crowley, Rebecca S.

2008-01-01

We performed a pilot study to investigate use of the cognitive heuristic Representativeness in clinical reasoning. We tested a set of tasks and assessments to determine whether subjects used the heuristics in reasoning, to obtain initial frequencies of heuristic use and related cognitive errors, and to collect cognitive process data using think-aloud techniques. The study investigates two aspects of the Representativeness heuristic - judging by perceived frequency and representativeness as causal beliefs. Results show that subjects apply both aspects of the heuristic during reasoning, and make errors related to misapplication of these heuristics. Subjects in this study rarely used base rates, showed significant variability in their recall of base rates, demonstrated limited ability to use provided base rates, and favored causal data in diagnosis. We conclude that the tasks and assessments we have developed provide a suitable test-bed to study the cognitive processes underlying heuristic errors. PMID:18999140

Simulator for neural networks and action potentials.

PubMed

Baxter, Douglas A; Byrne, John H

2007-01-01

A key challenge for neuroinformatics is to devise methods for representing, accessing, and integrating vast amounts of diverse and complex data. A useful approach to represent and integrate complex data sets is to develop mathematical models [Arbib (The Handbook of Brain Theory and Neural Networks, pp. 741-745, 2003); Arbib and Grethe (Computing the Brain: A Guide to Neuroinformatics, 2001); Ascoli (Computational Neuroanatomy: Principles and Methods, 2002); Bower and Bolouri (Computational Modeling of Genetic and Biochemical Networks, 2001); Hines et al. (J. Comput. Neurosci. 17, 7-11, 2004); Shepherd et al. (Trends Neurosci. 21, 460-468, 1998); Sivakumaran et al. (Bioinformatics 19, 408-415, 2003); Smolen et al. (Neuron 26, 567-580, 2000); Vadigepalli et al. (OMICS 7, 235-252, 2003)]. Models of neural systems provide quantitative and modifiable frameworks for representing data and analyzing neural function. These models can be developed and solved using neurosimulators. One such neurosimulator is simulator for neural networks and action potentials (SNNAP) [Ziv (J. Neurophysiol. 71, 294-308, 1994)]. SNNAP is a versatile and user-friendly tool for developing and simulating models of neurons and neural networks. SNNAP simulates many features of neuronal function, including ionic currents and their modulation by intracellular ions and/or second messengers, and synaptic transmission and synaptic plasticity. SNNAP is written in Java and runs on most computers. Moreover, SNNAP provides a graphical user interface (GUI) and does not require programming skills. This chapter describes several capabilities of SNNAP and illustrates methods for simulating neurons and neural networks. SNNAP is available at http://snnap.uth.tmc.edu .

Cav 1.3 channels play a crucial role in the formation of paroxysmal depolarization shifts in cultured hippocampal neurons.

PubMed

Stiglbauer, Victoria; Hotka, Matej; Ruiß, Manuel; Hilber, Karlheinz; Boehm, Stefan; Kubista, Helmut

2017-05-01

An increase of neuronal Ca v 1.3 L-type calcium channels (LTCCs) has been observed in various animal models of epilepsy. However, LTCC inhibitors failed in clinical trials of epileptic treatment. There is compelling evidence that paroxysmal depolarization shifts (PDSs) involve Ca 2+ influx through LTCCs. PDSs represent a hallmark of epileptiform activity. In recent years, a probable epileptogenic role for PDSs has been proposed. However, the implication of the two neuronal LTCC isoforms, Ca v 1.2 and Ca v 1.3, in PDSs remained unknown. Moreover, Ca 2+ -dependent nonspecific cation (CAN) channels have also been suspected to contribute to PDSs. Nevertheless, direct experimental support of an important role of CAN channel activation in PDS formation is still lacking. Primary neuronal networks derived from dissociated hippocampal neurons were generated from mice expressing a dihydropyridine-insensitive Ca v 1.2 mutant (Ca v 1.2DHP -/- mice) or from Ca v 1.3 -/- knockout mice. To investigate the role of Ca v 1.2 and Ca v 1.3, perforated patch-clamp recordings were made of epileptiform activity, which was elicited using either bicuculline or caffeine. LTCC activity was modulated using the dihydropyridines Bay K 8644 (agonist) and isradipine (antagonist). Distinct PDS could be elicited upon LTCC potentiation in Ca v 1.2DHP -/- neurons but not in Ca v 1.3 -/- neurons. In contrast, when bicuculline led to long-lasting, seizure-like discharge events rather than PDS, these were prolonged in Ca v 1.3 -/- neurons but not in Ca v 1.2DHP -/- neurons. Because only the Ca v 1.2 isoform is functionally coupled to CAN channels in primary hippocampal networks, PDS formation does not require CAN channel activity. Our data suggest that the LTCC requirement of PDS relates primarily to Ca v 1.3 channels rather than to Ca v 1.2 channels and CAN channels in hippocampal neurons. Hence, Ca v 1.3 may represent a new therapeutic target for suppression of PDS development. The proposed epileptogenic role of PDSs may allow for a prophylactic rather than the unsuccessful seizure suppressing application of LTCC inhibitors. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Neuromodulation targets intrinsic cardiac neurons to attenuate neuronally mediated atrial arrhythmias.

PubMed

Gibbons, David D; Southerland, E Marie; Hoover, Donald B; Beaumont, Eric; Armour, J Andrew; Ardell, Jeffrey L

2012-02-01

Our objective was to determine whether atrial fibrillation (AF) results from excessive activation of intrinsic cardiac neurons (ICNs) and, if so, whether select subpopulations of neurons therein represent therapeutic targets for suppression of this arrhythmogenic potential. Trains of five electrical stimuli (0.3-1.2 mA, 1 ms) were delivered during the atrial refractory period to mediastinal nerves (MSN) on the superior vena cava to evoke AF. Neuroanatomical studies were performed by injecting the neuronal tracer DiI into MSN sites that induced AF. Functional studies involved recording of neuronal activity in situ from the right atrial ganglionated plexus (RAGP) in response to MSN stimulation (MSNS) prior to and following neuromodulation involving either preemptive spinal cord stimulation (SCS; T(1)-T(3), 50 Hz, 200-ms duration) or ganglionic blockade (hexamethonium, 5 mg/kg). The tetramethylindocarbocyanine perchlorate (DiI) neuronal tracer labeled a subset (13.2%) of RAGP neurons, which also colocalized with cholinergic or adrenergic markers. A subset of DiI-labeled RAGP neurons were noncholinergic/nonadrenergic. MSNS evoked an ∼4-fold increase in RAGP neuronal activity from baseline, which SCS reduced by 43%. Hexamethonium blocked MSNS-evoked increases in neuronal activity. MSNS evoked AF in 78% of right-sided MSN sites, which SCS reduced to 33% and hexamethonium reduced to 7%. MSNS-induced bradycardia was maintained with SCS but was mitigated by hexamethonium. We conclude that MSNS activates subpopulations of intrinsic cardiac neurons, thereby resulting in the formation of atrial arrhythmias leading to atrial fibrillation. Stabilization of ICN local circuit neurons by SCS or the local circuit and autonomic efferent neurons with hexamethonium reduces the arrhythmogenic potential.

Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting

NASA Astrophysics Data System (ADS)

Li, Ming; Wang, Q. J.; Bennett, James C.; Robertson, David E.

2016-09-01

This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.

Effects of non-neuronal components for functional connectivity analysis from resting-state functional MRI toward automated diagnosis of schizophrenia

NASA Astrophysics Data System (ADS)

Kim, Junghoe; Lee, Jong-Hwan

2014-03-01

A functional connectivity (FC) analysis from resting-state functional MRI (rsfMRI) is gaining its popularity toward the clinical application such as diagnosis of neuropsychiatric disease. To delineate the brain networks from rsfMRI data, non-neuronal components including head motions and physiological artifacts mainly observed in cerebrospinal fluid (CSF), white matter (WM) along with a global brain signal have been regarded as nuisance variables in calculating the FC level. However, it is still unclear how the non-neuronal components can affect the performance toward diagnosis of neuropsychiatric disease. In this study, a systematic comparison of classification performance of schizophrenia patients was provided employing the partial correlation coefficients (CCs) as feature elements. Pair-wise partial CCs were calculated between brain regions, in which six combinatorial sets of nuisance variables were considered. The partial CCs were used as candidate feature elements followed by feature selection based on the statistical significance test between two groups in the training set. Once a linear support vector machine was trained using the selected features from the training set, the classification performance was evaluated using the features from the test set (i.e. leaveone- out cross validation scheme). From the results, the error rate using all non-neuronal components as nuisance variables (12.4%) was significantly lower than those using remaining combination of non-neuronal components as nuisance variables (13.8 ~ 20.0%). In conclusion, the non-neuronal components substantially degraded the automated diagnosis performance, which supports our hypothesis that the non-neuronal components are crucial in controlling the automated diagnosis performance of the neuropsychiatric disease using an fMRI modality.

A Subset of Palisade Endings Only in the Medial and Inferior Rectus Muscle in Monkey Contain Calretinin

PubMed Central

Lienbacher, Karoline; Ono, Seiji; Fleuriet, Jérome; Mustari, Michael; Horn, Anja K. E.

2018-01-01

Purpose To further chemically characterize palisade endings in extraocular muscles in rhesus monkeys. Methods Extraocular muscles of three rhesus monkeys were studied for expression of the calcium-binding protein calretinin (CR) in palisade endings and multiple endings. The complete innervation was visualized with antibodies against the synaptosomal-associated protein of 25 kDa and combined with immunofluorescence for CR. Six rhesus monkeys received tracer injections of choleratoxin subunit B or wheat germ agglutinin into either the belly or distal myotendinous junction of the medial or inferior rectus muscle to allow retrograde tracing in the C-group of the oculomotor nucleus. Double-immunofluorescence methods were used to study the CR content in retrogradely labeled neurons in the C-group. Results A subgroup of palisade and multiple endings was found to express CR, only in the medial and inferior rectus muscle. In contrast, the en plaque endings lacked CR. Accordingly, within the tracer-labeled neurons of the C-group, a subgroup expressed CR. Conclusions The study indicates that two different neuron populations targeting nontwitch muscle fibers are present within the C-group for inferior rectus and medial rectus, respectively, one expressing CR, one lacking CR. It is possible that the CR-negative neurons represent the basic population for all extraocular muscles, whereas the CR-positive neurons giving rise to CR-positive palisade endings represent a specialized, perhaps more excitable type of nerve ending in the medial and inferior rectus muscles, being more active in vergence. The malfunction of this CR-positive population of neurons that target nontwitch muscle fibers could play a significant role in strabismus.

