Navigating complex decision spaces: Problems and paradigms in sequential choice

PubMed Central

Walsh, Matthew M.; Anderson, John R.

2015-01-01

To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides two general solutions to this problem: model-free reinforcement learning and model-based reinforcement learning. In this review, we examine connections between stimulus-response and cognitive learning theories, habitual and goal-directed control, and model-free and model-based reinforcement learning. We then consider a range of problems related to temporal credit assignment. These include second-order conditioning and secondary reinforcers, latent learning and detour behavior, partially observable Markov decision processes, actions with distributed outcomes, and hierarchical learning. We ask whether humans and animals, when faced with these problems, behave in a manner consistent with reinforcement learning techniques. Throughout, we seek to identify neural substrates of model-free and model-based reinforcement learning. The former class of techniques is understood in terms of the neurotransmitter dopamine and its effects in the basal ganglia. The latter is understood in terms of a distributed network of regions including the prefrontal cortex, medial temporal lobes cerebellum, and basal ganglia. Not only do reinforcement learning techniques have a natural interpretation in terms of human and animal behavior, but they also provide a useful framework for understanding neural reward valuation and action selection. PMID:23834192

Dynamics of Action Potential Initiation in the GABAergic Thalamic Reticular Nucleus In Vivo

PubMed Central

Muñoz, Fabián; Fuentealba, Pablo

2012-01-01

Understanding the neural mechanisms of action potential generation is critical to establish the way neural circuits generate and coordinate activity. Accordingly, we investigated the dynamics of action potential initiation in the GABAergic thalamic reticular nucleus (TRN) using in vivo intracellular recordings in cats in order to preserve anatomically-intact axo-dendritic distributions and naturally-occurring spatiotemporal patterns of synaptic activity in this structure that regulates the thalamic relay to neocortex. We found a wide operational range of voltage thresholds for action potentials, mostly due to intrinsic voltage-gated conductances and not synaptic activity driven by network oscillations. Varying levels of synchronous synaptic inputs produced fast rates of membrane potential depolarization preceding the action potential onset that were associated with lower thresholds and increased excitability, consistent with TRN neurons performing as coincidence detectors. On the other hand the presence of action potentials preceding any given spike was associated with more depolarized thresholds. The phase-plane trajectory of the action potential showed somato-dendritic propagation, but no obvious axon initial segment component, prominent in other neuronal classes and allegedly responsible for the high onset speed. Overall, our results suggest that TRN neurons could flexibly integrate synaptic inputs to discharge action potentials over wide voltage ranges, and perform as coincidence detectors and temporal integrators, supported by a dynamic action potential threshold. PMID:22279567

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

Action Prediction in Younger versus Older Adults: Neural Correlates of Motor Familiarity

PubMed Central

Diersch, Nadine; Mueller, Karsten; Cross, Emily S.; Stadler, Waltraud; Rieger, Martina; Schütz-Bosbach, Simone

2013-01-01

Generating predictions during action observation is essential for efficient navigation through our social environment. With age, the sensitivity in action prediction declines. In younger adults, the action observation network (AON), consisting of premotor, parietal and occipitotemporal cortices, has been implicated in transforming executed and observed actions into a common code. Much less is known about age-related changes in the neural representation of observed actions. Using fMRI, the present study measured brain activity in younger and older adults during the prediction of temporarily occluded actions (figure skating elements and simple movement exercises). All participants were highly familiar with the movement exercises whereas only some participants were experienced figure skaters. With respect to the AON, the results confirm that this network was preferentially engaged for the more familiar movement exercises. Compared to younger adults, older adults recruited visual regions to perform the task and, additionally, the hippocampus and caudate when the observed actions were familiar to them. Thus, instead of effectively exploiting the sensorimotor matching properties of the AON, older adults seemed to rely predominantly on the visual dynamics of the observed actions to perform the task. Our data further suggest that the caudate played an important role during the prediction of the less familiar figure skating elements in better-performing groups. Together, these findings show that action prediction engages a distributed network in the brain, which is modulated by the content of the observed actions and the age and experience of the observer. PMID:23704980

Action prediction in younger versus older adults: neural correlates of motor familiarity.

PubMed

Diersch, Nadine; Mueller, Karsten; Cross, Emily S; Stadler, Waltraud; Rieger, Martina; Schütz-Bosbach, Simone

2013-01-01

Generating predictions during action observation is essential for efficient navigation through our social environment. With age, the sensitivity in action prediction declines. In younger adults, the action observation network (AON), consisting of premotor, parietal and occipitotemporal cortices, has been implicated in transforming executed and observed actions into a common code. Much less is known about age-related changes in the neural representation of observed actions. Using fMRI, the present study measured brain activity in younger and older adults during the prediction of temporarily occluded actions (figure skating elements and simple movement exercises). All participants were highly familiar with the movement exercises whereas only some participants were experienced figure skaters. With respect to the AON, the results confirm that this network was preferentially engaged for the more familiar movement exercises. Compared to younger adults, older adults recruited visual regions to perform the task and, additionally, the hippocampus and caudate when the observed actions were familiar to them. Thus, instead of effectively exploiting the sensorimotor matching properties of the AON, older adults seemed to rely predominantly on the visual dynamics of the observed actions to perform the task. Our data further suggest that the caudate played an important role during the prediction of the less familiar figure skating elements in better-performing groups. Together, these findings show that action prediction engages a distributed network in the brain, which is modulated by the content of the observed actions and the age and experience of the observer.

A Neural Dynamic Model Generates Descriptions of Object-Oriented Actions.

PubMed

Richter, Mathis; Lins, Jonas; Schöner, Gregor

2017-01-01

Describing actions entails that relations between objects are discovered. A pervasively neural account of this process requires that fundamental problems are solved: the neural pointer problem, the binding problem, and the problem of generating discrete processing steps from time-continuous neural processes. We present a prototypical solution to these problems in a neural dynamic model that comprises dynamic neural fields holding representations close to sensorimotor surfaces as well as dynamic neural nodes holding discrete, language-like representations. Making the connection between these two types of representations enables the model to describe actions as well as to perceptually ground movement phrases-all based on real visual input. We demonstrate how the dynamic neural processes autonomously generate the processing steps required to describe or ground object-oriented actions. By solving the fundamental problems of neural pointing, binding, and emergent discrete processing, the model may be a first but critical step toward a systematic neural processing account of higher cognition. Copyright © 2017 The Authors. Topics in Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

History, heresy and radiology in scientific discovery.

PubMed

McCredie, J

2009-10-01

Nowadays, most drugs reach the market after research has established their pharmacology, safety and efficacy. That was not always the case 50 years ago. Thalidomide was used before its target cell or mode of action were known. Commencing with the thalidomide catastrophe--an epidemic of gross birth defects (1958-1962)--thalidomide's origins are revisited to show how this drug came to be made and sold in the 1950s. Thalidomide intersected with Australian radiology in the 1970s. The site and mode of action of the drug was deduced from X-rays of thalidomide-induced bone defects, which have classical radiological signs of sensory neuropathic osteoarthropathy. The longitudinal reduction deformities follow the distribution of segmental sensory innervation of the limb skeleton, indicating neural crest as the target organ. Injury to one level of neural crest halts normal neurotrophism and deletes the dependent segment--a previously unrecognised embryonic mechanism that explains most non-genetic birth defects. The final common pathway is neural crest injury and failure of normal neurotrophism to result in longitudinal reduction deformities, for example, phocomelia.

Invariant recognition drives neural representations of action sequences

PubMed Central

Poggio, Tomaso

2017-01-01

Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs), that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences. PMID:29253864

Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network

PubMed Central

Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

2009-01-01

Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly’s halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support. PMID:20396612

Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network.

PubMed

Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

2009-06-01

Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly's halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support.

Cognitive Training for Impaired Neural Systems in Neuropsychiatric Illness

PubMed Central

Vinogradov, Sophia; Fisher, Melissa; de Villers-Sidani, Etienne

2012-01-01

Neuropsychiatric illnesses are associated with dysfunction in distributed prefrontal neural systems that underlie perception, cognition, social interactions, emotion regulation, and motivation. The high degree of learning-dependent plasticity in these networks—combined with the availability of advanced computerized technology—suggests that we should be able to engineer very specific training programs that drive meaningful and enduring improvements in impaired neural systems relevant to neuropsychiatric illness. However, cognitive training approaches for mental and addictive disorders must take into account possible inherent limitations in the underlying brain ‘learning machinery' due to pathophysiology, must grapple with the presence of complex overlearned maladaptive patterns of neural functioning, and must find a way to ally with developmental and psychosocial factors that influence response to illness and to treatment. In this review, we briefly examine the current state of knowledge from studies of cognitive remediation in psychiatry and we highlight open questions. We then present a systems neuroscience rationale for successful cognitive training for neuropsychiatric illnesses, one that emphasizes the distributed nature of neural assemblies that support cognitive and affective processing, as well as their plasticity. It is based on the notion that, during successful learning, the brain represents the relevant perceptual and cognitive/affective inputs and action outputs with disproportionately larger and more coordinated populations of neurons that are distributed (and that are interacting) across multiple levels of processing and throughout multiple brain regions. This approach allows us to address limitations found in earlier research and to introduce important principles for the design and evaluation of the next generation of cognitive training for impaired neural systems. We summarize work to date using such neuroscience-informed methods and indicate some of the exciting future directions of this field. PMID:22048465

Actionability and Simulation: No Representation without Communication

PubMed Central

Feldman, Jerome A.

2016-01-01

There remains considerable controversy about how the brain operates. This review focuses on brain activity rather than just structure and on concepts of action and actionability rather than truth conditions. Neural Communication is reviewed as a crucial aspect of neural encoding. Consequently, logical inference is superseded by neural simulation. Some remaining mysteries are discussed. PMID:27725807

Persistent neuronal activity in human prefrontal cortex links perception and action

PubMed Central

Haller, Matar; Case, John; Crone, Nathan E.; Chang, Edward F.; King-Stephens, David; Laxer, Kenneth D.; Weber, Peter B.; Parvizi, Josef; Knight, Robert T.; Shestyuk, Avgusta Y.

2017-01-01

How do humans flexibly respond to changing environmental demands on a sub-second temporal scale? Extensive research has highlighted the key role of the prefrontal cortex in flexible decision-making and adaptive behavior, yet the core mechanisms that translate sensory information into behavior remain undefined. Utilizing direct human cortical recordings, we investigated the temporal and spatial evolution of neuronal activity, indexed by the broadband gamma signal, while sixteen participants performed a broad range of self-paced cognitive tasks. Here we describe a robust domain- and modality-independent pattern of persistent stimulus-to-response neural activation that encodes stimulus features and predicts motor output on a trial-by-trial basis with near-perfect accuracy. Observed across a distributed network of brain areas, this persistent neural activation is centered in the prefrontal cortex and is required for successful response implementation, providing a functional substrate for domain-general transformation of perception into action, critical for flexible behavior.

Past makes future: role of pFC in prediction.

PubMed

Fuster, Joaquín M; Bressler, Steven L

2015-04-01

The pFC enables the essential human capacities for predicting future events and preadapting to them. These capacities rest on both the structure and dynamics of the human pFC. Structurally, pFC, together with posterior association cortex, is at the highest hierarchical level of cortical organization, harboring neural networks that represent complex goal-directed actions. Dynamically, pFC is at the highest level of the perception-action cycle, the circular processing loop through the cortex that interfaces the organism with the environment in the pursuit of goals. In its predictive and preadaptive roles, pFC supports cognitive functions that are critical for the temporal organization of future behavior, including planning, attentional set, working memory, decision-making, and error monitoring. These functions have a common future perspective and are dynamically intertwined in goal-directed action. They all utilize the same neural infrastructure: a vast array of widely distributed, overlapping, and interactive cortical networks of personal memory and semantic knowledge, named cognits, which are formed by synaptic reinforcement in learning and memory acquisition. From this cortex-wide reservoir of memory and knowledge, pFC generates purposeful, goal-directed actions that are preadapted to predicted future events.