Molecular biological effects of selective neuronal nitric oxide synthase inhibition in ovine lung injury

PubMed Central

Westphal, Martin; Enkhbaatar, Perenlei; Wang, Jianpu; Pazdrak, Konrad; Nakano, Yoshimitsu; Hamahata, Atsumori; Jonkam, Collette C.; Lange, Matthias; Connelly, Rhykka L.; Kulp, Gabriela A.; Cox, Robert A.; Hawkins, Hal K.; Schmalstieg, Frank C.; Horvath, Eszter; Szabo, Csaba; Traber, Lillian D.; Whorton, Elbert; Herndon, David N.; Traber, Daniel L.

2010-01-01

Neuronal nitric oxide synthase is critically involved in the pathogenesis of acute lung injury resulting from combined burn and smoke inhalation injury. We hypothesized that 7-nitroindazole, a selective neuronal nitric oxide synthase inhibitor, blocks central molecular mechanisms involved in the pathophysiology of this double-hit insult. Twenty-five adult ewes were surgically prepared and randomly allocated to 1) an uninjured, untreated sham group (n = 7), 2) an injured control group with no treatment (n = 7), 3) an injury group treated with 7-nitroindazole from 1-h postinjury to the remainder of the 24-h study period (n = 7), or 4) a sham-operated group subjected only to 7-nitroindazole to judge the effects in health. The combination injury was associated with twofold increased activity of neuronal nitric oxide synthase and oxidative/nitrosative stress, as indicated by significant increases in plasma nitrate/nitrite concentrations, 3-nitrotyrosine (an indicator of peroxynitrite formation), and malondialdehyde lung tissue content. The presence of systemic inflammation was evidenced by twofold, sixfold, and threefold increases in poly(ADP-ribose) polymerase, IL-8, and myeloperoxidase lung tissue concentrations, respectively (each P < 0.05 vs. sham). These molecular changes were linked to tissue damage, airway obstruction, and pulmonary shunting with deteriorated gas exchange. 7-Nitroindazole blocked, or at least attenuated, all these pathological changes. Our findings suggest 1) that nitric oxide formation derived from increased neuronal nitric oxide synthase activity represents a pivotal reactive agent in the patho-physiology of combined burn and smoke inhalation injury and 2) that selective neuronal nitric oxide synthase inhibition represents a goal-directed approach to attenuate the degree of injury. PMID:19965980

Synaptic and nonsynaptic plasticity approximating probabilistic inference

PubMed Central

Tully, Philip J.; Hennig, Matthias H.; Lansner, Anders

2014-01-01

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert. PMID:24782758

Expression of the P/Q (Cav2.1) calcium channel in nodose sensory neurons and arterial baroreceptors.

PubMed

Tatalovic, Milos; Glazebrook, Patricia A; Kunze, Diana L

2012-06-27

The predominant calcium current in nodose sensory neurons, including the subpopulation of baroreceptor neurons, is the N-type channel, Cav2.2. It is also the primary calcium channel responsible for transmitter release at their presynaptic terminals in the nucleus of the solitary tract in the brainstem. The P/Q channel, Cav2.1, the other major calcium channel responsible for transmitter release at mammalian synapses, represents only 15-20% of total calcium current in the general population of sensory neurons and makes a minor contribution to transmitter release at the presynaptic terminal. In the present study we identified a subpopulation of the largest nodose neurons (capacitance>50pF) in which, surprisingly, Cav2.1 represents over 50% of the total calcium current, differing from the remainder of the population. Consistent with these electrophysiological data, anti-Cav2.1 antibody labeling was more membrane delimited in a subgroup of the large neurons in slices of nodose ganglia. Data reported in other synapses in the central nervous system assign different roles in synaptic information transfer to the P/Q-type versus N-type calcium channels. The study raises the possibility that the P/Q channel which has been associated with high fidelity transmission at other central synapses serves a similar function in this group of large myelinated sensory afferents, including arterial baroreceptors where a high frequency regular discharge pattern signals the pressure pulse. This contrasts to the irregular lower frequency discharge of the unmyelinated fibers that make up the majority of the sensory population and that utilize the N-type channel in synaptic transmission. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Systematic Morphometry of Catecholamine Nuclei in the Brainstem.

PubMed

Bucci, Domenico; Busceti, Carla L; Calierno, Maria T; Di Pietro, Paola; Madonna, Michele; Biagioni, Francesca; Ryskalin, Larisa; Limanaqi, Fiona; Nicoletti, Ferdinando; Fornai, Francesco

2017-01-01

Catecholamine nuclei within the brainstem reticular formation (RF) play a pivotal role in a variety of brain functions. However, a systematic characterization of these nuclei in the very same experimental conditions is missing so far. Tyrosine hydroxylase (TH) immune-positive cells of the brainstem correspond to dopamine (DA)-, norepinephrine (NE)-, and epinephrine (E)-containing cells. Here, we report a systematic count of TH-positive neurons in the RF of the mouse brainstem by using stereological morphometry. All these nuclei were analyzed for anatomical localization, rostro-caudal extension, volume, neuron number, neuron density, and mean neuronal area for each nucleus. The present data apart from inherent informative value wish to represent a reference for neuronal mapping in those studies investigating the functional anatomy of the brainstem RF. These include: the sleep-wake cycle, movement control, muscle tone modulation, mood control, novelty orienting stimuli, attention, archaic responses to internal and external stressful stimuli, anxiety, breathing, blood pressure, and innumerable activities modulated by the archaic iso-dendritic hard core of the brainstem RF. Most TH-immune-positive cells fill the lateral part of the RF, which indeed possesses a high catecholamine content. A few nuclei are medial, although conventional nosography considers all these nuclei as part of the lateral column of the RF. Despite the key role of these nuclei in psychiatric and neurological disorders, only a few of them aspired a great attention in biomedical investigation, while most of them remain largely obscure although intense research is currently in progress. A simultaneous description of all these nuclei is not simply key to comprehend the variety of brainstem catecholamine reticular neurons, but probably represents an intrinsically key base for understanding brain physiology and physiopathology.

Systematic Morphometry of Catecholamine Nuclei in the Brainstem

PubMed Central

Bucci, Domenico; Busceti, Carla L.; Calierno, Maria T.; Di Pietro, Paola; Madonna, Michele; Biagioni, Francesca; Ryskalin, Larisa; Limanaqi, Fiona; Nicoletti, Ferdinando; Fornai, Francesco

2017-01-01

Catecholamine nuclei within the brainstem reticular formation (RF) play a pivotal role in a variety of brain functions. However, a systematic characterization of these nuclei in the very same experimental conditions is missing so far. Tyrosine hydroxylase (TH) immune-positive cells of the brainstem correspond to dopamine (DA)-, norepinephrine (NE)-, and epinephrine (E)-containing cells. Here, we report a systematic count of TH-positive neurons in the RF of the mouse brainstem by using stereological morphometry. All these nuclei were analyzed for anatomical localization, rostro-caudal extension, volume, neuron number, neuron density, and mean neuronal area for each nucleus. The present data apart from inherent informative value wish to represent a reference for neuronal mapping in those studies investigating the functional anatomy of the brainstem RF. These include: the sleep-wake cycle, movement control, muscle tone modulation, mood control, novelty orienting stimuli, attention, archaic responses to internal and external stressful stimuli, anxiety, breathing, blood pressure, and innumerable activities modulated by the archaic iso-dendritic hard core of the brainstem RF. Most TH-immune-positive cells fill the lateral part of the RF, which indeed possesses a high catecholamine content. A few nuclei are medial, although conventional nosography considers all these nuclei as part of the lateral column of the RF. Despite the key role of these nuclei in psychiatric and neurological disorders, only a few of them aspired a great attention in biomedical investigation, while most of them remain largely obscure although intense research is currently in progress. A simultaneous description of all these nuclei is not simply key to comprehend the variety of brainstem catecholamine reticular neurons, but probably represents an intrinsically key base for understanding brain physiology and physiopathology. PMID:29163071

Identification of Novel Small Molecule Activators of Nuclear Factor-κB With Neuroprotective Action Via High-Throughput Screening

PubMed Central

Manuvakhova, Marina S.; Johnson, Guyla G.; White, Misti C.; Ananthan, Subramaniam; Sosa, Melinda; Maddox, Clinton; McKellip, Sara; Rasmussen, Lynn; Wennerberg, Krister; Hobrath, Judith V.; White, E. Lucile; Maddry, Joseph A.; Grimaldi, Maurizio

2012-01-01

Neuronal noncytokine-dependent p50/p65 nuclear factor-κB (the primary NF-κB complex in the brain) activation has been shown to exert neuroprotective actions. Thus neuronal activation of NF-κB could represent a viable neuroprotective target. We have developed a cell-based assay able to detect NF-κB expression enhancement, and through its use we have identified small molecules able to up-regulate NF-κB expression and hence trigger its activation in neurons. We have successfully screened approximately 300,000 compounds and identified 1,647 active compounds. Cluster analysis of the structures within the hit population yielded 14 enriched chemical scaffolds. One high-potency and chemically attractive representative of each of these 14 scaffolds and four singleton structures were selected for follow-up. The experiments described here highlighted that seven compounds caused noncanonical long-lasting NF-κB activation in primary astrocytes. Molecular NF-κB docking experiments indicate that compounds could be modulating NF-κB-induced NF-κB expression via enhancement of NF-κB binding to its own promoter. Prototype compounds increased p65 expression in neurons and caused its nuclear translocation without affecting the inhibitor of NF-κB (I-κB). One of the prototypical compounds caused a large reduction of glutamate-induced neuronal death. In conclusion, we have provided evidence that we can use small molecules to activate p65 NF-κB expression in neurons in a cytokine receptor-independent manner, which results in both long-lasting p65 NF-κB translocation/activation and decreased glutamate neurotoxicity. PMID:21046675

Dendritic Slow Dynamics Enables Localized Cortical Activity to Switch between Mobile and Immobile Modes with Noisy Background Input

PubMed Central

Kurashige, Hiroki; Câteau, Hideyuki

2011-01-01

Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity – called a bump – can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability. PMID:21931635

Visual perception and imagery: a new molecular hypothesis.