Reversed Procrastination by Focal Disruption of Medial Frontal Cortex.

PubMed

Jha, Ashwani; Diehl, Beate; Scott, Catherine; McEvoy, Andrew W; Nachev, Parashkev

2016-11-07

An enduring puzzle in the neuroscience of voluntary action is the origin of the remarkably wide dispersion of the reaction time distribution, an interval far greater than is explained by synaptic or signal transductive noise [1, 2]. That we are able to change our planned actions-a key criterion of volition [3]-so close to the time of their onset implies decision-making must reach deep into the execution of action itself [4-6]. It has been influentially suggested the reaction time distribution therefore reflects deliberate neural procrastination [7], giving alternative response tendencies sufficient time for fair competition in pursuing a decision threshold that determines which one is behaviorally manifest: a race model, where action selection and execution are closely interrelated [8-11]. Although the medial frontal cortex exhibits a sensitivity to reaction time on functional imaging that is consistent with such a mechanism [12-14], direct evidence from disruptive studies has hitherto been lacking. If movement-generating and movement-delaying neural substrates are closely co-localized here, a large-scale lesion will inevitably mask any acceleration, for the movement itself could be disrupted. Circumventing this problem, here we observed focal intracranial electrical disruption of the medial frontal wall in the context of the pre-surgical evaluation of two patients with epilepsy temporarily reversing such hypothesized procrastination. Effector-specific behavioral acceleration, time-locked to the period of electrical disruption, occurred exclusively at a specific locus at the ventral border of the pre-supplementary motor area. A cardinal prediction of race models of voluntary action is thereby substantiated in the human brain. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

[Neuronal and synaptic properties: fundamentals of network plasticity].

PubMed

Le Masson, G

2000-02-01

Neurons, within the nervous system, are organized in different neural networks through synaptic connections. Two fundamental components are dynamically interacting in these functional units. The first one are the neurons themselves, and far from being simple action potential generators, they are capable of complex electrical integrative properties due to various types, number, distribution and modulation of voltage-gated ionic channels. The second elements are the synapses where a similar complexity and plasticity is found. Identifying both cellular and synaptic intrinsic properties is necessary to understand the links between neural networks behavior and physiological function, and is a useful step towards a better control of neurological diseases.

Melatonin membrane receptors in peripheral tissues: Distribution and functions

PubMed Central

Slominski, Radomir M.; Reiter, Russel J.; Schlabritz-Loutsevitch, Natalia; Ostrom, Rennolds S.; Slominski, Andrzej T.

2012-01-01

Many of melatonin’s actions are mediated through interaction with the G-protein coupled membrane bound melatonin receptors type 1 and type 2 (MT1 and MT2, respectively) or, indirectly with nuclear orphan receptors from the RORα/RZR family. Melatonin also binds to the quinone reductase II enzyme, previously defined the MT3 receptor. Melatonin receptors are widely distributed in the body; herein we summarize their expression and actions in non-neural tissues. Several controversies still exist regarding, for example, whether melatonin binds the RORα/RZR family. Studies of the peripheral distribution of melatonin receptors are important since they are attractive targets for immunomodulation, regulation of endocrine, reproductive and cardiovascular functions, modulation of skin pigmentation, hair growth, cancerogenesis, and aging. Melatonin receptor agonists and antagonists have an exciting future since they could define multiple mechanisms by which melatonin modulates the complexity of such a wide variety of physiological and pathological processes. PMID:22245784

The Neural Representation of Goal-Directed Actions and Outcomes in the Ventral Striatum's Olfactory Tubercle

PubMed Central

Gadziola, Marie A.

2016-01-01

The ventral striatum is critical for evaluating reward information and the initiation of goal-directed behaviors. The many cellular, afferent, and efferent similarities between the ventral striatum's nucleus accumbens and olfactory tubercle (OT) suggests the distributed involvement of neurons within the ventral striatopallidal complex in motivated behaviors. Although the nucleus accumbens has an established role in representing goal-directed actions and their outcomes, it is not known whether this function is localized within the nucleus accumbens or distributed also within the OT. Answering such a fundamental question will expand our understanding of the neural mechanisms underlying motivated behaviors. Here we address whether the OT encodes natural reinforcers and serves as a substrate for motivational information processing. In recordings from mice engaged in a novel water-motivated instrumental task, we report that OT neurons modulate their firing rate during initiation and progression of the instrumental licking behavior, with some activity being internally generated and preceding the first lick. We further found that as motivational drive decreases throughout a session, the activity of OT neurons is enhanced earlier relative to the behavioral action. Additionally, OT neurons discriminate the types and magnitudes of fluid reinforcers. Together, these data suggest that the processing of reward information and the orchestration of goal-directed behaviors is a global principle of the ventral striatum and have important implications for understanding the neural systems subserving addiction and mood disorders. SIGNIFICANCE STATEMENT Goal-directed behaviors are widespread among animals and underlie complex behaviors ranging from food intake, social behavior, and even pathological conditions, such as gambling and drug addiction. The ventral striatum is a neural system critical for evaluating reward information and the initiation of goal-directed behaviors. Here we show that neurons in the olfactory tubercle subregion of the ventral striatum robustly encode the onset and progression of motivated behaviors, and discriminate the type and magnitude of a reward. Our findings are novel in showing that olfactory tubercle neurons participate in such coding schemes and are in accordance with the principle that ventral striatum substructures may cooperate to guide motivated behaviors. PMID:26758844

Understanding approach and avoidance in verbal descriptions of everyday actions: An ERP study.

PubMed

Marrero, Hipólito; Urrutia, Mabel; Beltrán, David; Gámez, Elena; Díaz, José M

2017-06-01

Understanding verbal descriptions of everyday actions could involve the neural representation of action direction (avoidance and approach) toward persons and things. We recorded the electrophysiological activity of participants while they were reading approach/avoidance action sentences that were directed toward a target: a thing/a person (i.e., "Petra accepted/rejected Ramón in her group"/ "Petra accepted/rejected the receipt of the bank"). We measured brain potentials time locked to the target word. In the case of things, we found a N400-like component with right frontal distribution modulated by approach/avoidance action. This component was more negative in avoidance than in approach sentences. In the case of persons, a later negative event-related potential (545-750 ms) with left frontal distribution was sensitive to verb direction, showing more negative amplitude for approach than avoidance actions. In addition, more negativity in approach-person sentences was associated with fear avoidance trait, whereas less negativity in avoidance-person sentences was associated with a greater approach trait. Our results support that verbal descriptions of approach/avoidance actions are encoded differently depending on whether the target is a thing or a person. Implications of these results for a social, emotional and motivational understanding of action language are discussed.

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

PubMed Central

Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

2011-01-01

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569

Transient stimulation of distinct subpopulations of striatal neurons mimics changes in action value

PubMed Central

Tai, Lung-Hao; Lee, A. Moses; Benavidez, Nora; Bonci, Antonello; Wilbrecht, Linda

2012-01-01

In changing environments animals must adaptively select actions to achieve their goals. In tasks involving goal-directed action selection, striatal neural activity has been shown to represent the value of competing actions. Striatal representations of action value could potentially bias responses toward actions of higher value. However, no study to date has demonstrated the direct impact of distinct striatal pathways in goal-directed action selection. Here we show in mice that transient optogenetic stimulation of dorsal striatal dopamine D1 and D2 receptor-expressing neurons during decision-making introduces opposing biases in the distribution of choices. The effect of stimulation on choice is dependent on recent reward history and mimics an additive change in the action value. While stimulation prior to and during movement initiation produces a robust bias in choice behavior, this bias is significantly diminished when stimulation is delayed after response initiation. Together, our data demonstrate the role of striatal activity in goal-directed action selection. PMID:22902719

The auditory representation of speech sounds in human motor cortex

PubMed Central

Cheung, Connie; Hamilton, Liberty S; Johnson, Keith; Chang, Edward F

2016-01-01

In humans, listening to speech evokes neural responses in the motor cortex. This has been controversially interpreted as evidence that speech sounds are processed as articulatory gestures. However, it is unclear what information is actually encoded by such neural activity. We used high-density direct human cortical recordings while participants spoke and listened to speech sounds. Motor cortex neural patterns during listening were substantially different than during articulation of the same sounds. During listening, we observed neural activity in the superior and inferior regions of ventral motor cortex. During speaking, responses were distributed throughout somatotopic representations of speech articulators in motor cortex. The structure of responses in motor cortex during listening was organized along acoustic features similar to auditory cortex, rather than along articulatory features as during speaking. Motor cortex does not contain articulatory representations of perceived actions in speech, but rather, represents auditory vocal information. DOI: http://dx.doi.org/10.7554/eLife.12577.001 PMID:26943778

Competition in high dimensional spaces using a sparse approximation of neural fields.

PubMed

Quinton, Jean-Charles; Girau, Bernard; Lefort, Mathieu

2011-01-01

The Continuum Neural Field Theory implements competition within topologically organized neural networks with lateral inhibitory connections. However, due to the polynomial complexity of matrix-based implementations, updating dense representations of the activity becomes computationally intractable when an adaptive resolution or an arbitrary number of input dimensions is required. This paper proposes an alternative to self-organizing maps with a sparse implementation based on Gaussian mixture models, promoting a trade-off in redundancy for higher computational efficiency and alleviating constraints on the underlying substrate.This version reproduces the emergent attentional properties of the original equations, by directly applying them within a continuous approximation of a high dimensional neural field. The model is compatible with preprocessed sensory flows but can also be interfaced with artificial systems. This is particularly important for sensorimotor systems, where decisions and motor actions must be taken and updated in real-time. Preliminary tests are performed on a reactive color tracking application, using spatially distributed color features.

Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

PubMed Central

Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo

2015-01-01

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. PMID:25972586

Modeling fMRI signals can provide insights into neural processing in the cerebral cortex.

PubMed

Vanni, Simo; Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo

2015-08-01

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. Copyright © 2015 the American Physiological Society.

Folate receptor 1 is necessary for neural plate cell apical constriction during Xenopus neural tube formation

PubMed Central

Balashova, Olga A.; Visina, Olesya

2017-01-01

Folate supplementation prevents up to 70% of neural tube defects (NTDs), which result from a failure of neural tube closure during embryogenesis. The elucidation of the mechanisms underlying folate action has been challenging. This study introduces Xenopus laevis as a model to determine the cellular and molecular mechanisms involved in folate action during neural tube formation. We show that knockdown of folate receptor 1 (Folr1; also known as FRα) impairs neural tube formation and leads to NTDs. Folr1 knockdown in neural plate cells only is necessary and sufficient to induce NTDs. Folr1-deficient neural plate cells fail to constrict, resulting in widening of the neural plate midline and defective neural tube closure. Pharmacological inhibition of folate action by methotrexate during neurulation induces NTDs by inhibiting folate interaction with its uptake systems. Our findings support a model in which the folate receptor interacts with cell adhesion molecules, thus regulating the apical cell membrane remodeling and cytoskeletal dynamics necessary for neural plate folding. Further studies in this organism could unveil novel cellular and molecular events mediated by folate and lead to new ways of preventing NTDs. PMID:28255006

Localization of basic fibroblast growth factor binding sites in the chick embryonic neural retina.

PubMed

Cirillo, A; Arruti, C; Courtois, Y; Jeanny, J C

1990-12-01

We have investigated the localization of basic fibroblast growth factor (bFGF) binding sites during the development of the neural retina in the chick embryo. The specificity of the affinity of bFGF for its receptors was assessed by competition experiments with unlabelled growth factor or with heparin, as well as by heparitinase treatment of the samples. Two different types of binding sites were observed in the neural retina by light-microscopic autoradiography. The first type, localized mainly to basement membranes, was highly sensitive to heparitinase digestion and to competition with heparin. It was not developmentally regulated. The second type of binding site, resistant to heparin competition, appeared to be associated with retinal cells from the earliest stages studied (3-day-old embryo, stages 21-22 of Hamburger and Hamilton). Its distribution was found to vary during embryonic development, paralleling layering of the neural retina. Binding of bFGF to the latter sites was observed throughout the retinal neuroepithelium at early stages but displayed a distinct pattern at the time when the inner and outer plexiform layers were formed. During the development of the inner plexiform layer, a banded pattern of bFGF binding was observed. These bands, lying parallel to the vitreal surface, seemed to codistribute with the synaptic bands existing in the inner plexiform layer. The presence of intra-retinal bFGF binding sites whose distribution varies with embryonic development suggests a regulatory mechanism involving differential actions of bFGF on neural retinal cells.

Neural activity underlying motor-action preparation and cognitive narrowing in approach-motivated goal states.

PubMed

Gable, Philip A; Threadgill, A Hunter; Adams, David L

2016-02-01

High-approach-motivated (pre-goal) positive affect states encourage tenacious goal pursuit and narrow cognitive scope. As such, high approach-motivated states likely enhance the neural correlates of motor-action preparation to aid in goal acquisition. These neural correlates may also relate to the cognitive narrowing associated with high approach-motivated states. In the present study, we investigated motor-action preparation during pre-goal and post-goal states using an index of beta suppression over the motor cortex. The results revealed that beta suppression was greatest in pre-goal positive states, suggesting that higher levels of motor-action preparation occur during high approach-motivated positive states. Furthermore, beta and alpha suppression in the high approach-motivated positive states predicted greater cognitive narrowing. These results suggest that approach-motivated pre-goal states engage the neural substrates of motor-action preparation and cognitive narrowing. Individual differences in motor-action preparation relate to the degree of cognitive narrowing.

Distinct neural substrates for visual short-term memory of actions.

PubMed

Cai, Ying; Urgolites, Zhisen; Wood, Justin; Chen, Chuansheng; Li, Siyao; Chen, Antao; Xue, Gui

2018-06-26

Fundamental theories of human cognition have long posited that the short-term maintenance of actions is supported by one of the "core knowledge" systems of human visual cognition, yet its neural substrates are still not well understood. In particular, it is unclear whether the visual short-term memory (VSTM) of actions has distinct neural substrates or, as proposed by the spatio-object architecture of VSTM, shares them with VSTM of objects and spatial locations. In two experiments, we tested these two competing hypotheses by directly contrasting the neural substrates for VSTM of actions with those for objects and locations. Our results showed that the bilateral middle temporal cortex (MT) was specifically involved in VSTM of actions because its activation and its functional connectivity with the frontal-parietal network (FPN) were only modulated by the memory load of actions, but not by that of objects/agents or locations. Moreover, the brain regions involved in the maintenance of spatial location information (i.e., superior parietal lobule, SPL) was also recruited during the maintenance of actions, consistent with the temporal-spatial nature of actions. Meanwhile, the frontoparietal network (FPN) was commonly involved in all types of VSTM and showed flexible functional connectivity with the domain-specific regions, depending on the current working memory tasks. Together, our results provide clear evidence for a distinct neural system for maintaining actions in VSTM, which supports the core knowledge system theory and the domain-specific and domain-general architectures of VSTM. © 2018 Wiley Periodicals, Inc.

Sentential Negation Might Share Neurophysiological Mechanisms with Action Inhibition. Evidence from Frontal Theta Rhythm.

PubMed

de Vega, Manuel; Morera, Yurena; León, Inmaculada; Beltrán, David; Casado, Pilar; Martín-Loeches, Manuel

2016-06-01

According to the literature, negations such as "not" or "don't" reduce the accessibility in memory of the concepts under their scope. Moreover, negations applied to action contents (e.g., "don't write the letter") impede the activation of motor processes in the brain, inducing "disembodied" representations. These facts provide important information on the behavioral and neural consequences of negations. However, how negations themselves are processed in the brain is still poorly understood. In two electrophysiological experiments, we explored whether sentential negation shares neural mechanisms with action monitoring or inhibition. Human participants read action-related sentences in affirmative or negative form ("now you will cut the bread" vs "now you will not cut the bread") while performing a simultaneous Go/NoGo task. The analysis of the EEG rhythms revealed that theta oscillations were significantly reduced for NoGo trials in the context of negative sentences compared with affirmative sentences. Given the fact that theta oscillations are often considered as neural markers of response inhibition processes, their modulation by negative sentences strongly suggests that negation uses neural resources of response inhibition. We propose a new approach that views the syntactic operator of negation as relying on the neural machinery of high-order action-monitoring processes. Previous studies have shown that linguistic negation reduces the accessibility of the negated concepts and suppresses the activation of specific brain regions that operate in affirmative statements. Although these studies focus on the consequences of negation on cognitive and neural processes, the proper neural mechanisms of negation have not yet been explored. In the present EEG study, we tested the hypothesis that negation uses the neural network of action inhibition. Using a Go/NoGo task embedded in a sentence comprehension task, we found that negation in the context of NoGo trials modulates frontal theta rhythm, which is usually considered a signature of action inhibition and control mechanisms. Copyright © 2016 the authors 0270-6474/16/366002-09$15.00/0.

Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation.

PubMed

Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S

2017-04-01

Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

PubMed

Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

2015-05-01

Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

The importance of actions and the worth of an object: dissociable neural systems representing core value and economic value.

PubMed

Brosch, Tobias; Coppin, Géraldine; Schwartz, Sophie; Sander, David

2012-06-01

Neuroeconomic research has delineated neural regions involved in the computation of value, referring to a currency for concrete choices and decisions ('economic value'). Research in psychology and sociology, on the other hand, uses the term 'value' to describe motivational constructs that guide choices and behaviors across situations ('core value'). As a first step towards an integration of these literatures, we compared the neural regions computing economic value and core value. Replicating previous work, economic value computations activated a network centered on medial orbitofrontal cortex. Core value computations activated medial prefrontal cortex, a region involved in the processing of self-relevant information and dorsal striatum, involved in action selection. Core value ratings correlated with activity in precuneus and anterior prefrontal cortex, potentially reflecting the degree to which a core value is perceived as internalized part of one's self-concept. Distributed activation pattern in insula and ACC allowed differentiating individual core value types. These patterns may represent evaluation profiles reflecting prototypical fundamental concerns expressed in the core value types. Our findings suggest mechanisms by which core values, as motivationally important long-term goals anchored in the self-schema, may have the behavioral power to drive decisions and behaviors in the absence of immediately rewarding behavioral options.

The neural basis of body form and body action agnosia.

PubMed

Moro, Valentina; Urgesi, Cosimo; Pernigo, Simone; Lanteri, Paola; Pazzaglia, Mariella; Aglioti, Salvatore Maria

2008-10-23

Visual analysis of faces and nonfacial body stimuli brings about neural activity in different cortical areas. Moreover, processing body form and body action relies on distinct neural substrates. Although brain lesion studies show specific face processing deficits, neuropsychological evidence for defective recognition of nonfacial body parts is lacking. By combining psychophysics studies with lesion-mapping techniques, we found that lesions of ventromedial, occipitotemporal areas induce face and body recognition deficits while lesions involving extrastriate body area seem causatively associated with impaired recognition of body but not of face and object stimuli. We also found that body form and body action recognition deficits can be double dissociated and are causatively associated with lesions to extrastriate body area and ventral premotor cortex, respectively. Our study reports two category-specific visual deficits, called body form and body action agnosia, and highlights their neural underpinnings.

Neural associative memories for the integration of language, vision and action in an autonomous agent.

PubMed

Markert, H; Kaufmann, U; Kara Kayikci, Z; Palm, G

2009-03-01

Language understanding is a long-standing problem in computer science. However, the human brain is capable of processing complex languages with seemingly no difficulties. This paper shows a model for language understanding using biologically plausible neural networks composed of associative memories. The model is able to deal with ambiguities on the single word and grammatical level. The language system is embedded into a robot in order to demonstrate the correct semantical understanding of the input sentences by letting the robot perform corresponding actions. For that purpose, a simple neural action planning system has been combined with neural networks for visual object recognition and visual attention control mechanisms.

Are corporations people too? The neural correlates of moral judgments about companies and individuals.

PubMed

Plitt, Mark; Savjani, Ricky R; Eagleman, David M

2015-04-01

To investigate whether the legal concept of "corporate personhood" mirrors an inherent similarity in the neural processing of the actions of corporations and people, we measured brain responses to vignettes about corporations and people while participants underwent functional magnetic resonance imaging. We found that anti-social actions of corporations elicited more intense negative emotions and that pro-social actions of people elicited more intense positive emotions. However, the networks underlying the moral decisions about corporations and people are strikingly similar, including regions of the canonical theory of mind network. In analyzing the activity in these networks, we found differences in the emotional processing of these two types of vignettes: neutral actions of corporations showed neural correlates that more closely resembled negative actions than positive actions. Collectively, these findings indicate that our brains understand and analyze the actions of corporations and people very similarly, with a small emotional bias against corporations.

Neural retina of chick embryo in organ culture: effects of blockade of growth factors by suramin.

PubMed

Cirillo, A; Chifflet, S; Villar, B

2001-06-01

The neural retina is a highly organized organ whose final histoarchitecture depends on the presence of diverse growth factors and on their interactions with extracellular matrix components. However, the role of growth factors on retinal development is not fully understood. Suramin has been shown to produce diverse cellular effects via the simultaneous block of the action of several growth factors. We have therefore studied the effects of suramin on organotypic culture of chick embryo neural retina in order to gain further insights into the participation of growth factors in neural retinal development. Neural retina was incubated for 24 h with suramin at 50-200 microM and then processed to determine cell proliferation, nuclear morphology, and actin distribution. Suramin provoked extensive morphological changes revealed by a decrease in BrdU incorporation, alterations in cellular organization, and disruption of the outer limiting membrane, with the emergence of cellular elements through it. All of these effects were dose-dependent and markedly attenuated by the simultaneous presence of suramin and fibroblast growth factor 2 (FGF-2) in the culture medium. These findings indicate that suramin induces pleiotropic effects on the histoarchitecture of the chicken neural retina in organ culture and suggest that FGF-2 is one of the biological modulators involved in the maintenance of the structural organization of the chicken neural retina.

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response.

PubMed

Naumann, Eva A; Fitzgerald, James E; Dunn, Timothy W; Rihel, Jason; Sompolinsky, Haim; Engert, Florian

2016-11-03

Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data. Copyright © 2016 Elsevier Inc. All rights reserved.

Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances

PubMed Central

Soltoggio, Andrea; Lemme, Andre; Reinhart, Felix; Steil, Jochen J.

2013-01-01

Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards. Such an uncertainty is handled by biological neural networks, but represents a challenge for computational models, suggesting the lack of a satisfactory theory for robotic neural conditioning. The present study demonstrates the use of rare neural correlations in making correct associations between rewards and previous cues or actions. Rare correlations are functional in selecting sparse synapses to be eligible for later weight updates if a reward occurs. The repetition of this process singles out the associating and reward-triggering pathways, and thereby copes with distal rewards. The neural network displays macro-level classical and operant conditioning, which is demonstrated in an interactive real-life human-robot interaction. The proposed mechanism models realistic conditioning in humans and animals and implements similar behaviors in neuro-robotic platforms. PMID:23565092

Real-time strategy game training: emergence of a cognitive flexibility trait.