PubMed

Bókkon, I

2009-05-01

Here, we put forward a redox molecular hypothesis about the natural biophysical substrate of visual perception and visual imagery. This hypothesis is based on the redox and bioluminescent processes of neuronal cells in retinotopically organized cytochrome oxidase-rich visual areas. Our hypothesis is in line with the functional roles of reactive oxygen and nitrogen species in living cells that are not part of haphazard process, but rather a very strict mechanism used in signaling pathways. We point out that there is a direct relationship between neuronal activity and the biophoton emission process in the brain. Electrical and biochemical processes in the brain represent sensory information from the external world. During encoding or retrieval of information, electrical signals of neurons can be converted into synchronized biophoton signals by bioluminescent radical and non-radical processes. Therefore, information in the brain appears not only as an electrical (chemical) signal but also as a regulated biophoton (weak optical) signal inside neurons. During visual perception, the topological distribution of photon stimuli on the retina is represented by electrical neuronal activity in retinotopically organized visual areas. These retinotopic electrical signals in visual neurons can be converted into synchronized biophoton signals by radical and non-radical processes in retinotopically organized mitochondria-rich areas. As a result, regulated bioluminescent biophotons can create intrinsic pictures (depictive representation) in retinotopically organized cytochrome oxidase-rich visual areas during visual imagery and visual perception. The long-term visual memory is interpreted as epigenetic information regulated by free radicals and redox processes. This hypothesis does not claim to solve the secret of consciousness, but proposes that the evolution of higher levels of complexity made the intrinsic picture representation of the external visual world possible by regulated redox and bioluminescent reactions in the visual system during visual perception and visual imagery.

Utilisation d'images aeroportees a tres haute resolution spatiale pour l'estimation de la vigueur des peuplements forestiers du nord-ouest du Nouveau-Brunswick

NASA Astrophysics Data System (ADS)

Louis, Ognel Pierre

Le but de cette etude est de developper un outil permettant d'estimer le niveau de risque de perte de vigueur des peuplements forestiers de la region de Gounamitz au nord-ouest du Nouveau-Brunswick via des donnees d'inventaires forestiers et des donnees de teledetection. Pour ce faire, un marteloscope de 100m x 100m et 20 parcelles d'echantillonnages ont ete delimites. A l'interieur de ces derniers, le niveau de risque de perte de vigueur des arbres ayant un DHP superieur ou egal a 9 cm a ete determine. Afin de caracteriser le risque de perte de vigueur des arbres, leurs positions spatiales ont ete repertoriees a partir d'un GPS en tenant compte des defauts au niveau des tiges. Pour mener a bien ce travail, les indices de vegetation et de textures et les bandes spectrales de l'image aeroportee ont ete extraits et consideres comme variables independantes. Le niveau de risque de perte de vigueur obtenu par espece d'arbre a travers les inventaires forestiers a ete considere comme variable dependante. En vue d'obtenir la superficie des peuplements forestiers de la region d'etude, une classification dirigee des images a partir de l'algorithme maximum de vraisemblance a ete effectuee. Le niveau de risque de perte de vigueur par type d'arbre a ensuite ete estime a l'aide des reseaux de neurones en utilisant un reseau dit perceptron multicouches. Il s'agit d'un modele de reseau de neurones compose de : 11 neurones sur la couche d'entree, correspondant aux variables independantes, 35 neurones sur la couche cachee et 4 neurones sur la couche de sortie. La prediction a partir des reseaux de neurones produit une matrice de confusion qui permet d'obtenir des mesures quantitatives d'estimation, notamment un pourcentage de classification globale de 91,7% pour la prediction du risque de perte de vigueur du peuplement de resineux et de 89,7% pour celui du peuplement de feuillus. L'evaluation de la performance des reseaux de neurones fournit une valeur de MSE globale de 0,04, et une RMSE (Mean Square Error) globale de 0,20 pour le peuplement de feuillus. Quant au peuplement de resineux, une valeur de MSE (Mean Square Error) globale de 0,05 et une valeur de RMSE globale de 0,22 ont ete obtenues. Pour la validation des resultats, le niveau de risque de perte de vigueur predit a ete compare avec le risque de perte de vigueur de reference. Les resultats obtenus donnent un coefficient de determination de 0,98 pour le peuplement de feuillus et 0,93 pour le peuplement de resineux.

Investigating Bacterial Sources of Toxicity as an Environmental Contributor to Dopaminergic Neurodegeneration

PubMed Central

Caldwell, Kim A.; Tucci, Michelle L.; Armagost, Jafa; Hodges, Tyler W.; Chen, Jue; Memon, Shermeen B.; Blalock, Jeana E.; DeLeon, Susan M.; Findlay, Robert H.; Ruan, Qingmin; Webber, Philip J.; Standaert, David G.; Olson, Julie B.; Caldwell, Guy A.

2009-01-01

Parkinson disease (PD) involves progressive neurodegeneration, including loss of dopamine (DA) neurons from the substantia nigra. Select genes associated with rare familial forms of PD function in cellular pathways, such as the ubiquitin-proteasome system (UPS), involved in protein degradation. The misfolding and accumulation of proteins, such as α-synuclein, into inclusions termed Lewy Bodies represents a clinical hallmark of PD. Given the predominance of sporadic PD among patient populations, environmental toxins may induce the disease, although their nature is largely unknown. Thus, an unmet challenge surrounds the discovery of causal or contributory neurotoxic factors that could account for the prevalence of sporadic PD. Bacteria within the order Actinomycetales are renowned for their robust production of secondary metabolites and might represent unidentified sources of environmental exposures. Among these, the aerobic genera, Streptomyces, produce natural proteasome inhibitors that block protein degradation and may potentially damage DA neurons. Here we demonstrate that a metabolite produced by a common soil bacterium, S. venezuelae, caused DA neurodegeneration in the nematode, Caenorhabditis elegans, which increased as animals aged. This metabolite, which disrupts UPS function, caused gradual degeneration of all neuronal classes examined, however DA neurons were particularly vulnerable to exposure. The presence of DA exacerbated toxicity because neurodegeneration was attenuated in mutant nematodes depleted for tyrosine hydroxylase (TH), the rate-limiting enzyme in DA production. Strikingly, this factor caused dose-dependent death of human SH-SY5Y neuroblastoma cells, a dopaminergic line. Efforts to purify the toxic activity revealed that it is a highly stable, lipophilic, and chemically unique small molecule. Evidence of a robust neurotoxic factor that selectively impacts neuronal survival in a progressive yet moderate manner is consistent with the etiology of age-associated neurodegenerative diseases. Collectively, these data suggest the potential for exposures to the metabolites of specific common soil bacteria to possibly represent a contributory environmental component to PD. PMID:19806188

Reward-dependent learning in neuronal networks for planning and decision making.

PubMed

Dehaene, S; Changeux, J P

2000-01-01

Neuronal network models have been proposed for the organization of evaluation and decision processes in prefrontal circuitry and their putative neuronal and molecular bases. The models all include an implementation and simulation of an elementary reward mechanism. Their central hypothesis is that tentative rules of behavior, which are coded by clusters of active neurons in prefrontal cortex, are selected or rejected based on an evaluation by this reward signal, which may be conveyed, for instance, by the mesencephalic dopaminergic neurons with which the prefrontal cortex is densely interconnected. At the molecular level, the reward signal is postulated to be a neurotransmitter such as dopamine, which exerts a global modulatory action on prefrontal synaptic efficacies, either via volume transmission or via targeted synaptic triads. Negative reinforcement has the effect of destabilizing the currently active rule-coding clusters; subsequently, spontaneous activity varies again from one cluster to another, giving the organism the chance to discover and learn a new rule. Thus, reward signals function as effective selection signals that either maintain or suppress currently active prefrontal representations as a function of their current adequacy. Simulations of this variation-selection have successfully accounted for the main features of several major tasks that depend on prefrontal cortex integrity, such as the delayed-response test, the Wisconsin card sorting test, the Tower of London test and the Stroop test. For the more complex tasks, we have found it necessary to supplement the external reward input with a second mechanism that supplies an internal reward; it consists of an auto-evaluation loop which short-circuits the reward input from the exterior. This allows for an internal evaluation of covert motor intentions without actualizing them as behaviors, by simply testing them covertly by comparison with memorized former experiences. This element of architecture gives access to enhanced rates of learning via an elementary process of internal or covert mental simulation. We have recently applied these ideas to a new model, developed with M. Kerszberg, which hypothesizes that prefrontal cortex and its reward-related connections contribute crucially to conscious effortful tasks. This model distinguishes two main computational spaces within the human brain: a unique global workspace composed of distributed and heavily interconnected neurons with long-range axons, and a set of specialized and modular perceptual, motor, memory, evaluative and attentional processors. We postulate that workspace neurons are mobilized in effortful tasks for which the specialized processors do not suffice; they selectively mobilize or suppress, through descending connections, the contribution of specific processor neurons. In the course of task performance, workspace neurons become spontaneously co-activated, forming discrete though variable spatio-temporal patterns subject to modulation by vigilance signals and to selection by reward signals. A computer simulation of the Stroop task shows workspace activation to increase during acquisition of a novel task, effortful execution, and after errors. This model makes predictions concerning the spatio-temporal activation patterns during brain imaging of cognitive tasks, particularly concerning the conditions of activation of dorsolateral prefrontal cortex and anterior cingulate, their relation to reward mechanisms, and their specific reaction during error processing.

A Map of Anticipatory Activity in Mouse Motor Cortex.

PubMed

Chen, Tsai-Wen; Li, Nuo; Daie, Kayvon; Svoboda, Karel

2017-05-17

Activity in the mouse anterior lateral motor cortex (ALM) instructs directional movements, often seconds before movement initiation. It is unknown whether this preparatory activity is localized to ALM or widely distributed within motor cortex. Here we imaged activity across motor cortex while mice performed a whisker-based object localization task with a delayed, directional licking response. During tactile sensation and the delay epoch, object location was represented in motor cortex areas that are medial and posterior relative to ALM, including vibrissal motor cortex. Preparatory activity appeared first in deep layers of ALM, seconds before the behavioral response, and remained localized to ALM until the behavioral response. Later, widely distributed neurons represented the outcome of the trial. Cortical area was more predictive of neuronal selectivity than laminar location or axonal projection target. Motor cortex therefore represents sensory, motor, and outcome information in a spatially organized manner. Copyright © 2017 Elsevier Inc. All rights reserved.