PubMed

Glass, Brian D; Maddox, W Todd; Love, Bradley C

2013-01-01

Training in action video games can increase the speed of perceptual processing. However, it is unknown whether video-game training can lead to broad-based changes in higher-level competencies such as cognitive flexibility, a core and neurally distributed component of cognition. To determine whether video gaming can enhance cognitive flexibility and, if so, why these changes occur, the current study compares two versions of a real-time strategy (RTS) game. Using a meta-analytic Bayes factor approach, we found that the gaming condition that emphasized maintenance and rapid switching between multiple information and action sources led to a large increase in cognitive flexibility as measured by a wide array of non-video gaming tasks. Theoretically, the results suggest that the distributed brain networks supporting cognitive flexibility can be tuned by engrossing video game experience that stresses maintenance and rapid manipulation of multiple information sources. Practically, these results suggest avenues for increasing cognitive function.

Real-Time Strategy Game Training: Emergence of a Cognitive Flexibility Trait

PubMed Central

Glass, Brian D.; Maddox, W. Todd; Love, Bradley C.

2013-01-01

Training in action video games can increase the speed of perceptual processing. However, it is unknown whether video-game training can lead to broad-based changes in higher-level competencies such as cognitive flexibility, a core and neurally distributed component of cognition. To determine whether video gaming can enhance cognitive flexibility and, if so, why these changes occur, the current study compares two versions of a real-time strategy (RTS) game. Using a meta-analytic Bayes factor approach, we found that the gaming condition that emphasized maintenance and rapid switching between multiple information and action sources led to a large increase in cognitive flexibility as measured by a wide array of non-video gaming tasks. Theoretically, the results suggest that the distributed brain networks supporting cognitive flexibility can be tuned by engrossing video game experience that stresses maintenance and rapid manipulation of multiple information sources. Practically, these results suggest avenues for increasing cognitive function. PMID:23950921

Methylphenidate and Atomoxetine Inhibit Social Play Behavior through Prefrontal and Subcortical Limbic Mechanisms in Rats

PubMed Central

Achterberg, E.J. Marijke; van Kerkhof, Linda W.M.; Damsteegt, Ruth; Trezza, Viviana

2015-01-01

Positive social interactions during the juvenile and adolescent phases of life, in the form of social play behavior, are important for social and cognitive development. However, the neural mechanisms of social play behavior remain incompletely understood. We have previously shown that methylphenidate and atomoxetine, drugs widely used for the treatment of attention-deficit hyperactivity disorder (ADHD), suppress social play in rats through a noradrenergic mechanism of action. Here, we aimed to identify the neural substrates of the play-suppressant effects of these drugs. Methylphenidate is thought to exert its effects on cognition and emotion through limbic corticostriatal systems. Therefore, methylphenidate was infused into prefrontal and orbitofrontal cortical regions as well as into several subcortical limbic areas implicated in social play. Infusion of methylphenidate into the anterior cingulate cortex, infralimbic cortex, basolateral amygdala, and habenula inhibited social play, but not social exploratory behavior or locomotor activity. Consistent with a noradrenergic mechanism of action of methylphenidate, infusion of the noradrenaline reuptake inhibitor atomoxetine into these same regions also reduced social play. Methylphenidate administration into the prelimbic, medial/ventral orbitofrontal, and ventrolateral orbitofrontal cortex, mediodorsal thalamus, or nucleus accumbens shell was ineffective. Our data show that the inhibitory effects of methylphenidate and atomoxetine on social play are mediated through a distributed network of prefrontal and limbic subcortical regions implicated in cognitive control and emotional processes. These findings increase our understanding of the neural underpinnings of this developmentally important social behavior, as well as the mechanism of action of two widely used treatments for ADHD. PMID:25568111

Using fuzzy logic to integrate neural networks and knowledge-based systems

NASA Technical Reports Server (NTRS)

Yen, John

1991-01-01

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

A spiking neural integrator model of the adaptive control of action by the medial prefrontal cortex.

PubMed

Bekolay, Trevor; Laubach, Mark; Eliasmith, Chris

2014-01-29

Subjects performing simple reaction-time tasks can improve reaction times by learning the expected timing of action-imperative stimuli and preparing movements in advance. Success or failure on the previous trial is often an important factor for determining whether a subject will attempt to time the stimulus or wait for it to occur before initiating action. The medial prefrontal cortex (mPFC) has been implicated in enabling the top-down control of action depending on the outcome of the previous trial. Analysis of spike activity from the rat mPFC suggests that neural integration is a key mechanism for adaptive control in precisely timed tasks. We show through simulation that a spiking neural network consisting of coupled neural integrators captures the neural dynamics of the experimentally recorded mPFC. Errors lead to deviations in the normal dynamics of the system, a process that could enable learning from past mistakes. We expand on this coupled integrator network to construct a spiking neural network that performs a reaction-time task by following either a cue-response or timing strategy, and show that it performs the task with similar reaction times as experimental subjects while maintaining the same spiking dynamics as the experimentally recorded mPFC.

When language meets action: the neural integration of gesture and speech.

PubMed

Willems, Roel M; Ozyürek, Asli; Hagoort, Peter

2007-10-01

Although generally studied in isolation, language and action often co-occur in everyday life. Here we investigated one particular form of simultaneous language and action, namely speech and gestures that speakers use in everyday communication. In a functional magnetic resonance imaging study, we identified the neural networks involved in the integration of semantic information from speech and gestures. Verbal and/or gestural content could be integrated easily or less easily with the content of the preceding part of speech. Premotor areas involved in action observation (Brodmann area [BA] 6) were found to be specifically modulated by action information "mismatching" to a language context. Importantly, an increase in integration load of both verbal and gestural information into prior speech context activated Broca's area and adjacent cortex (BA 45/47). A classical language area, Broca's area, is not only recruited for language-internal processing but also when action observation is integrated with speech. These findings provide direct evidence that action and language processing share a high-level neural integration system.

Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study Using a Brain-Computer Interface.

PubMed

Fernández-Soto, Alicia; Martínez-Rodrigo, Arturo; Moncho-Bogani, José; Latorre, José Miguel; Fernández-Caballero, Antonio

2018-06-01

For the sake of establishing the neural correlates of phrase quadrature perception in harmonic rhythm, a musical experiment has been designed to induce music-evoked stimuli related to one important aspect of harmonic rhythm, namely the phrase quadrature. Brain activity is translated to action through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. The results of processing the acquired signals are in line with previous studies that use different musical parameters to induce emotions. Indeed, our experiment shows statistical differences in theta and alpha bands between the fulfillment and break of phrase quadrature, an important cue of harmonic rhythm, in two classical sonatas.

Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model

PubMed Central

Panda, Priyadarshini; Srinivasa, Narayan

2018-01-01

A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competitive accuracy with respect to state-of-the-art non-spiking neural models. PMID:29551962

The neural substrates of action identification.

PubMed

Marsh, Abigail A; Kozak, Megan N; Wegner, Daniel M; Reid, Marguerite E; Yu, Henry H; Blair, R J R

2010-12-01

Mentalization is the process by which an observer views a target as possessing higher cognitive faculties such as goals, intentions and desires. Mentalization can be assessed using action identification paradigms, in which observers choose mentalistic (goals-focused) or mechanistic (action-focused) descriptions of targets' actions. Neural structures that play key roles in inferring goals and intentions from others' observed or imagined actions include temporo-parietal junction, ventral premotor cortex and extrastriate body area. We hypothesized that these regions play a role in action identification as well. Data collected using functional magnetic resonance imaging (fMRI) confirmed our predictions that activity in ventral premotor cortex and middle temporal gyrus near the extrastriate body area varies both as a function of the valence of the target and the extent to which actions are identified as goal-directed. In addition, the inferior parietal lobule is preferentially engaged when participants identify the actions of mentalized targets. Functional connectivity analyses suggest support from other regions, including the medial prefrontal cortex and amygdala, during mentalization. We found correlations between action identification and Autism Quotient scores, suggesting that understanding the neural correlates of action identification may enhance our understanding of the underpinnings of essential social cognitive processes.

Neural hijacking: action of high-frequency electrical stimulation on cortical circuits.

PubMed

Cheney, P D; Griffin, D M; Van Acker, G M

2013-10-01

Electrical stimulation of the brain was one of the first experimental methods applied to understanding brain organization and function and it continues as a highly useful method both in research and clinical applications. Intracortical microstimulation (ICMS) involves applying electrical stimuli through a microelectrode suitable for recording the action potentials of single neurons. ICMS can be categorized into single-pulse stimulation; high-frequency, short-duration stimulation; and high-frequency, long-duration stimulation. For clinical and experimental reasons, considerable interest focuses on the mechanism of neural activation by electrical stimuli. In this article, we discuss recent results suggesting that action potentials evoked in cortical neurons by high-frequency electrical stimulation do not sum with the natural, behaviorally related background activity; rather, high-frequency stimulation eliminates and replaces natural activity. We refer to this as neural hijacking. We propose that a major component of the mechanism underlying neural hijacking is excitation of axons by ICMS and elimination of natural spikes by antidromic collision with stimulus-driven spikes evoked at high frequency. Evidence also supports neural hijacking as an important mechanism underlying the action of deep brain stimulation in the subthalamic nucleus and its therapeutic effect in treating Parkinson's disease.

Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations.

PubMed

Giese, Martin A; Rizzolatti, Giacomo

2015-10-07

Action recognition has received enormous interest in the field of neuroscience over the last two decades. In spite of this interest, the knowledge in terms of fundamental neural mechanisms that provide constraints for underlying computations remains rather limited. This fact stands in contrast with a wide variety of speculative theories about how action recognition might work. This review focuses on new fundamental electrophysiological results in monkeys, which provide constraints for the detailed underlying computations. In addition, we review models for action recognition and processing that have concrete mathematical implementations, as opposed to conceptual models. We think that only such implemented models can be meaningfully linked quantitatively to physiological data and have a potential to narrow down the many possible computational explanations for action recognition. In addition, only concrete implementations allow judging whether postulated computational concepts have a feasible implementation in terms of realistic neural circuits. Copyright © 2015 Elsevier Inc. All rights reserved.

Marketing actions can modulate neural representations of experienced pleasantness.

PubMed

Plassmann, Hilke; O'Doherty, John; Shiv, Baba; Rangel, Antonio

2008-01-22

Despite the importance and pervasiveness of marketing, almost nothing is known about the neural mechanisms through which it affects decisions made by individuals. We propose that marketing actions, such as changes in the price of a product, can affect neural representations of experienced pleasantness. We tested this hypothesis by scanning human subjects using functional MRI while they tasted wines that, contrary to reality, they believed to be different and sold at different prices. Our results show that increasing the price of a wine increases subjective reports of flavor pleasantness as well as blood-oxygen-level-dependent activity in medial orbitofrontal cortex, an area that is widely thought to encode for experienced pleasantness during experiential tasks. The paper provides evidence for the ability of marketing actions to modulate neural correlates of experienced pleasantness and for the mechanisms through which the effect operates.

A continuous-time neural model for sequential action.

PubMed

Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard

2014-11-05

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Strategies for improving neural signal detection using a neural-electronic interface.

PubMed

Szlavik, Robert B

2003-03-01

There have been various theoretical and experimental studies presented in the literature that focus on interfacing neurons with discrete electronic devices, such as transistors. From both a theoretical and experimental perspective, these studies have emphasized the variability in the characteristics of the detected action potential from the nerve cell. The demonstrated lack of reproducible fidelity of the nerve cell action potential at the device junction would make it impractical to implement these devices in any neural prosthetic application where reliable detection of the action potential was a prerequisite. In this study, the effects of several different physical parameters on the fidelity of the detected action potential at the device junction are investigated and discussed. The impact of variations in the extracellular resistivity, which directly affects the junction seal resistance, is studied along with the impact of variable nerve cell membrane capacitance and variations in the injected charge. These parameters are discussed in the context of their suitability to design manipulation for the purpose of improving the fidelity of the detected neural action potential. In addition to investigating the effects of variations in these parameters, the applicability of the linear equivalent circuit approach to calculating the junction potential is investigated.