Recapitulation of spinal motor neuron-specific disease phenotypes in a human cell model of spinal muscular atrophy

PubMed Central

Wang, Zhi-Bo; Zhang, Xiaoqing; Li, Xue-Jun

2013-01-01

Establishing human cell models of spinal muscular atrophy (SMA) to mimic motor neuron-specific phenotypes holds the key to understanding the pathogenesis of this devastating disease. Here, we developed a closely representative cell model of SMA by knocking down the disease-determining gene, survival motor neuron (SMN), in human embryonic stem cells (hESCs). Our study with this cell model demonstrated that knocking down of SMN does not interfere with neural induction or the initial specification of spinal motor neurons. Notably, the axonal outgrowth of spinal motor neurons was significantly impaired and these disease-mimicking neurons subsequently degenerated. Furthermore, these disease phenotypes were caused by SMN-full length (SMN-FL) but not SMN-Δ7 (lacking exon 7) knockdown, and were specific to spinal motor neurons. Restoring the expression of SMN-FL completely ameliorated all of the disease phenotypes, including specific axonal defects and motor neuron loss. Finally, knockdown of SMN-FL led to excessive mitochondrial oxidative stress in human motor neuron progenitors. The involvement of oxidative stress in the degeneration of spinal motor neurons in the SMA cell model was further confirmed by the administration of N-acetylcysteine, a potent antioxidant, which prevented disease-related apoptosis and subsequent motor neuron death. Thus, we report here the successful establishment of an hESC-based SMA model, which exhibits disease gene isoform specificity, cell type specificity, and phenotype reversibility. Our model provides a unique paradigm for studying how motor neurons specifically degenerate and highlights the potential importance of antioxidants for the treatment of SMA. PMID:23208423

Energy-efficient neural information processing in individual neurons and neuronal networks.

PubMed

Yu, Lianchun; Yu, Yuguo

2017-11-01

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Parvalbumin+ Neurons and Npas1+ Neurons Are Distinct Neuron Classes in the Mouse External Globus Pallidus

PubMed Central

Hernández, Vivian M.; Hegeman, Daniel J.; Cui, Qiaoling; Kelver, Daniel A.; Fiske, Michael P.; Glajch, Kelly E.; Pitt, Jason E.; Huang, Tina Y.; Justice, Nicholas J.

2015-01-01

Compelling evidence suggests that pathological activity of the external globus pallidus (GPe), a nucleus in the basal ganglia, contributes to the motor symptoms of a variety of movement disorders such as Parkinson's disease. Recent studies have challenged the idea that the GPe comprises a single, homogenous population of neurons that serves as a simple relay in the indirect pathway. However, we still lack a full understanding of the diversity of the neurons that make up the GPe. Specifically, a more precise classification scheme is needed to better describe the fundamental biology and function of different GPe neuron classes. To this end, we generated a novel multicistronic BAC (bacterial artificial chromosome) transgenic mouse line under the regulatory elements of the Npas1 gene. Using a combinatorial transgenic and immunohistochemical approach, we discovered that parvalbumin-expressing neurons and Npas1-expressing neurons in the GPe represent two nonoverlapping cell classes, amounting to 55% and 27% of the total GPe neuron population, respectively. These two genetically identified cell classes projected primarily to the subthalamic nucleus and to the striatum, respectively. Additionally, parvalbumin-expressing neurons and Npas1-expressing neurons were distinct in their autonomous and driven firing characteristics, their expression of intrinsic ion conductances, and their responsiveness to chronic 6-hydroxydopamine lesion. In summary, our data argue that parvalbumin-expressing neurons and Npas1-expressing neurons are two distinct functional classes of GPe neurons. This work revises our understanding of the GPe, and provides the foundation for future studies of its function and dysfunction. SIGNIFICANCE STATEMENT Until recently, the heterogeneity of the constituent neurons within the external globus pallidus (GPe) was not fully appreciated. We addressed this knowledge gap by discovering two principal GPe neuron classes, which were identified by their nonoverlapping expression of the markers parvalbumin and Npas1. Our study provides evidence that parvalbumin and Npas1 neurons have different topologies within the basal ganglia. PMID:26311767

Parvalbumin+ Neurons and Npas1+ Neurons Are Distinct Neuron Classes in the Mouse External Globus Pallidus.

PubMed

Hernández, Vivian M; Hegeman, Daniel J; Cui, Qiaoling; Kelver, Daniel A; Fiske, Michael P; Glajch, Kelly E; Pitt, Jason E; Huang, Tina Y; Justice, Nicholas J; Chan, C Savio

2015-08-26

Compelling evidence suggests that pathological activity of the external globus pallidus (GPe), a nucleus in the basal ganglia, contributes to the motor symptoms of a variety of movement disorders such as Parkinson's disease. Recent studies have challenged the idea that the GPe comprises a single, homogenous population of neurons that serves as a simple relay in the indirect pathway. However, we still lack a full understanding of the diversity of the neurons that make up the GPe. Specifically, a more precise classification scheme is needed to better describe the fundamental biology and function of different GPe neuron classes. To this end, we generated a novel multicistronic BAC (bacterial artificial chromosome) transgenic mouse line under the regulatory elements of the Npas1 gene. Using a combinatorial transgenic and immunohistochemical approach, we discovered that parvalbumin-expressing neurons and Npas1-expressing neurons in the GPe represent two nonoverlapping cell classes, amounting to 55% and 27% of the total GPe neuron population, respectively. These two genetically identified cell classes projected primarily to the subthalamic nucleus and to the striatum, respectively. Additionally, parvalbumin-expressing neurons and Npas1-expressing neurons were distinct in their autonomous and driven firing characteristics, their expression of intrinsic ion conductances, and their responsiveness to chronic 6-hydroxydopamine lesion. In summary, our data argue that parvalbumin-expressing neurons and Npas1-expressing neurons are two distinct functional classes of GPe neurons. This work revises our understanding of the GPe, and provides the foundation for future studies of its function and dysfunction. Until recently, the heterogeneity of the constituent neurons within the external globus pallidus (GPe) was not fully appreciated. We addressed this knowledge gap by discovering two principal GPe neuron classes, which were identified by their nonoverlapping expression of the markers parvalbumin and Npas1. Our study provides evidence that parvalbumin and Npas1 neurons have different topologies within the basal ganglia. Copyright © 2015 the authors 0270-6474/15/3511830-18$15.00/0.

Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform

PubMed Central

Bai, Guanbing; Liu, Jinghong; Song, Yueming; Zuo, Yujia

2017-01-01

To address the limitation of the existing UAV (unmanned aerial vehicles) photoelectric localization method used for moving objects, this paper proposes an improved two-UAV intersection localization system based on airborne optoelectronic platforms by using the crossed-angle localization method of photoelectric theodolites for reference. This paper introduces the makeup and operating principle of intersection localization system, creates auxiliary coordinate systems, transforms the LOS (line of sight, from the UAV to the target) vectors into homogeneous coordinates, and establishes a two-UAV intersection localization model. In this paper, the influence of the positional relationship between UAVs and the target on localization accuracy has been studied in detail to obtain an ideal measuring position and the optimal localization position where the optimal intersection angle is 72.6318°. The result shows that, given the optimal position, the localization root mean square error (RMS) will be 25.0235 m when the target is 5 km away from UAV baselines. Finally, the influence of modified adaptive Kalman filtering on localization results is analyzed, and an appropriate filtering model is established to reduce the localization RMS error to 15.7983 m. Finally, An outfield experiment was carried out and obtained the optimal results: σB=1.63×10−4 (°), σL=1.35×10−4 (°), σH=15.8 (m), σsum=27.6 (m), where σB represents the longitude error, σL represents the latitude error, σH represents the altitude error, and σsum represents the error radius. PMID:28067814

Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform.

PubMed

Bai, Guanbing; Liu, Jinghong; Song, Yueming; Zuo, Yujia

2017-01-06

To address the limitation of the existing UAV (unmanned aerial vehicles) photoelectric localization method used for moving objects, this paper proposes an improved two-UAV intersection localization system based on airborne optoelectronic platforms by using the crossed-angle localization method of photoelectric theodolites for reference. This paper introduces the makeup and operating principle of intersection localization system, creates auxiliary coordinate systems, transforms the LOS (line of sight, from the UAV to the target) vectors into homogeneous coordinates, and establishes a two-UAV intersection localization model. In this paper, the influence of the positional relationship between UAVs and the target on localization accuracy has been studied in detail to obtain an ideal measuring position and the optimal localization position where the optimal intersection angle is 72.6318°. The result shows that, given the optimal position, the localization root mean square error (RMS) will be 25.0235 m when the target is 5 km away from UAV baselines. Finally, the influence of modified adaptive Kalman filtering on localization results is analyzed, and an appropriate filtering model is established to reduce the localization RMS error to 15.7983 m. Finally, An outfield experiment was carried out and obtained the optimal results: σ B = 1.63 × 10 - 4 ( ° ) , σ L = 1.35 × 10 - 4 ( ° ) , σ H = 15.8 ( m ) , σ s u m = 27.6 ( m ) , where σ B represents the longitude error, σ L represents the latitude error, σ H represents the altitude error, and σ s u m represents the error radius.

An integrated analog O/E/O link for multi-channel laser neurons

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

Nahmias, Mitchell A., E-mail: mnahmias@princeton.edu; Tait, Alexander N.; Tolias, Leonidas

2016-04-11

We demonstrate an analog O/E/O electronic link to allow integrated laser neurons to accept many distinguishable, high bandwidth input signals simultaneously. This device utilizes wavelength division multiplexing to achieve multi-channel fan-in, a photodetector to sum signals together, and a laser cavity to perform a nonlinear operation. Its speed outpaces accelerated-time neuromorphic electronics, and it represents a viable direction towards scalable networking approaches.

Sex-switching of the Drosophila brain by two antagonistic chromatin factors

PubMed Central

Ito, Hiroki; Sato, Kosei; Yamamoto, Daisuke

2013-01-01

In Drosophila melanogaster, the fruitless (fru) gene encoding BTB-Zn-finger transcription factors organizes male sexual behavior by controlling the development of sexually dimorphic neuronal circuitry. However, the molecular mechanism by which fru controls the sexual fate of neurons has been unknown. Our recent study represents a first step toward clarification of this mechanism. We have shown that: (1) Fru forms a complex with the transcriptional cofactor Bonus (Bon), which recruits either of two chromatin regulators, Histone deacetylase 1 (HDAC1) or Heterochromatin protein 1a (HP1a), to Fru-target sites; (2) the Fru-Bon complex has a masculinizing effect on single sexually-dimorphic neurons when it recruits HDAC1, whereas it has a demasculinizing effect when it recruits HP1a; (3) HDAC1 or HP1a thus recruited to Fru-target sites determines the sexual fate of single neurons in an all-or-none manner, as manipulations of HDAC1 or HP1a expression levels affect the proportion of male-typical neurons and female-typical neurons without producing neurons of intersexual characteristics. Here, we hypothesize that chromatin landscape changes induced by ecdysone surges direct the HDAC1- or HP1a-containing Fru complex to distinct targets, thereby allowing them to switch the neuronal sexual fate in the brain. PMID:23519136

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.

Glucose-monitoring neurons in the mediodorsal prefrontal cortex.

PubMed

Nagy, Bernadett; Szabó, István; Papp, Szilárd; Takács, Gábor; Szalay, Csaba; Karádi, Zoltán

2012-03-20

The mediodorsal prefrontal cortex (mdPFC), a key structure of the limbic neural circuitry, plays important roles in the central regulation of feeding. As an integrant part of the forebrain dopamine (DA) system, it performs complex roles via interconnections with various brain areas where glucose-monitoring (GM) neurons have been identified. The main goal of the present experiments was to examine whether similar GM neurons exist in the mediodorsal prefrontal cortex. To search for such chemosensory cells here, and to estimate their involvement in the DA circuitry, extracellular single neuron activity of the mediodorsal prefrontal cortex of anesthetized Wistar and Sprague-Dawley rats was recorded by means of tungsten wire multibarreled glass microelectrodes during microelectrophoretic administration of d-glucose and DA. One fourth of the neurons tested changed in firing rate in response to glucose, thus, proved to be elements of the forebrain GM neural network. DA responsive neurons in the mdPFC were found to represent similar proportion of all cells; the glucose-excited units were shown to display excitatory whereas the glucose-inhibited neurons were demonstrated to exert mainly inhibitory responses to dopamine. The glucose-monitoring neurons of the mdPFC and their distinct DA sensitivity are suggested to be of particular significance in adaptive processes of the central feeding control. Copyright © 2012 Elsevier B.V. All rights reserved.

Cultured networks of excitatory projection neurons and inhibitory interneurons for studying human cortical neurotoxicity

PubMed Central

Xu, Jin-Chong; Fan, Jing; Wang, Xueqing; Eacker, Stephen M.; Kam, Tae-In; Chen, Li; Yin, Xiling; Zhu, Juehua; Chi, Zhikai; Jiang, Haisong; Chen, Rong; Dawson, Ted M.; Dawson, Valina L.

2017-01-01

Translating neuroprotective treatments from discovery in cell and animal models to the clinic has proven challenging. To reduce the gap between basic studies of neurotoxicity and neuroprotection and clinically relevant therapies, we developed a human cortical neuron culture system from human embryonic stem cells (ESCs) or inducible pluripotent stem cells (iPSCs) that generated both excitatory and inhibitory neuronal networks resembling the composition of the human cortex. This methodology used timed administration of retinoic acid (RA) to FOXG1 neural precursor cells leading to differentiation of neuronal populations representative of the six cortical layers with both excitatory and inhibitory neuronal networks that were functional and homeostatically stable. In human cortical neuron cultures, excitotoxicity or ischemia due to oxygen and glucose deprivation led to cell death that was dependent on N-methyl-D-aspartate (NMDA) receptors, nitric oxide (NO), and the poly (ADP-ribose) polymerase (PARP)-dependent cell death, a cell death pathway designated parthanatos to separate it from apoptosis, necroptosis and other forms of cell death. Neuronal cell death was attenuated by PARP inhibitors that are currently in clinical trials for cancer treatment. This culture system provides a new platform for the study of human cortical neurotoxicity and suggests that PARP inhibitors may be useful for ameliorating excitotoxic and ischemic cell death in human neurons. PMID:27053772

Chemical structure and morphology of dorsal root ganglion neurons from naive and inflamed mice.

PubMed

Barabas, Marie E; Mattson, Eric C; Aboualizadeh, Ebrahim; Hirschmugl, Carol J; Stucky, Cheryl L

2014-12-05

Fourier transform infrared spectromicroscopy provides label-free imaging to detect the spatial distribution of the characteristic functional groups in proteins, lipids, phosphates, and carbohydrates simultaneously in individual DRG neurons. We have identified ring-shaped distributions of lipid and/or carbohydrate enrichment in subpopulations of neurons which has never before been reported. These distributions are ring-shaped within the cytoplasm and are likely representative of the endoplasmic reticulum. The prevalence of chemical ring subtypes differs between large- and small-diameter neurons. Peripheral inflammation increased the relative lipid content specifically in small-diameter neurons, many of which are nociceptive. Because many small-diameter neurons express an ion channel involved in inflammatory pain, transient receptor potential ankyrin 1 (TRPA1), we asked whether this increase in lipid content occurs in TRPA1-deficient (knock-out) neurons. No statistically significant change in lipid content occurred in TRPA1-deficient neurons, indicating that the inflammation-mediated increase in lipid content is largely dependent on TRPA1. Because TRPA1 is known to mediate mechanical and cold sensitization that accompanies peripheral inflammation, our findings may have important implications for a potential role of lipids in inflammatory pain. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

Pericellular innervation of neurons expressing abnormally hyperphosphorylated tau in the hippocampal formation of Alzheimer's disease patients.

PubMed

Blazquez-Llorca, Lidia; Garcia-Marin, Virginia; Defelipe, Javier

2010-01-01

Neurofibrillary tangles (NFT) represent one of the main neuropathological features in the cerebral cortex associated with Alzheimer's disease (AD). This neurofibrillary lesion involves the accumulation of abnormally hyperphosphorylated or abnormally phosphorylated microtubule-associated protein tau into paired helical filaments (PHF-tau) within neurons. We have used immunocytochemical techniques and confocal microscopy reconstructions to examine the distribution of PHF-tau-immunoreactive (ir) cells, and their perisomatic GABAergic and glutamatergic innervations in the hippocampal formation and adjacent cortex of AD patients. Furthermore, correlative light and electron microscopy was employed to examine these neurons and the perisomatic synapses. We observed two patterns of staining in PHF-tau-ir neurons, pattern I (without NFT) and pattern II (with NFT), the distribution of which varies according to the cortical layer and area. Furthermore, the distribution of both GABAergic and glutamatergic terminals around the soma and proximal processes of PHF-tau-ir neurons does not seem to be altered as it is indistinguishable from both control cases and from adjacent neurons that did not contain PHF-tau. At the electron microscope level, a normal looking neuropil with typical symmetric and asymmetric synapses was observed around PHF-tau-ir neurons. These observations suggest that the synaptic connectivity around the perisomatic region of these PHF-tau-ir neurons was apparently unaltered.

Response to ‘comment on recent modeling studies of astrocyte–neuron metabolic interactions': much ado about nothing

PubMed Central

Mangia, Silvia; DiNuzzo, Mauro; Giove, Federico; Carruthers, Anthony; Simpson, Ian A; Vannucci, Susan J

2011-01-01

For many years, a tenet of cerebral metabolism held that glucose was the obligate energy substrate of the mammalian brain and that neuronal oxidative metabolism represented the majority of this glucose utilization. In 1994, Pellerin and Magistretti formulated the astrocyte–neuron lactate shuttle (ANLS) hypothesis, in which astrocytes, not neurons, metabolized glucose, with subsequent transport of the glycolytically derived lactate to fuel the energy needs of the neuron during neurotransmission. By considering the concentrations and kinetic characteristics of the nutrient transporter proteins, Simpson et al later supported the opposite view, in which lactate flows from neurons to astrocytes, thus leading to the neuron–astrocyte lactate shuttle (NALS). Most recently, a commentary was published in this journal attempting to discredit the NALS. This challenge has stimulated the present response in which we detail the inaccuracies of the commentary and further model several different possibilities. Although our simulations continue to support the predominance of neuronal glucose utilization during activation and neuronal to astrocytic lactate flow, the most important result is that, regardless of the direction of the flow, the overall contribution of lactate to cerebral glucose metabolism is found to be so small as to make this ongoing debate ‘much ado about nothing'. PMID:21427731

Modulation of spike coding by subthreshold extracellular electric fields and neuronal morphology

NASA Astrophysics Data System (ADS)

Wei, Xile; Li, Bingjie; Lu, Meili; Yi, Guosheng; Wang, Jiang

2015-07-01

We use a two-compartment model, which includes soma and dendrite, to explore how extracellular subthreshold sinusoidal electric fields (EFs) influence the spike coding of an active neuron. By changing the intensity and the frequency of subthreshold EFs, we find that subthreshold EFs indeed affect neuronal coding remarkably within several stimulus frequency windows where the field effects on spike timing are stronger than that on spiking rate. The field effects are maximized at several harmonics of the intrinsic spiking frequency of an active neuron. Our findings implicate the potential resonance mechanism underlying subthreshold field effects. We also discuss how neuronal morphologic properties constrain subthreshold EF effects on spike timing. The morphologic properties are represented by two parameters, gc and p, where gc is the internal conductance between soma and dendrite and geometric factor p characterizes the proportion of area occupied by soma. We find that the contribution to field effects from the variation of p is stronger than that from gc, which suggests that neuronal geometric features play a crucial role in subthreshold field effects. Theoretically, these insights into how subthreshold sinusoidal EFs modulate ongoing neuron behaviors could contribute to uncovering the relevant mechanism of subthreshold sinusoidal EFs effects on neuronal coding. Furthermore, they are useful in rationally designing noninvasive brain stimulation strategies and developing electromagnetic stimulus techniques.

Estimating Fast Neural Input Using Anatomical and Functional Connectivity

PubMed Central

Eriksson, David

2016-01-01

In the last 20 years there has been an increased interest in estimating signals that are sent between neurons and brain areas. During this time many new methods have appeared for measuring those signals. Here we review a wide range of methods for which connected neurons can be identified anatomically, by tracing axons that run between the cells, or functionally, by detecting if the activity of two neurons are correlated with a short lag. The signals that are sent between the neurons are represented by the activity in the neurons that are connected to the target population or by the activity at the corresponding synapses. The different methods not only differ in the accuracy of the signal measurement but they also differ in the type of signal being measured. For example, unselective recording of all neurons in the source population encompasses more indirect pathways to the target population than if one selectively record from the neurons that project to the target population. Infact, this degree of selectivity is similar to that of optogenetic perturbations; one can perturb selectively or unselectively. Thus it becomes possible to match a given signal measurement method with a signal perturbation method, something that allows for an exact input control to any neuronal population. PMID:28066189

Classification of neocortical interneurons using affinity propagation.