Marketing actions can modulate neural representations of experienced pleasantness

PubMed Central

Plassmann, Hilke; O'Doherty, John; Shiv, Baba; Rangel, Antonio

2008-01-01

Despite the importance and pervasiveness of marketing, almost nothing is known about the neural mechanisms through which it affects decisions made by individuals. We propose that marketing actions, such as changes in the price of a product, can affect neural representations of experienced pleasantness. We tested this hypothesis by scanning human subjects using functional MRI while they tasted wines that, contrary to reality, they believed to be different and sold at different prices. Our results show that increasing the price of a wine increases subjective reports of flavor pleasantness as well as blood-oxygen-level-dependent activity in medial orbitofrontal cortex, an area that is widely thought to encode for experienced pleasantness during experiential tasks. The paper provides evidence for the ability of marketing actions to modulate neural correlates of experienced pleasantness and for the mechanisms through which the effect operates. PMID:18195362

Neural computations underlying inverse reinforcement learning in the human brain

PubMed Central

Pauli, Wolfgang M; Bossaerts, Peter; O'Doherty, John

2017-01-01

In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while learning about slot machines yielding hidden preferred and non-preferred food outcomes with varying probabilities, through observing the repeated slot choices of agents with similar and dissimilar food preferences. Using formal model comparison, we found that participants implemented inverse RL as opposed to a simple imitation strategy, in which the actions of the other agent are copied instead of inferring the underlying reward structure of the decision problem. Our computational fMRI analysis revealed that anterior dorsomedial prefrontal cortex encoded inferences about action-values within the value space of the agent as opposed to that of the observer, demonstrating that inverse RL is an abstract cognitive process divorceable from the values and concerns of the observer him/herself. PMID:29083301

Anaesthetic Tricaine Acts Preferentially on Neural Voltage-Gated Sodium Channels and Fails to Block Directly Evoked Muscle Contraction

PubMed Central

Attili, Seetharamaiah; Hughes, Simon M.

2014-01-01

Movements in animals arise through concerted action of neurons and skeletal muscle. General anaesthetics prevent movement and cause loss of consciousness by blocking neural function. Anaesthetics of the amino amide-class are thought to act by blockade of voltage-gated sodium channels. In fish, the commonly used anaesthetic tricaine methanesulphonate, also known as 3-aminobenzoic acid ethyl ester, metacaine or MS-222, causes loss of consciousness. However, its role in blocking action potentials in distinct excitable cells is unclear, raising the possibility that tricaine could act as a neuromuscular blocking agent directly causing paralysis. Here we use evoked electrical stimulation to show that tricaine efficiently blocks neural action potentials, but does not prevent directly evoked muscle contraction. Nifedipine-sensitive L-type Cav channels affecting movement are also primarily neural, suggesting that muscle Nav channels are relatively insensitive to tricaine. These findings show that tricaine used at standard concentrations in zebrafish larvae does not paralyse muscle, thereby diminishing concern that a direct action on muscle could mask a lack of general anaesthesia. PMID:25090007

Neural theory for the perception of causal actions.

PubMed

Fleischer, Falk; Christensen, Andrea; Caggiano, Vittorio; Thier, Peter; Giese, Martin A

2012-07-01

The efficient prediction of the behavior of others requires the recognition of their actions and an understanding of their action goals. In humans, this process is fast and extremely robust, as demonstrated by classical experiments showing that human observers reliably judge causal relationships and attribute interactive social behavior to strongly simplified stimuli consisting of simple moving geometrical shapes. While psychophysical experiments have identified critical visual features that determine the perception of causality and agency from such stimuli, the underlying detailed neural mechanisms remain largely unclear, and it is an open question why humans developed this advanced visual capability at all. We created pairs of naturalistic and abstract stimuli of hand actions that were exactly matched in terms of their motion parameters. We show that varying critical stimulus parameters for both stimulus types leads to very similar modulations of the perception of causality. However, the additional form information about the hand shape and its relationship with the object supports more fine-grained distinctions for the naturalistic stimuli. Moreover, we show that a physiologically plausible model for the recognition of goal-directed hand actions reproduces the observed dependencies of causality perception on critical stimulus parameters. These results support the hypothesis that selectivity for abstract action stimuli might emerge from the same neural mechanisms that underlie the visual processing of natural goal-directed action stimuli. Furthermore, the model proposes specific detailed neural circuits underlying this visual function, which can be evaluated in future experiments.

Evolution and Optimality of Similar Neural Mechanisms for Perception and Action during Search

PubMed Central

Zhang, Sheng; Eckstein, Miguel P.

2010-01-01

A prevailing theory proposes that the brain's two visual pathways, the ventral and dorsal, lead to differing visual processing and world representations for conscious perception than those for action. Others have claimed that perception and action share much of their visual processing. But which of these two neural architectures is favored by evolution? Successful visual search is life-critical and here we investigate the evolution and optimality of neural mechanisms mediating perception and eye movement actions for visual search in natural images. We implement an approximation to the ideal Bayesian searcher with two separate processing streams, one controlling the eye movements and the other stream determining the perceptual search decisions. We virtually evolved the neural mechanisms of the searchers' two separate pathways built from linear combinations of primary visual cortex receptive fields (V1) by making the simulated individuals' probability of survival depend on the perceptual accuracy finding targets in cluttered backgrounds. We find that for a variety of targets, backgrounds, and dependence of target detectability on retinal eccentricity, the mechanisms of the searchers' two processing streams converge to similar representations showing that mismatches in the mechanisms for perception and eye movements lead to suboptimal search. Three exceptions which resulted in partial or no convergence were a case of an organism for which the targets are equally detectable across the retina, an organism with sufficient time to foveate all possible target locations, and a strict two-pathway model with no interconnections and differential pre-filtering based on parvocellular and magnocellular lateral geniculate cell properties. Thus, similar neural mechanisms for perception and eye movement actions during search are optimal and should be expected from the effects of natural selection on an organism with limited time to search for food that is not equi-detectable across its retina and interconnected perception and action neural pathways. PMID:20838589

Lifelong learning of human actions with deep neural network self-organization.

PubMed

Parisi, German I; Tani, Jun; Weber, Cornelius; Wermter, Stefan

2017-12-01

Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Neural correlates of instrumental contingency learning: Differential effects of action-reward conjunction and disjunction

PubMed Central

Liljeholm, Mimi; Tricomi, Elizabeth; O’Doherty, John P.; Balleine, Bernard W.

2011-01-01

Contingency theories of goal-directed action propose that experienced disjunctions between an action and its specific consequences, as well as conjunctions between these events, contribute to encoding the action-outcome association. Although considerable behavioral research in rats and humans has provided evidence for this proposal, relatively little is known about the neural processes that contribute to the two components of the contingency calculation. Specifically, while recent findings suggest that the influence of action-outcome conjunctions on goal-directed learning is mediated by a circuit involving ventromedial prefrontal, medial orbitofrontal cortex and dorsomedial striatum, the neural processes that mediate the influence of experienced disjunctions between these events are unknown. Here we show differential responses to probabilities of conjunctive and disjunctive reward deliveries in the ventromedial prefrontal cortex, the dorsomedial striatum, and the inferior frontal gyrus. Importantly, activity in the inferior parietal lobule and the left middle frontal gyrus varied with a formal integration of the two reward probabilities, ΔP, as did response rates and explicit judgments of the causal efficacy of the action. PMID:21325514

Advances in Cochlear Implant Telemetry: Evoked Neural Responses, Electrical Field Imaging, and Technical Integrity

PubMed Central

Mens, Lucas H. M.

2007-01-01

During the last decade, cochlear implantation has evolved into a well-established treatment of deafness, predominantly because of many improvements in speech processing and the controlled excitation of the auditory nerve. Cochlear implants now also feature telemetry, which is highly useful to monitor the proper functioning of the implanted electronics and electrode contacts. Telemetry can also support the clinical management in young children and difficult cases where neural unresponsiveness is suspected. This article will review recent advances in the telemetry of the electrically evoked compound action potential that have made these measurements simple and routine procedures in most cases. The distribution of the electrical stimulus itself sampled by “electrical field imaging” reveals general patterns of current flow in the normal cochlea and gross abnormalities in individual patients; models have been developed to derive more subtle insights from an individual electrical field imaging. Finally, some thoughts are given to the extended application of telemetry, for example, in monitoring the neural responses or in combination with other treatments of the deaf ear. PMID:17709572

Neural mechanisms and models underlying joint action.

PubMed

Chersi, Fabian

2011-06-01

Humans, in particular, and to a lesser extent also other species of animals, possess the impressive capability of smoothly coordinating their actions with those of others. The great amount of work done in recent years in neuroscience has provided new insights into the processes involved in joint action, intention understanding, and task sharing. In particular, the discovery of mirror neurons, which fire both when animals execute actions and when they observe the same actions done by other individuals, has shed light on the intimate relationship between perception and action elucidating the direct contribution of motor knowledge to action understanding. Up to date, however, a detailed description of the neural processes involved in these phenomena is still mostly lacking. Building upon data from single neuron recordings in monkeys observing the actions of a demonstrator and then executing the same or a complementary action, this paper describes the functioning of a biologically constraint neural network model of the motor and mirror systems during joint action. In this model, motor sequences are encoded as independent neuronal chains that represent concatenations of elementary motor acts leading to a specific goal. Action execution and recognition are achieved through the propagation of activity within specific chains. Due to the dual property of mirror neurons, the same architecture is capable of smoothly integrating and switching between observed and self-generated action sequences, thus allowing to evaluate multiple hypotheses simultaneously, understand actions done by others, and to respond in an appropriate way.

The Brain as a Distributed Intelligent Processing System: An EEG Study

PubMed Central

da Rocha, Armando Freitas; Rocha, Fábio Theoto; Massad, Eduardo

2011-01-01

Background Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion The present results support these claims and the neural efficiency hypothesis. PMID:21423657

Intention, emotion, and action: a neural theory based on semantic pointers.

PubMed

Schröder, Tobias; Stewart, Terrence C; Thagard, Paul

2014-06-01

We propose a unified theory of intentions as neural processes that integrate representations of states of affairs, actions, and emotional evaluation. We show how this theory provides answers to philosophical questions about the concept of intention, psychological questions about human behavior, computational questions about the relations between belief and action, and neuroscientific questions about how the brain produces actions. Our theory of intention ties together biologically plausible mechanisms for belief, planning, and motor control. The computational feasibility of these mechanisms is shown by a model that simulates psychologically important cases of intention. © 2013 Cognitive Science Society, Inc.

Surface electromyographic amplitude does not identify differences in neural drive to synergistic muscles.

PubMed

Martinez-Valdes, Eduardo; Negro, Francesco; Falla, Deborah; De Nunzio, Alessandro Marco; Farina, Dario

2018-04-01

Surface electromyographic (EMG) signal amplitude is typically used to compare the neural drive to muscles. We experimentally investigated this association by studying the motor unit (MU) behavior and action potentials in the vastus medialis (VM) and vastus lateralis (VL) muscles. Eighteen participants performed isometric knee extensions at four target torques [10, 30, 50, and 70% of the maximum torque (MVC)] while high-density EMG signals were recorded from the VM and VL. The absolute EMG amplitude was greater for VM than VL ( P < 0.001), whereas the EMG amplitude normalized with respect to MVC was greater for VL than VM ( P < 0.04). Because differences in EMG amplitude can be due to both differences in the neural drive and in the size of the MU action potentials, we indirectly inferred the neural drives received by the two muscles by estimating the synaptic inputs received by the corresponding motor neuron pools. For this purpose, we analyzed the increase in discharge rate from recruitment to target torque for motor units matched by recruitment threshold in the two muscles. This analysis indicated that the two muscles received similar levels of neural drive. Nonetheless, the size of the MU action potentials was greater for VM than VL ( P < 0.001), and this difference explained most of the differences in EMG amplitude between the two muscles (~63% of explained variance). These results indicate that EMG amplitude, even following normalization, does not reflect the neural drive to synergistic muscles. Moreover, absolute EMG amplitude is mainly explained by the size of MU action potentials. NEW & NOTEWORTHY Electromyographic (EMG) amplitude is widely used to compare indirectly the strength of neural drive received by synergistic muscles. However, there are no studies validating this approach with motor unit data. Here, we compared between-muscles differences in surface EMG amplitude and motor unit behavior. The results clarify the limitations of surface EMG to interpret differences in neural drive between muscles.