PubMed

Santana, Roberto; McGarry, Laura M; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael

2013-01-01

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

Induction of human umbilical Wharton's jelly-derived mesenchymal stem cells toward motor neuron-like cells.

PubMed

Bagher, Zohreh; Ebrahimi-Barough, Somayeh; Azami, Mahmoud; Mirzadeh, Hamid; Soleimani, Mansooreh; Ai, Jafar; Nourani, Mohammad Reza; Joghataei, Mohammad Taghi

2015-10-01

The most important property of stem cells from different sources is the capacity to differentiate into various cells and tissue types. However, problems including contamination, normal karyotype, and ethical issues cause many limitations in obtaining and using these cells from different sources. The cells in Wharton's jelly region of umbilical cord represent a pool source of primitive cells with properties of mesenchymal stem cells (MSCs). The aim of this study was to determine the potential of human Wharton's jelly-derived mesenchymal stem cells (WJMSCs) for differentiation to motor neuron cells. WJMSCs were induced to differentiate into motor neuron-like cells by using different signaling molecules and neurotrophic factors in vitro. Differentiated neurons were then characterized for expression of motor neuron markers including nestin, PAX6, NF-H, Islet 1, HB9, and choline acetyl transferase (ChAT) by quantitative reverse transcription PCR and immunocytochemistry. Our results showed that differentiated WJMSCs could significantly express motor neuron biomarkers in RNA and protein levels 15 d post induction. These results suggested that WJMSCs can differentiate to motor neuron-like cells and might provide a potential source in cell therapy for neurodegenerative disease.

Supranormal orientation selectivity of visual neurons in orientation-restricted animals.

PubMed

Sasaki, Kota S; Kimura, Rui; Ninomiya, Taihei; Tabuchi, Yuka; Tanaka, Hiroki; Fukui, Masayuki; Asada, Yusuke C; Arai, Toshiya; Inagaki, Mikio; Nakazono, Takayuki; Baba, Mika; Kato, Daisuke; Nishimoto, Shinji; Sanada, Takahisa M; Tani, Toshiki; Imamura, Kazuyuki; Tanaka, Shigeru; Ohzawa, Izumi

2015-11-16

Altered sensory experience in early life often leads to remarkable adaptations so that humans and animals can make the best use of the available information in a particular environment. By restricting visual input to a limited range of orientations in young animals, this investigation shows that stimulus selectivity, e.g., the sharpness of tuning of single neurons in the primary visual cortex, is modified to match a particular environment. Specifically, neurons tuned to an experienced orientation in orientation-restricted animals show sharper orientation tuning than neurons in normal animals, whereas the opposite was true for neurons tuned to non-experienced orientations. This sharpened tuning appears to be due to elongated receptive fields. Our results demonstrate that restricted sensory experiences can sculpt the supranormal functions of single neurons tailored for a particular environment. The above findings, in addition to the minimal population response to orientations close to the experienced one, agree with the predictions of a sparse coding hypothesis in which information is represented efficiently by a small number of activated neurons. This suggests that early brain areas adopt an efficient strategy for coding information even when animals are raised in a severely limited visual environment where sensory inputs have an unnatural statistical structure.

Upregulation of Ih expressed in IB4-negative Aδ nociceptive DRG neurons contributes to mechanical hypersensitivity associated with cervical radiculopathic pain

PubMed Central

Liu, Da-Lu; Lu, Na; Han, Wen-Juan; Chen, Rong-Gui; Cong, Rui; Xie, Rou-Gang; Zhang, Yu-Fei; Kong, Wei-Wei; Hu, San-Jue; Luo, Ceng

2015-01-01

Cervical radiculopathy represents aberrant mechanical hypersensitivity. Primary sensory neuron’s ability to sense mechanical force forms mechanotransduction. However, whether this property undergoes activity-dependent plastic changes and underlies mechanical hypersensitivity associated with cervical radiculopathic pain (CRP) is not clear. Here we show a new CRP model producing stable mechanical compression of dorsal root ganglion (DRG), which induces dramatic behavioral mechanical hypersensitivity. Amongst nociceptive DRG neurons, a mechanically sensitive neuron, isolectin B4 negative Aδ-type (IB4− Aδ) DRG neuron displays spontaneous activity with hyperexcitability after chronic compression of cervical DRGs. Focal mechanical stimulation on somata of IB4- Aδ neuron induces abnormal hypersensitivity. Upregulated HCN1 and HCN3 channels and increased Ih current on this subset of primary nociceptors underlies the spontaneous activity together with neuronal mechanical hypersensitivity, which further contributes to the behavioral mechanical hypersensitivity associated with CRP. This study sheds new light on the functional plasticity of a specific subset of nociceptive DRG neurons to mechanical stimulation and reveals a novel mechanism that could underlie the mechanical hypersensitivity associated with cervical radiculopathy. PMID:26577374

[Single and Network Neuron Activity of Subthalamic Nucleus at Impulsive and Delayed (Self-Control) Reactions in Choice Behavior].

PubMed

Sidorina, V V; Gerasimova, Yu A; Kuleshova, E P; Merzhanova, G Kh

2015-01-01

During our experiments on cats was investigated the subthalamic neuron activity at different types of behavior in case of reinforcement choice depending on its value and availability. In chronic experiences the multiunit activity in subthalamic nucleus (STN) and orbitofrontal cortex (FC) has been recorded. Multiunit activity was analyzed over frequency and network properties of spikes. It was shown, that STN neurons reaction to different reinforcements and conditional stimulus at short- or long-delay reactions was represented by increasing or decreasing of frequency of single neurons. However the same STN neu- rons responded with increasing of frequency of single neuron during expectation of mix-bread-meat and decreasing--during the meat expectation. It has been revealed, that the number of STN interneuron interactions was authentic more at impulsive behavior than at self-control choice of behavior. The number of interactions between FC and STN neurons within intervals of 0-30 Ms was authentic more at display impulsive than during self-control behavior. These results suppose that FC and STN neurons participate in integration of reinforcement estimation; and distinctions in a choice of behavior are defined by the local and distributed interneuron interactions of STN and FC.

Supranormal orientation selectivity of visual neurons in orientation-restricted animals

PubMed Central

Sasaki, Kota S.; Kimura, Rui; Ninomiya, Taihei; Tabuchi, Yuka; Tanaka, Hiroki; Fukui, Masayuki; Asada, Yusuke C.; Arai, Toshiya; Inagaki, Mikio; Nakazono, Takayuki; Baba, Mika; Kato, Daisuke; Nishimoto, Shinji; Sanada, Takahisa M.; Tani, Toshiki; Imamura, Kazuyuki; Tanaka, Shigeru; Ohzawa, Izumi

2015-01-01

Altered sensory experience in early life often leads to remarkable adaptations so that humans and animals can make the best use of the available information in a particular environment. By restricting visual input to a limited range of orientations in young animals, this investigation shows that stimulus selectivity, e.g., the sharpness of tuning of single neurons in the primary visual cortex, is modified to match a particular environment. Specifically, neurons tuned to an experienced orientation in orientation-restricted animals show sharper orientation tuning than neurons in normal animals, whereas the opposite was true for neurons tuned to non-experienced orientations. This sharpened tuning appears to be due to elongated receptive fields. Our results demonstrate that restricted sensory experiences can sculpt the supranormal functions of single neurons tailored for a particular environment. The above findings, in addition to the minimal population response to orientations close to the experienced one, agree with the predictions of a sparse coding hypothesis in which information is represented efficiently by a small number of activated neurons. This suggests that early brain areas adopt an efficient strategy for coding information even when animals are raised in a severely limited visual environment where sensory inputs have an unnatural statistical structure. PMID:26567927

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Asymmetric generalization in adaptation to target displacement errors in humans and in a neural network model.

PubMed

Westendorff, Stephanie; Kuang, Shenbing; Taghizadeh, Bahareh; Donchin, Opher; Gail, Alexander

2015-04-01

Different error signals can induce sensorimotor adaptation during visually guided reaching, possibly evoking different neural adaptation mechanisms. Here we investigate reach adaptation induced by visual target errors without perturbing the actual or sensed hand position. We analyzed the spatial generalization of adaptation to target error to compare it with other known generalization patterns and simulated our results with a neural network model trained to minimize target error independent of prediction errors. Subjects reached to different peripheral visual targets and had to adapt to a sudden fixed-amplitude displacement ("jump") consistently occurring for only one of the reach targets. Subjects simultaneously had to perform contralateral unperturbed saccades, which rendered the reach target jump unnoticeable. As a result, subjects adapted by gradually decreasing reach errors and showed negative aftereffects for the perturbed reach target. Reach errors generalized to unperturbed targets according to a translational rather than rotational generalization pattern, but locally, not globally. More importantly, reach errors generalized asymmetrically with a skewed generalization function in the direction of the target jump. Our neural network model reproduced the skewed generalization after adaptation to target jump without having been explicitly trained to produce a specific generalization pattern. Our combined psychophysical and simulation results suggest that target jump adaptation in reaching can be explained by gradual updating of spatial motor goal representations in sensorimotor association networks, independent of learning induced by a prediction-error about the hand position. The simulations make testable predictions about the underlying changes in the tuning of sensorimotor neurons during target jump adaptation. Copyright © 2015 the American Physiological Society.

Asymmetric generalization in adaptation to target displacement errors in humans and in a neural network model

PubMed Central

Westendorff, Stephanie; Kuang, Shenbing; Taghizadeh, Bahareh; Donchin, Opher

2015-01-01

Different error signals can induce sensorimotor adaptation during visually guided reaching, possibly evoking different neural adaptation mechanisms. Here we investigate reach adaptation induced by visual target errors without perturbing the actual or sensed hand position. We analyzed the spatial generalization of adaptation to target error to compare it with other known generalization patterns and simulated our results with a neural network model trained to minimize target error independent of prediction errors. Subjects reached to different peripheral visual targets and had to adapt to a sudden fixed-amplitude displacement (“jump”) consistently occurring for only one of the reach targets. Subjects simultaneously had to perform contralateral unperturbed saccades, which rendered the reach target jump unnoticeable. As a result, subjects adapted by gradually decreasing reach errors and showed negative aftereffects for the perturbed reach target. Reach errors generalized to unperturbed targets according to a translational rather than rotational generalization pattern, but locally, not globally. More importantly, reach errors generalized asymmetrically with a skewed generalization function in the direction of the target jump. Our neural network model reproduced the skewed generalization after adaptation to target jump without having been explicitly trained to produce a specific generalization pattern. Our combined psychophysical and simulation results suggest that target jump adaptation in reaching can be explained by gradual updating of spatial motor goal representations in sensorimotor association networks, independent of learning induced by a prediction-error about the hand position. The simulations make testable predictions about the underlying changes in the tuning of sensorimotor neurons during target jump adaptation. PMID:25609106

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

PubMed

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation. Copyright © 2015 the authors 0270-6474/15/3510112-23$15.00/0.

A-type potassium channels differentially tune afferent pathways from rat solitary tract nucleus to caudal ventrolateral medulla or paraventricular hypothalamus

PubMed Central

Bailey, T W; Hermes, S M; Whittier, K L; Aicher, S A; Andresen, M C

2007-01-01

The solitary tract nucleus (NTS) conveys visceral information to diverse central networks involved in homeostatic regulation. Although afferent information content arriving at various CNS sites varies substantially, little is known about the contribution of processing within the NTS to these differences. Using retrograde dyes to identify specific NTS projection neurons, we recently reported that solitary tract (ST) afferents directly contact NTS neurons projecting to caudal ventrolateral medulla (CVLM) but largely only indirectly contact neurons projecting to the hypothalamic paraventricular nucleus (PVN). Since intrinsic properties impact information transmission, here we evaluated potassium channel expression and somatodendritic morphology of projection neurons and their relation to afferent information output directed to PVN or CVLM pathways. In slices, tracer-identified projection neurons were classified as directly or indirectly (polysynaptically) coupled to ST afferents by EPSC latency characteristics (directly coupled, jitter < 200 μs). In each neuron, voltage-dependent potassium currents (IK) were evaluated and, in representative neurons, biocytin-filled structures were quantified. Both CVLM- and PVN-projecting neurons had similar, tetraethylammonium-sensitive IK. However, only PVN-projecting NTS neurons displayed large transient, 4aminopyridine-sensitive, A-type currents (IKA). PVN-projecting neurons had larger cell bodies with more elaborate dendritic morphology than CVLM-projecting neurons. ST shocks faithfully (> 75%) triggered action potentials in CVLM-projecting neurons but spike output was uniformly low (< 20%) in PVN-projecting neurons. Pre-conditioning hyperpolarization removed IKA inactivation and attenuated ST-evoked spike generation along PVN but not CVLM pathways. Thus, multiple differences in structure, organization, synaptic transmission and ion channel expression tune the overall fidelity of afferent signals that reach these destinations. PMID:17510187

Molecular codes for neuronal individuality and cell assembly in the brain

PubMed Central

Yagi, Takeshi

2012-01-01

The brain contains an enormous, but finite, number of neurons. The ability of this limited number of neurons to produce nearly limitless neural information over a lifetime is typically explained by combinatorial explosion; that is, by the exponential amplification of each neuron's contribution through its incorporation into “cell assemblies” and neural networks. In development, each neuron expresses diverse cellular recognition molecules that permit the formation of the appropriate neural cell assemblies to elicit various brain functions. The mechanism for generating neuronal assemblies and networks must involve molecular codes that give neurons individuality and allow them to recognize one another and join appropriate networks. The extensive molecular diversity of cell-surface proteins on neurons is likely to contribute to their individual identities. The clustered protocadherins (Pcdh) is a large subfamily within the diverse cadherin superfamily. The clustered Pcdh genes are encoded in tandem by three gene clusters, and are present in all known vertebrate genomes. The set of clustered Pcdh genes is expressed in a random and combinatorial manner in each neuron. In addition, cis-tetramers composed of heteromultimeric clustered Pcdh isoforms represent selective binding units for cell-cell interactions. Here I present the mathematical probabilities for neuronal individuality based on the random and combinatorial expression of clustered Pcdh isoforms and their formation of cis-tetramers in each neuron. Notably, clustered Pcdh gene products are known to play crucial roles in correct axonal projections, synaptic formation, and neuronal survival. Their molecular and biological features induce a hypothesis that the diverse clustered Pcdh molecules provide the molecular code by which neuronal individuality and cell assembly permit the combinatorial explosion of networks that supports enormous processing capability and plasticity of the brain. PMID:22518100

Neurotensin enhances glutamatergic EPSCs in VTA neurons by acting on different neurotensin receptors.

PubMed

Bose, Poulomee; Rompré, Pierre-Paul; Warren, Richard A

2015-11-01

Neurotensin (NT) is an endogenous neuropeptide that modulates dopamine and glutamate neurotransmission in several limbic regions innervated by neurons located in the ventral tegmental area (VTA). While several studies showed that NT exerted a direct modulation on VTA dopamine neurons less is known about its role in the modulation of glutamatergic neurotransmission in this region. The present study was aimed at characterising the effects of NT on glutamate-mediated responses in different populations of VTA neurons. Using whole cell patch clamp recording technique in horizontal rat brain slices, we measured the amplitude of glutamatergic excitatory post-synaptic currents (EPSCs) evoked by electrical stimulation of VTA afferents before and after application of different concentrations of NT1-13 or its C-terminal fragment, NT8-13. Neurons were classified as either Ih(+) or Ih(-) based on the presence or absence of a hyperpolarisation activated cationic current (Ih). We found that NT1-13 and NT8-13 produced comparable concentration dependent increase in the amplitude of EPSCs in both Ih(+) and Ih(-) neurons. In Ih(+) neurons, the enhancement effect of NT8-13 was blocked by both antagonists, while in Ih(-) neurons it was blocked by the NTS1/NTS2 antagonist, SR142948A, but not the preferred NTS1 antagonist, SR48692. In as much as Ih(-) neurons are non-dopaminergic neurons and Ih(+) neurons represent both dopamine and non-dopamine neurons, we can conclude that NT enhances glutamatergic mediated responses in dopamine, and in a subset of non-dopamine, neurons by acting respectively on NTS1 and an NT receptor other than NTS1. Copyright © 2015 Elsevier Inc. All rights reserved.

Action Potential Waveform Variability Limits Multi-Unit Separation in Freely Behaving Rats

PubMed Central

Stratton, Peter; Cheung, Allen; Wiles, Janet; Kiyatkin, Eugene; Sah, Pankaj; Windels, François

2012-01-01

Extracellular multi-unit recording is a widely used technique to study spontaneous and evoked neuronal activity in awake behaving animals. These recordings are done using either single-wire or mulitwire electrodes such as tetrodes. In this study we have tested the ability of single-wire electrodes to discriminate activity from multiple neurons under conditions of varying noise and neuronal cell density. Using extracellular single-unit recording, coupled with iontophoresis to drive cell activity across a wide dynamic range, we studied spike waveform variability, and explored systematic differences in single-unit spike waveform within and between brain regions as well as the influence of signal-to-noise ratio (SNR) on the similarity of spike waveforms. We also modelled spike misclassification for a range of cell densities based on neuronal recordings obtained at different SNRs. Modelling predictions were confirmed by classifying spike waveforms from multiple cells with various SNRs using a leading commercial spike-sorting system. Our results show that for single-wire recordings, multiple units can only be reliably distinguished under conditions of high recording SNR (≥4) and low neuronal density (≈20,000/ mm3). Physiological and behavioural changes, as well as technical limitations typical of awake animal preparations, reduce the accuracy of single-channel spike classification, resulting in serious classification errors. For SNR <4, the probability of misclassifying spikes approaches 100% in many cases. Our results suggest that in studies where the SNR is low or neuronal density is high, separation of distinct units needs to be evaluated with great caution. PMID:22719894

Spinally projecting neurons of the dorsal column nucleus in a reptile: locus of origin and trajectory of termination.

PubMed

Pritz, M B

1996-01-01

Interconnections between the dorsal column nucleus and the spinal cord were investigated in a reptile, Caiman crocodilus. After placement of an anterograde tracer into the dorsal column nucleus, descending fibers are seen to leave this nucleus to enter the dorsal funiculus where they course ventrally to terminate in lamina V of the spinal cord as far caudally as C2. Placement of a retrograde tracer into cut fibers of the cervical spinal cord identified the relay cells of the dorsal column nucleus that project to the spinal cord. These neurons were mainly clustered in a caudal and ventral part of this nucleus. The soma of these spinally projecting cells were small and were generally round or oval in shape. A number of these neurons had the long axis of their soma oriented dorsoventrally, with a primary dendrite extending dorsally. Fibers in the dorsal funiculus that originated from the spinal cord enter the caudal part of the dorsal column nucleus and turn ventral. In the dorsal column nucleus, these axons run parallel to the vertically oriented dendrites of these spinally projecting cells before termination in close relation to the cell bodies of these neurons. Quantitative observations (mean +/- standard error) were made on well labeled neurons and included several measurements: area, perimeter, and degree of eccentricity (greatest width/greatest length) in both the transverse as well as the sagittal plane. These spinally projecting neurons in Caiman are located in the dorsal column nucleus in a position similar to that of spinally projecting cells in cats.

Exact distinction of excitatory and inhibitory neurons in neural networks: a study with GFP-GAD67 neurons optically and electrophysiologically recognized on multielectrode arrays

PubMed Central

Becchetti, Andrea; Gullo, Francesca; Bruno, Giuseppe; Dossi, Elena; Lecchi, Marzia; Wanke, Enzo

2012-01-01

Distinguishing excitatory from inhibitory neurons with multielectrode array (MEA) recordings is a serious experimental challenge. The current methods, developed in vitro, mostly rely on spike waveform analysis. These however often display poor resolution and may produce errors caused by the variability of spike amplitudes and neuron shapes. Recent recordings in human brain suggest that the spike waveform features correlate with time-domain statistics such as spiking rate, autocorrelation, and coefficient of variation. However, no precise criteria are available to exactly assign identified units to specific neuronal types, either in vivo or in vitro. To solve this problem, we combined MEA recording with fluorescence imaging of neocortical cultures from mice expressing green fluorescent protein (GFP) in GABAergic cells. In this way, we could sort out “authentic excitatory neurons” (AENs) and “authentic inhibitory neurons” (AINs). We thus characterized 1275 units (from 405 electrodes, n = 10 experiments), based on autocorrelation, burst length, spike number (SN), spiking rate, squared coefficient of variation, and Fano factor (FF) (the ratio between spike-count variance and mean). These metrics differed by about one order of magnitude between AINs and AENs. In particular, the FF turned out to provide a firing code which exactly (no overlap) recognizes excitatory and inhibitory units. The difference in FF between all of the identified AEN and AIN groups was highly significant (p < 10−8, ANOVA post-hoc Tukey test). Our results indicate a statistical metric-based approach to distinguish excitatory from inhibitory neurons independently from the spike width. PMID:22973197