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

1997-01-01

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

1998-01-01

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns

PubMed Central

Cruchet, Steeve; Gustafson, Kyle; Benton, Richard; Floreano, Dario

2015-01-01

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs—locomotor bouts—matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior. PMID:26600381

Distribution and function of voltage-gated sodium channels in the nervous system.

PubMed

Wang, Jun; Ou, Shao-Wu; Wang, Yun-Jie

2017-11-02

Voltage-gated sodium channels (VGSCs) are the basic ion channels for neuronal excitability, which are crucial for the resting potential and the generation and propagation of action potentials in neurons. To date, at least nine distinct sodium channel isoforms have been detected in the nervous system. Recent studies have identified that voltage-gated sodium channels not only play an essential role in the normal electrophysiological activities of neurons but also have a close relationship with neurological diseases. In this study, the latest research findings regarding the structure, type, distribution, and function of VGSCs in the nervous system and their relationship to neurological diseases, such as epilepsy, neuropathic pain, brain tumors, neural trauma, and multiple sclerosis, are reviewed in detail.

Neural networks and MIMD-multiprocessors

NASA Technical Reports Server (NTRS)

Vanhala, Jukka; Kaski, Kimmo

1990-01-01

Two artificial neural network models are compared. They are the Hopfield Neural Network Model and the Sparse Distributed Memory model. Distributed algorithms for both of them are designed and implemented. The run time characteristics of the algorithms are analyzed theoretically and tested in practice. The storage capacities of the networks are compared. Implementations are done using a distributed multiprocessor system.

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

PubMed Central

Schöner, Gregor; Gail, Alexander

2012-01-01

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations. PMID:23166483

Motor-Iconicity of Sign Language Does Not Alter the Neural Systems Underlying Tool and Action Naming

ERIC Educational Resources Information Center

Emmorey, Karen; Grabowski, Thomas; McCullough, Stephen; Damasio, Hannah; Ponto, Laurie; Hichwa, Richard; Bellugi, Ursula

2004-01-01

Positron emission tomography was used to investigate whether the motor-iconic basis of certain forms in American Sign Language (ASL) partially alters the neural systems engaged during lexical retrieval. Most ASL nouns denoting tools and ASL verbs referring to tool-based actions are produced with a handshape representing the human hand holding a…

Exploring the role of neural mirroring in children with autism spectrum disorder.

PubMed

Ruysschaert, Lieselot; Warreyn, Petra; Wiersema, Jan R; Oostra, Ann; Roeyers, Herbert

2014-04-01

Investigating the underlying neural mechanisms of autism spectrum disorder (ASD) has recently been influenced by the discovery of mirror neurons. These neurons, active during both observation and execution of actions, are thought to play a crucial role in imitation and other social-communicative skills that are often impaired in ASD. In the current electroencephalographic study, we investigated mu suppression, indicating neural mirroring in children with ASD between the ages of 24 and 48 months and age-matched typically developing children, during observation of goal-directed actions and non-goal-directed mimicked hand movements, as well as during action execution. Results revealed no significant group differences with significant central mu suppression in the ASD children and control children during both execution and observation of goal-directed actions and during observation of hand movements. Furthermore, no significant correlations between mu suppression on one hand and quality of imitation, age, and social communication questionnaire scores on the other hand were found. These findings challenge the "broken mirror" hypothesis of ASD, suggesting that impaired neural mirroring is not a distinctive feature of ASD. © 2014 International Society for Autism Research, Wiley Periodicals, Inc.

Increasingly complex representations of natural movies across the dorsal stream are shared between subjects.

PubMed

Güçlü, Umut; van Gerven, Marcel A J

2017-01-15

Recently, deep neural networks (DNNs) have been shown to provide accurate predictions of neural responses across the ventral visual pathway. We here explore whether they also provide accurate predictions of neural responses across the dorsal visual pathway, which is thought to be devoted to motion processing and action recognition. This is achieved by training deep neural networks to recognize actions in videos and subsequently using them to predict neural responses while subjects are watching natural movies. Moreover, we explore whether dorsal stream representations are shared between subjects. In order to address this question, we examine if individual subject predictions can be made in a common representational space estimated via hyperalignment. Results show that a DNN trained for action recognition can be used to accurately predict how dorsal stream responds to natural movies, revealing a correspondence in representations of DNN layers and dorsal stream areas. It is also demonstrated that models operating in a common representational space can generalize to responses of multiple or even unseen individual subjects to novel spatio-temporal stimuli in both encoding and decoding settings, suggesting that a common representational space underlies dorsal stream responses across multiple subjects. Copyright © 2015 Elsevier Inc. All rights reserved.

The extraction of neural strategies from the surface EMG: an update

PubMed Central

Merletti, Roberto; Enoka, Roger M.

2014-01-01

A surface EMG signal represents the linear transformation of motor neuron discharge times by the compound action potentials of the innervated muscle fibers and is often used as a source of information about neural activation of muscle. However, retrieving the embedded neural code from a surface EMG signal is extremely challenging. Most studies use indirect approaches in which selected features of the signal are interpreted as indicating certain characteristics of the neural code. These indirect associations are constrained by limitations that have been detailed previously (Farina D, Merletti R, Enoka RM. J Appl Physiol 96: 1486–1495, 2004) and are generally difficult to overcome. In an update on these issues, the current review extends the discussion to EMG-based coherence methods for assessing neural connectivity. We focus first on EMG amplitude cancellation, which intrinsically limits the association between EMG amplitude and the intensity of the neural activation and then discuss the limitations of coherence methods (EEG-EMG, EMG-EMG) as a way to assess the strength of the transmission of synaptic inputs into trains of motor unit action potentials. The debated influence of rectification on EMG spectral analysis and coherence measures is also discussed. Alternatively, there have been a number of attempts to identify the neural information directly by decomposing surface EMG signals into the discharge times of motor unit action potentials. The application of this approach is extremely powerful, but validation remains a central issue. PMID:25277737

Studies of stimulus parameters for seizure disruption using neural network simulations.

PubMed

Anderson, William S; Kudela, Pawel; Cho, Jounhong; Bergey, Gregory K; Franaszczuk, Piotr J

2007-08-01

A large scale neural network simulation with realistic cortical architecture has been undertaken to investigate the effects of external electrical stimulation on the propagation and evolution of ongoing seizure activity. This is an effort to explore the parameter space of stimulation variables to uncover promising avenues of research for this therapeutic modality. The model consists of an approximately 800 mum x 800 mum region of simulated cortex, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. The cell dynamics are governed by a modified version of the Hodgkin-Huxley equations in single compartment format. Axonal connections are patterned after histological data and published models of local cortical wiring. Stimulation induced action potentials take place at the axon initial segments, according to threshold requirements on the applied electric field distribution. Stimulation induced action potentials in horizontal axonal branches are also separately simulated. The calculations are performed on a 16 node distributed 32-bit processor system. Clear differences in seizure evolution are presented for stimulated versus the undisturbed rhythmic activity. Data is provided for frequency dependent stimulation effects demonstrating a plateau effect of stimulation efficacy as the applied frequency is increased from 60 to 200 Hz. Timing of the stimulation with respect to the underlying rhythmic activity demonstrates a phase dependent sensitivity. Electrode height and position effects are also presented. Using a dipole stimulation electrode arrangement, clear orientation effects of the dipole with respect to the model connectivity is also demonstrated. A sensitivity analysis of these results as a function of the stimulation threshold is also provided.

Spontaneous action potentials and neural coding in unmyelinated axons.

PubMed

O'Donnell, Cian; van Rossum, Mark C W

2015-04-01

The voltage-gated Na and K channels in neurons are responsible for action potential generation. Because ion channels open and close in a stochastic fashion, spontaneous (ectopic) action potentials can result even in the absence of stimulation. While spontaneous action potentials have been studied in detail in single-compartment models, studies on spatially extended processes have been limited. The simulations and analysis presented here show that spontaneous rate in unmyelinated axon depends nonmonotonically on the length of the axon, that the spontaneous activity has sub-Poisson statistics, and that neural coding can be hampered by the spontaneous spikes by reducing the probability of transmitting the first spike in a train.

Integrating robotic action with biologic perception: A brain-machine symbiosis theory

NASA Astrophysics Data System (ADS)

Mahmoudi, Babak

In patients with motor disability the natural cyclic flow of information between the brain and external environment is disrupted by their limb impairment. Brain-Machine Interfaces (BMIs) aim to provide new communication channels between the brain and environment by direct translation of brain's internal states into actions. For enabling the user in a wide range of daily life activities, the challenge is designing neural decoders that autonomously adapt to different tasks, environments, and to changes in the pattern of neural activity. In this dissertation, a novel decoding framework for BMIs is developed in which a computational agent autonomously learns how to translate neural states into action based on maximization of a measure of shared goal between user and the agent. Since the agent and brain share the same goal, a symbiotic relationship between them will evolve therefore this decoding paradigm is called a Brain-Machine Symbiosis (BMS) framework. A decoding agent was implemented within the BMS framework based on the Actor-Critic method of Reinforcement Learning. The rule of the Actor as a neural decoder was to find mapping between the neural representation of motor states in the primary motor cortex (MI) and robot actions in order to solve reaching tasks. The Actor learned the optimal control policy using an evaluative feedback that was estimated by the Critic directly from the user's neural activity of the Nucleus Accumbens (NAcc). Through a series of computational neuroscience studies in a cohort of rats it was demonstrated that NAcc could provide a useful evaluative feedback by predicting the increase or decrease in the probability of earning reward based on the environmental conditions. Using a closed-loop BMI simulator it was demonstrated the Actor-Critic decoding architecture was able to adapt to different tasks as well as changes in the pattern of neural activity. The custom design of a dual micro-wire array enabled simultaneous implantation of MI and NAcc for the development of a full closed-loop system. The Actor-Critic decoding architecture was able to solve the brain-controlled reaching task using a robotic arm by capturing the interdependency between the simultaneous action representation in MI and reward expectation in NAcc.

Incongruent Imagery Interferes with Action Initiation

ERIC Educational Resources Information Center

Ramsey, Richard; Cumming, Jennifer; Eastough, Daniel; Edwards, Martin G.

2010-01-01

It has been suggested that representing an action through observation and imagery share neural processes with action execution. In support of this view, motor-priming research has shown that observing an action can influence action initiation. However, there is little motor-priming research showing that imagining an action can modulate action…

A Neural Network Aero Design System for Advanced Turbo-Engines

NASA Technical Reports Server (NTRS)

Sanz, Jose M.

1999-01-01

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Action recognition is sensitive to the identity of the actor.

PubMed

Ferstl, Ylva; Bülthoff, Heinrich; de la Rosa, Stephan

2017-09-01

Recognizing who is carrying out an action is essential for successful human interaction. The cognitive mechanisms underlying this ability are little understood and have been subject of discussions in embodied approaches to action recognition. Here we examine one solution, that visual action recognition processes are at least partly sensitive to the actor's identity. We investigated the dependency between identity information and action related processes by testing the sensitivity of neural action recognition processes to clothing and facial identity information with a behavioral adaptation paradigm. Our results show that action adaptation effects are in fact modulated by both clothing information and the actor's facial identity. The finding demonstrates that neural processes underlying action recognition are sensitive to identity information (including facial identity) and thereby not exclusively tuned to actions. We suggest that such response properties are useful to help humans in knowing who carried out an action. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Precursor processes of human self-initiated action.

PubMed

Khalighinejad, Nima; Schurger, Aaron; Desantis, Andrea; Zmigrod, Leor; Haggard, Patrick

2018-01-15

A gradual buildup of electrical potential over motor areas precedes self-initiated movements. Recently, such "readiness potentials" (RPs) were attributed to stochastic fluctuations in neural activity. We developed a new experimental paradigm that operationalized self-initiated actions as endogenous 'skip' responses while waiting for target stimuli in a perceptual decision task. We compared these to a block of trials where participants could not choose when to skip, but were instead instructed to skip. Frequency and timing of motor action were therefore balanced across blocks, so that conditions differed only in how the timing of skip decisions was generated. We reasoned that across-trial variability of EEG could carry as much information about the source of skip decisions as the mean RP. EEG variability decreased more markedly prior to self-initiated compared to externally-triggered skip actions. This convergence suggests a consistent preparatory process prior to self-initiated action. A leaky stochastic accumulator model could reproduce this convergence given the additional assumption of a systematic decrease in input noise prior to self-initiated actions. Our results may provide a novel neurophysiological perspective on the topical debate regarding whether self-initiated actions arise from a deterministic neurocognitive process, or from neural stochasticity. We suggest that the key precursor of self-initiated action may manifest as a reduction in neural noise. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain.

PubMed

Caballero, Javier A; Lepora, Nathan F; Gurney, Kevin N

2015-01-01

Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT) for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI) - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD) or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices). Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity). These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks.

Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain

PubMed Central

Caballero, Javier A.; Lepora, Nathan F.; Gurney, Kevin N.

2015-01-01

Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT) for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI) - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD) or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices). Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity). These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks. PMID:25923907

Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

PubMed

Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain

2016-10-01

This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.

Neural dynamics of speech act comprehension: an MEG study of naming and requesting.

PubMed

Egorova, Natalia; Pulvermüller, Friedemann; Shtyrov, Yury

2014-05-01

The neurobiological basis and temporal dynamics of communicative language processing pose important yet unresolved questions. It has previously been suggested that comprehension of the communicative function of an utterance, i.e. the so-called speech act, is supported by an ensemble of neural networks, comprising lexico-semantic, action and mirror neuron as well as theory of mind circuits, all activated in concert. It has also been demonstrated that recognition of the speech act type occurs extremely rapidly. These findings however, were obtained in experiments with insufficient spatio-temporal resolution, thus possibly concealing important facets of the neural dynamics of the speech act comprehension process. Here, we used magnetoencephalography to investigate the comprehension of Naming and Request actions performed with utterances controlled for physical features, psycholinguistic properties and the probability of occurrence in variable contexts. The results show that different communicative actions are underpinned by a dynamic neural network, which differentiates between speech act types very early after the speech act onset. Within 50-90 ms, Requests engaged mirror-neuron action-comprehension systems in sensorimotor cortex, possibly for processing action knowledge and intentions. Still, within the first 200 ms of stimulus onset (100-150 ms), Naming activated brain areas involved in referential semantic retrieval. Subsequently (200-300 ms), theory of mind and mentalising circuits were activated in medial prefrontal and temporo-parietal areas, possibly indexing processing of intentions and assumptions of both communication partners. This cascade of stages of processing information about actions and intentions, referential semantics, and theory of mind may underlie dynamic and interactive speech act comprehension.

Fluctuations in central and peripheral temperatures induced by intravenous nicotine: central and peripheral contributions

PubMed Central

Tang, Jeremy; Kiyatkin, Eugene A.

2011-01-01

Nicotine (NIC) is a highly addictive substance that interacts with different subtypes of nicotinic acetylcholine receptors widely distributed in the central and peripheral nervous systems. While the direct action of NIC on central neurons appears to be essential for its reinforcing properties, the role of peripheral actions of this drug remains a matter of controversy. In this study, we examined changes in locomotor activity and temperature fluctuations in the brain (nucleus accumbens and ventral tegmental area), temporal muscle, and skin induced by intravenous (iv) NIC at low human-relevant doses (10 and 30 μg/kg) in freely moving rats. These effects were compared to those induced by social interaction, an arousing procedure that induces behavioral activation and temperature responses via pure neural mechanism procedure, and iv injections of a peripherally acting NIC analogue, NIC pyrrolidine methiodide (NIC-PM) used at equimolar doses. We found that NIC at 30 μg/kg induces a modest locomotor activation, rapid and strong decrease in skin temperature, and weak increases in brain and muscle temperature. While these effects were qualitatively similar to those induced by social interaction, they were much weaker and showed a tendency to increase with repeated drug administrations. In contrast, NIC-PM did not affect locomotion and induced much weaker than NIC increases in brain and muscle temperatures and decreases in skin temperature; these effects showed a tendency to be weaker with repeated drug administrations. Our data indicate that NIC's actions in the brain are essential to induce locomotor activation and brain and body hyperthermic responses. However, rapid peripheral action of NIC on sensory afferents could be an important factor in triggering its central effects, contributing to neural and physiological activation following repeated drug use. PMID:21295014

GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

PubMed

Lin, C T; Jou, C P

2000-01-01

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

DATA MAYHEM VERSUS NIMBLE INFORMATION: TRANSFORMING HECTIC IMAGERY INTELLIGENCE DATA INTO ACTIONABLE INFORMATION USING ARTIFICIAL NEURAL NETWORKS

DTIC Science & Technology

2017-10-01

AU/ACSC/MORALES/AY17 AIR COMMAND AND STAFF COLLEGE DISTANCE LEARNING AIR UNIVERSITY DATA MAYHEM VERSUS NIMBLE INFORMATION : TRANSFORMING...HECTIC IMAGERY INTELLIGENCE DATA INTO ACTIONABLE INFORMATION USING ARTIFICIAL NEURAL NETWORKS by Luis A. Morales, Major, USAF A Research...finding solutions to compliment and supplement human analysts’ capacity, so intelligence and information can reach operators and end-users at the

Neural Correlates of Human Action Observation in Hearing and Deaf Subjects

PubMed Central

Corina, David; Chiu, Yi-Shiuan; Knapp, Heather; Greenwald, Ralf; Jose-Robertson, Lucia San; Braun, Allen

2007-01-01

Accumulating evidence has suggested the existence of a human action recognition system involving inferior frontal, parietal, and superior temporal regions that may participate in both the perception and execution of actions. However, little is known about the specificity of this system in response to different forms of human action. Here we present data from PET neuroimaging studies from passive viewing of three distinct action types, intransitive self-oriented actions (e.g., stretching, rubbing one’s eyes, etc.), transitive object-oriented actions (e.g., opening a door, lifting a cup to the lips to drink), and the abstract, symbolic actions–signs used in American Sign Language. Our results show that these different classes of human actions engage a frontal/parietal/STS human action recognition system in a highly similar fashion. However, the results indicate that this neural consistency across motion classes is true primarily for hearing subjects. Data from deaf signers shows a non-uniform response to different classes of human actions. As expected, deaf signers engaged left-hemisphere perisylvian language areas during the perception of signed language signs. Surprisingly, these subjects did not engage the expected frontal/parietal/STS circuitry during passive viewing of non-linguistic actions, but rather reliably activated middle-occipital temporal-ventral regions which are known to participate in the detection of human bodies, faces, and movements. Comparisons with data from hearing subjects establish statistically significant contributions of middle-occipital temporal-ventral during the processing of non-linguistic actions in deaf signers. These results suggest that during human motion processing, deaf individuals may engage specialized neural systems that allow for rapid, online differentiation of meaningful linguistic actions from non-linguistic human movements. PMID:17459349

A cardiac electrical activity model based on a cellular automata system in comparison with neural network model.

PubMed

Khan, Muhammad Sadiq Ali; Yousuf, Sidrah

2016-03-01

Cardiac Electrical Activity is commonly distributed into three dimensions of Cardiac Tissue (Myocardium) and evolves with duration of time. The indicator of heart diseases can occur randomly at any time of a day. Heart rate, conduction and each electrical activity during cardiac cycle should be monitor non-invasively for the assessment of "Action Potential" (regular) and "Arrhythmia" (irregular) rhythms. Many heart diseases can easily be examined through Automata model like Cellular Automata concepts. This paper deals with the different states of cardiac rhythms using cellular automata with the comparison of neural network also provides fast and highly effective stimulation for the contraction of cardiac muscles on the Atria in the result of genesis of electrical spark or wave. The specific formulated model named as "States of automaton Proposed Model for CEA (Cardiac Electrical Activity)" by using Cellular Automata Methodology is commonly shows the three states of cardiac tissues conduction phenomena (i) Resting (Relax and Excitable state), (ii) ARP (Excited but Absolutely refractory Phase i.e. Excited but not able to excite neighboring cells) (iii) RRP (Excited but Relatively Refractory Phase i.e. Excited and able to excite neighboring cells). The result indicates most efficient modeling with few burden of computation and it is Action Potential during the pumping of blood in cardiac cycle.

Emotional Complexity and the Neural Representation of Emotion in Motion

PubMed Central

Barnard, Philip J.; Lawrence, Andrew D.

2011-01-01

According to theories of emotional complexity, individuals low in emotional complexity encode and represent emotions in visceral or action-oriented terms, whereas individuals high in emotional complexity encode and represent emotions in a differentiated way, using multiple emotion concepts. During functional magnetic resonance imaging, participants viewed valenced animated scenarios of simple ball-like figures attending either to social or spatial aspects of the interactions. Participant’s emotional complexity was assessed using the Levels of Emotional Awareness Scale. We found a distributed set of brain regions previously implicated in processing emotion from facial, vocal and bodily cues, in processing social intentions, and in emotional response, were sensitive to emotion conveyed by motion alone. Attention to social meaning amplified the influence of emotion in a subset of these regions. Critically, increased emotional complexity correlated with enhanced processing in a left temporal polar region implicated in detailed semantic knowledge; with a diminished effect of social attention; and with increased differentiation of brain activity between films of differing valence. Decreased emotional complexity was associated with increased activity in regions of pre-motor cortex. Thus, neural coding of emotion in semantic vs action systems varies as a function of emotional complexity, helping reconcile puzzling inconsistencies in neuropsychological investigations of emotion recognition. PMID:20207691

Reduction in Neural Performance following Recovery from Anoxic Stress Is Mimicked by AMPK Pathway Activation

PubMed Central

Money, Tomas G. A.; Sproule, Michael K. J.; Hamour, Amr F.; Robertson, R. Meldrum

2014-01-01

Nervous systems are energetically expensive to operate and maintain. Both synaptic and action potential signalling require a significant investment to maintain ion homeostasis. We have investigated the tuning of neural performance following a brief period of anoxia in a well-characterized visual pathway in the locust, the LGMD/DCMD looming motion-sensitive circuit. We hypothesised that the energetic cost of signalling can be dynamically modified by cellular mechanisms in response to metabolic stress. We examined whether recovery from anoxia resulted in a decrease in excitability of the electrophysiological properties in the DCMD neuron. We further examined the effect of these modifications on behavioural output. We show that recovery from anoxia affects metabolic rate, flight steering behaviour, and action potential properties. The effects of anoxia on action potentials can be mimicked by activation of the AMPK metabolic pathway. We suggest this is evidence of a coordinated cellular mechanism to reduce neural energetic demand following an anoxic stress. Together, this represents a dynamically-regulated means to link the energetic demands of neural signaling with the environmental constraints faced by the whole animal. PMID:24533112

Reduction in neural performance following recovery from anoxic stress is mimicked by AMPK pathway activation.