Compromised NMDA/Glutamate Receptor Expression in Dopaminergic Neurons Impairs Instrumental Learning, But Not Pavlovian Goal Tracking or Sign Tracking

PubMed

James, Alex S; Pennington, Zachary T; Tran, Phu; Jentsch, James David

2015-01-01

Two theories regarding the role for dopamine neurons in learning include the concepts that their activity serves as a (1) mechanism that confers incentive salience onto rewards and associated cues and/or (2) contingency teaching signal reflecting reward prediction error. While both theories are provocative, the causal role for dopamine cell activity in either mechanism remains controversial. In this study mice that either fully or partially lacked NMDARs in dopamine neurons exclusively, as well as appropriate controls, were evaluated for reward-related learning; this experimental design allowed for a test of the premise that NMDA/glutamate receptor (NMDAR)-mediated mechanisms in dopamine neurons, including NMDA-dependent regulation of phasic discharge activity of these cells, modulate either the instrumental learning processes or the likelihood of pavlovian cues to become highly motivating incentive stimuli that directly attract behavior. Loss of NMDARs in dopamine neurons did not significantly affect baseline dopamine utilization in the striatum, novelty evoked locomotor behavior, or consumption of a freely available, palatable food solution. On the other hand, animals lacking NMDARs in dopamine cells exhibited a selective reduction in reinforced lever responses that emerged over the course of instrumental learning. Loss of receptor expression did not, however, influence the likelihood of an animal acquiring a pavlovian conditional response associated with attribution of incentive salience to reward-paired cues (sign tracking). These data support the view that reductions in NMDAR signaling in dopamine neurons affect instrumental reward-related learning but do not lend support to hypotheses that suggest that the behavioral significance of this signaling includes incentive salience attribution.

Independent Neuronal Representation of Facial and Vocal Identity in the Monkey Hippocampus and Inferotemporal Cortex.

PubMed

Sliwa, Julia; Planté, Aurélie; Duhamel, Jean-René; Wirth, Sylvia

2016-03-01

Social interactions make up to a large extent the prime material of episodic memories. We therefore asked how social signals are coded by neurons in the hippocampus. Human hippocampus is home to neurons representing familiar individuals in an abstract and invariant manner ( Quian Quiroga et al. 2009). In contradistinction, activity of rat hippocampal cells is only weakly altered by the presence of other rats ( von Heimendahl et al. 2012; Zynyuk et al. 2012). We probed the activity of monkey hippocampal neurons to faces and voices of familiar and unfamiliar individuals (monkeys and humans). Thirty-one percent of neurons recorded without prescreening responded to faces or to voices. Yet responses to faces were more informative about individuals than responses to voices and neuronal responses to facial and vocal identities were not correlated, indicating that in our sample identity information was not conveyed in an invariant manner like in human neurons. Overall, responses displayed by monkey hippocampal neurons were similar to the ones of neurons recorded simultaneously in inferotemporal cortex, whose role in face perception is established. These results demonstrate that the monkey hippocampus participates in the read-out of social information contrary to the rat hippocampus, but possibly lack an explicit conceptual coding of as found in humans. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Neural Correlates of Object-Associated Choice Behavior in the Perirhinal Cortex of Rats

PubMed Central

Ahn, Jae-Rong

2015-01-01

The perirhinal cortex (PRC) is reportedly important for object recognition memory, with supporting physiological evidence obtained largely from primate studies. Whether neurons in the rodent PRC also exhibit similar physiological correlates of object recognition, however, remains to be determined. We recorded single units from the PRC in a PRC-dependent, object-cued spatial choice task in which, when cued by an object image, the rat chose the associated spatial target from two identical discs appearing on a touchscreen monitor. The firing rates of PRC neurons were significantly modulated by critical events in the task, such as object sampling and choice response. Neuronal firing in the PRC was correlated primarily with the conjunctive relationships between an object and its associated choice response, although some neurons also responded to the choice response alone. However, we rarely observed a PRC neuron that represented a specific object exclusively regardless of spatial response in rats, although the neurons were influenced by the perceptual ambiguity of the object at the population level. Some PRC neurons fired maximally after a choice response, and this post-choice feedback signal significantly enhanced the neuronal specificity for the choice response in the subsequent trial. Our findings suggest that neurons in the rat PRC may not participate exclusively in object recognition memory but that their activity may be more dynamically modulated in conjunction with other variables, such as choice response and its outcomes. PMID:25632144

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

Hippocampal and cortical neuronal growth mediated by the small molecule natural product clovanemagnolol.

PubMed

Khaing, Zin; Kang, Danby; Camelio, Andrew M; Schmidt, Christine E; Siegel, Dionicio

2011-08-15

The use of small molecule surrogates of growth factors that directly or indirectly promote growth represents an attractive approach to regenerative medicine. With synthetic access to clovanemagnolol, a small molecule initially isolated from the bark of the Bigleaf Magnolia tree, we have examined the small molecule's ability to promote growth of embryonic hippocampal and cortical neurons in serum-free medium. Comparisons with magnolol, a known promoter of growth, reveals that clovanmagnolol is a potent neurotrophic agent, promoting neuronal growth at concentrations of 10 nM. In addition, both clovanemagnolol and magnolol promote growth through a biphasic dose response. Copyright © 2011 Elsevier Ltd. All rights reserved.

Adaptive regulation of sparseness by feedforward inhibition

PubMed Central

Assisi, Collins; Stopfer, Mark; Laurent, Gilles; Bazhenov, Maxim

2014-01-01

In the mushroom body of insects, odors are represented by very few spikes in a small number of neurons, a highly efficient strategy known as sparse coding. Physiological studies of these neurons have shown that sparseness is maintained across thousand-fold changes in odor concentration. Using a realistic computational model, we propose that sparseness in the olfactory system is regulated by adaptive feedforward inhibition. When odor concentration changes, feedforward inhibition modulates the duration of the temporal window over which the mushroom body neurons may integrate excitatory presynaptic input. This simple adaptive mechanism could maintain the sparseness of sensory representations across wide ranges of stimulus conditions. PMID:17660812

Neuronal morphology in the lateral geniculate nucleus of the porpoise (Phocoena phocoena).

PubMed

Revishchin, A V; Garey, L J

1993-01-01

The Golgi and Nissl methods and cytochrome oxidase (CO) histochemistry were used to study the overall structure and neuronal morphology of the lateral geniculate nucleus (LGN) of the Black Sea porpoise (Phocoena phocoena). Differences were observed between dorsal and ventral portions of the nucleus in terms of cell size and CO staining. In addition to prominent fibre bundles crossing the LGN horizontally, vertically oriented variations of CO staining were apparent. Neuronal types in the LGN corresponded broadly to those observed in land mammals. The commonest were variants of multipolar cells, and may represent thalamocortical relay cells. Various other types were probably interneuronal.

Adaptive control system for pulsed megawatt klystrons

DOEpatents

Bolie, Victor W.

1992-01-01

The invention provides an arrangement for reducing waveform errors such as errors in phase or amplitude in output pulses produced by pulsed power output devices such as klystrons by generating an error voltage representing the extent of error still present in the trailing edge of the previous output pulse, using the error voltage to provide a stored control voltage, and applying the stored control voltage to the pulsed power output device to limit the extent of error in the leading edge of the next output pulse.

Hypothalamic carnitine metabolism integrates nutrient and hormonal feedback to regulate energy homeostasis.

PubMed

Stark, Romana; Reichenbach, Alex; Andrews, Zane B

2015-12-15

The maintenance of energy homeostasis requires the hypothalamic integration of nutrient feedback cues, such as glucose, fatty acids, amino acids, and metabolic hormones such as insulin, leptin and ghrelin. Although hypothalamic neurons are critical to maintain energy homeostasis research efforts have focused on feedback mechanisms in isolation, such as glucose alone, fatty acids alone or single hormones. However this seems rather too simplistic considering the range of nutrient and endocrine changes associated with different metabolic states, such as starvation (negative energy balance) or diet-induced obesity (positive energy balance). In order to understand how neurons integrate multiple nutrient or hormonal signals, we need to identify and examine potential intracellular convergence points or common molecular targets that have the ability to sense glucose, fatty acids, amino acids and hormones. In this review, we focus on the role of carnitine metabolism in neurons regulating energy homeostasis. Hypothalamic carnitine metabolism represents a novel means for neurons to facilitate and control both nutrient and hormonal feedback. In terms of nutrient regulation, carnitine metabolism regulates hypothalamic fatty acid sensing through the actions of CPT1 and has an underappreciated role in glucose sensing since carnitine metabolism also buffers mitochondrial matrix levels of acetyl-CoA, an allosteric inhibitor of pyruvate dehydrogenase and hence glucose metabolism. Studies also show that hypothalamic CPT1 activity also controls hormonal feedback. We hypothesis that hypothalamic carnitine metabolism represents a key molecular target that can concurrently integrate nutrient and hormonal information, which is critical to maintain energy homeostasis. We also suggest this is relevant to broader neuroendocrine research as it predicts that hormonal signaling in the brain varies depending on current nutrient status. Indeed, the metabolic action of ghrelin, leptin or insulin at POMC or NPY neurons may depend on appropriate nutrient-sensing in these neurons and we hypothesize carnitine metabolism is critical in the integrative processing. Future research is required to examine the neuron-specific effects of carnitine metabolism on concurrent nutrient- and hormonal-sensing in AgRP and POMC neurons. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Prefrontal neurons represent winning and losing during competitive video shooting games between monkeys.

PubMed

Hosokawa, Takayuki; Watanabe, Masataka

2012-05-30

Humans and animals must work to support their survival and reproductive needs. Because resources are limited in the natural environment, competition is inevitable, and competing successfully is vitally important. However, the neuronal mechanisms of competitive behavior are poorly studied. We examined whether neurons in the lateral prefrontal cortex (LPFC) showed response sensitivity related to a competitive game. In this study, monkeys played a video shooting game, either competing with another monkey or the computer, or playing alone without a rival. Monkeys performed more quickly and more accurately in the competitive than in the noncompetitive games, indicating that they were more motivated in the competitive than in the noncompetitive games. LPFC neurons showed differential activity between the competitive and noncompetitive games showing winning- and losing-related activity. Furthermore, activities of prefrontal neurons differed depending on whether the competition was between monkeys or between the monkey and the computer. These results indicate that LPFC neurons may play an important role in monitoring the outcome of competition and enabling animals to adapt their behavior to increase their chances of obtaining a reward in a socially interactive environment.