PubMed

Money, Tomas G A; Sproule, Michael K J; Hamour, Amr F; Robertson, R Meldrum

2014-01-01

Nervous systems are energetically expensive to operate and maintain. Both synaptic and action potential signalling require a significant investment to maintain ion homeostasis. We have investigated the tuning of neural performance following a brief period of anoxia in a well-characterized visual pathway in the locust, the LGMD/DCMD looming motion-sensitive circuit. We hypothesised that the energetic cost of signalling can be dynamically modified by cellular mechanisms in response to metabolic stress. We examined whether recovery from anoxia resulted in a decrease in excitability of the electrophysiological properties in the DCMD neuron. We further examined the effect of these modifications on behavioural output. We show that recovery from anoxia affects metabolic rate, flight steering behaviour, and action potential properties. The effects of anoxia on action potentials can be mimicked by activation of the AMPK metabolic pathway. We suggest this is evidence of a coordinated cellular mechanism to reduce neural energetic demand following an anoxic stress. Together, this represents a dynamically-regulated means to link the energetic demands of neural signaling with the environmental constraints faced by the whole animal.

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants.

PubMed

Cruz-Garza, Jesus G; Hernandez, Zachery R; Tse, Teresa; Caducoy, Eunice; Abibullaev, Berdakh; Contreras-Vidal, Jose L

2015-10-04

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing "in action and in context". This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.

A neural network approach to burst detection.

PubMed

Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

2002-01-01

This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

The development and modeling of devices and paradigms for transcranial magnetic stimulation

PubMed Central

Goetz, Stefan M.; Deng, Zhi-De

2017-01-01

Magnetic stimulation is a noninvasive neurostimulation technique that can evoke action potentials and modulate neural circuits through induced electric fields. Biophysical models of magnetic stimulation have become a major driver for technological developments and the understanding of the mechanisms of magnetic neurostimulation and neuromodulation. Major technological developments involve stimulation coils with different spatial characteristics and pulse sources to control the pulse waveform. While early technological developments were the result of manual design and invention processes, there is a trend in both stimulation coil and pulse source design to mathematically optimize parameters with the help of computational models. To date, macroscopically highly realistic spatial models of the brain as well as peripheral targets, and user-friendly software packages enable researchers and practitioners to simulate the treatment-specific and induced electric field distribution in the brains of individual subjects and patients. Neuron models further introduce the microscopic level of neural activation to understand the influence of activation dynamics in response to different pulse shapes. A number of models that were designed for online calibration to extract otherwise covert information and biomarkers from the neural system recently form a third branch of modeling. PMID:28443696

The development and modelling of devices and paradigms for transcranial magnetic stimulation.

PubMed

Goetz, Stefan M; Deng, Zhi-De

2017-04-01

Magnetic stimulation is a non-invasive neurostimulation technique that can evoke action potentials and modulate neural circuits through induced electric fields. Biophysical models of magnetic stimulation have become a major driver for technological developments and the understanding of the mechanisms of magnetic neurostimulation and neuromodulation. Major technological developments involve stimulation coils with different spatial characteristics and pulse sources to control the pulse waveform. While early technological developments were the result of manual design and invention processes, there is a trend in both stimulation coil and pulse source design to mathematically optimize parameters with the help of computational models. To date, macroscopically highly realistic spatial models of the brain, as well as peripheral targets, and user-friendly software packages enable researchers and practitioners to simulate the treatment-specific and induced electric field distribution in the brains of individual subjects and patients. Neuron models further introduce the microscopic level of neural activation to understand the influence of activation dynamics in response to different pulse shapes. A number of models that were designed for online calibration to extract otherwise covert information and biomarkers from the neural system recently form a third branch of modelling.

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

PubMed

Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming

2018-05-01

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.

The role of efference copy in striatal learning.

PubMed

Fee, Michale S

2014-04-01

Reinforcement learning requires the convergence of signals representing context, action, and reward. While models of basal ganglia function have well-founded hypotheses about the neural origin of signals representing context and reward, the function and origin of signals representing action are less clear. Recent findings suggest that exploratory or variable behaviors are initiated by a wide array of 'action-generating' circuits in the midbrain, brainstem, and cortex. Thus, in order to learn, the striatum must incorporate an efference copy of action decisions made in these action-generating circuits. Here we review several recent neural models of reinforcement learning that emphasize the role of efference copy signals. Also described are ideas about how these signals might be integrated with inputs signaling context and reward. Copyright © 2014 Elsevier Ltd. All rights reserved.

Neural Mirroring Systems: Exploring the EEG Mu Rhythm in Human Infancy

PubMed Central

Marshall, Peter J.; Meltzoff, Andrew N.

2010-01-01

How do human children come to understand the actions of other people? What neural systems are associated with the processing of others’ actions and how do these systems develop, starting in infancy? These questions span cognitive psychology and developmental cognitive neuroscience, and addressing them has important implications for the study of social cognition. A large amount of research has used behavioral measures to investigate infants’ imitation of the actions of other people; a related but smaller literature has begun to use neurobiological measures to study of infants’ action representation. Here we focus on experiments employing electroencephalographic (EEG) techniques for assessing mu rhythm desynchronization in infancy, and analyze how this work illuminates the links between action perception and production prior to the onset of language. PMID:21528008

The Cluster Variation Method: A Primer for Neuroscientists.

PubMed

Maren, Alianna J

2016-09-30

Effective Brain-Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables , is defined in terms of a single interaction enthalpy parameter ( h ) for the case of an equiprobable distribution of bistate (neural/neural ensemble) units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution) yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found.

The Cluster Variation Method: A Primer for Neuroscientists

PubMed Central

Maren, Alianna J.

2016-01-01

Effective Brain–Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h) for the case of an equiprobable distribution of bistate (neural/neural ensemble) units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution) yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found. PMID:27706022

Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

A Neural Network Aero Design System for Advanced Turbo-Engines

NASA Technical Reports Server (NTRS)

Sanz, Jose M.

1999-01-01

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a neural network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. The neural network technique works well not only as an interpolating device but also as an extrapolating device to achieve blade designs from a given database. Two validating test cases are discussed.

Interplay Between Conceptual Expectations and Movement Predictions Underlies Action Understanding.

PubMed

Ondobaka, Sasha; de Lange, Floris P; Wittmann, Marco; Frith, Chris D; Bekkering, Harold

2015-09-01

Recent accounts of understanding goal-directed action underline the importance of a hierarchical predictive architecture. However, the neural implementation of such an architecture remains elusive. In the present study, we used functional neuroimaging to quantify brain activity associated with predicting physical movements, as they were modulated by conceptual-expectations regarding the purpose of the object involved in the action. Participants observed object-related actions preceded by a cue that generated both conceptual goal expectations and movement goal predictions. In 2 tasks, observers judged whether conceptual or movement goals matched or mismatched the cue. At the conceptual level, expected goals specifically recruited the posterior cingulate cortex, irrespectively of the task and the perceived movement goal. At the movement level, neural activation of the parieto-frontal circuit, including inferior frontal gyrus and the inferior parietal lobe, reflected unpredicted movement goals. Crucially, this movement prediction error was only present when the purpose of the involved object was expected. These findings provide neural evidence that prior conceptual expectations influence processing of physical movement goals and thereby support the hierarchical predictive account of action processing. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Robust spike classification based on frequency domain neural waveform features.

PubMed

Yang, Chenhui; Yuan, Yuan; Si, Jennie

2013-12-01

We introduce a new spike classification algorithm based on frequency domain features of the spike snippets. The goal for the algorithm is to provide high classification accuracy, low false misclassification, ease of implementation, robustness to signal degradation, and objectivity in classification outcomes. In this paper, we propose a spike classification algorithm based on frequency domain features (CFDF). It makes use of frequency domain contents of the recorded neural waveforms for spike classification. The self-organizing map (SOM) is used as a tool to determine the cluster number intuitively and directly by viewing the SOM output map. After that, spike classification can be easily performed using clustering algorithms such as the k-Means. In conjunction with our previously developed multiscale correlation of wavelet coefficient (MCWC) spike detection algorithm, we show that the MCWC and CFDF detection and classification system is robust when tested on several sets of artificial and real neural waveforms. The CFDF is comparable to or outperforms some popular automatic spike classification algorithms with artificial and real neural data. The detection and classification of neural action potentials or neural spikes is an important step in single-unit-based neuroscientific studies and applications. After the detection of neural snippets potentially containing neural spikes, a robust classification algorithm is applied for the analysis of the snippets to (1) extract similar waveforms into one class for them to be considered coming from one unit, and to (2) remove noise snippets if they do not contain any features of an action potential. Usually, a snippet is a small 2 or 3 ms segment of the recorded waveform, and differences in neural action potentials can be subtle from one unit to another. Therefore, a robust, high performance classification system like the CFDF is necessary. In addition, the proposed algorithm does not require any assumptions on statistical properties of the noise and proves to be robust under noise contamination.

Imitation, empathy, and mirror neurons.

PubMed

Iacoboni, Marco

2009-01-01

There is a convergence between cognitive models of imitation, constructs derived from social psychology studies on mimicry and empathy, and recent empirical findings from the neurosciences. The ideomotor framework of human actions assumes a common representational format for action and perception that facilitates imitation. Furthermore, the associative sequence learning model of imitation proposes that experience-based Hebbian learning forms links between sensory processing of the actions of others and motor plans. Social psychology studies have demonstrated that imitation and mimicry are pervasive, automatic, and facilitate empathy. Neuroscience investigations have demonstrated physiological mechanisms of mirroring at single-cell and neural-system levels that support the cognitive and social psychology constructs. Why were these neural mechanisms selected, and what is their adaptive advantage? Neural mirroring solves the "problem of other minds" (how we can access and understand the minds of others) and makes intersubjectivity possible, thus facilitating social behavior.

Perception as a Route for Motor Skill Learning: Perspectives from Neuroscience.

PubMed

Ossmy, Ori; Mukamel, Roy

2018-04-22

Learning a motor skill requires physical practice that engages neural networks involved in movement. These networks have also been found to be engaged during perception of sensory signals associated with actions. Nonetheless, despite extensive evidence for the existence of such sensory-evoked neural activity in motor pathways, much less is known about their contribution to learning and actual changes in behavior. Primate studies usually involve an overlearned task while studies in humans have largely focused on characterizing activity of the action observation network (AON) in the context of action understanding, theory of mind, and social interactions. Relatively few studies examined neural plasticity induced by perception and its role in transfer of motor knowledge. Here, we review this body of literature and point to future directions for the development of alternative, physiologically grounded ways in which sensory signals could be harnessed to improve motor skills. Copyright © 2018. Published by Elsevier Ltd.

Vicarious Neural Processing of Outcomes during Observational Learning

PubMed Central

Monfardini, Elisabetta; Gazzola, Valeria; Boussaoud, Driss

2013-01-01

Learning what behaviour is appropriate in a specific context by observing the actions of others and their outcomes is a key constituent of human cognition, because it saves time and energy and reduces exposure to potentially dangerous situations. Observational learning of associative rules relies on the ability to map the actions of others onto our own, process outcomes, and combine these sources of information. Here, we combined newly developed experimental tasks and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms that govern such observational learning. Results show that the neural systems involved in individual trial-and-error learning and in action observation and execution both participate in observational learning. In addition, we identified brain areas that specifically activate for others’ incorrect outcomes during learning in the posterior medial frontal cortex (pMFC), the anterior insula and the posterior superior temporal sulcus (pSTS). PMID:24040104

Neural activity predicts attitude change in cognitive dissonance.

PubMed

van Veen, Vincent; Krug, Marie K; Schooler, Jonathan W; Carter, Cameron S

2009-11-01

When our actions conflict with our prior attitudes, we often change our attitudes to be more consistent with our actions. This phenomenon, known as cognitive dissonance, is considered to be one of the most influential theories in psychology. However, the neural basis of this phenomenon is unknown. Using a Solomon four-group design, we scanned participants with functional MRI while they argued that the uncomfortable scanner environment was nevertheless a pleasant experience. We found that cognitive dissonance engaged the dorsal anterior cingulate cortex and anterior insula; furthermore, we found that the activation of these regions tightly predicted participants' subsequent attitude change. These effects were not observed in a control group. Our findings elucidate the neural representation of cognitive dissonance, and support the role of the anterior cingulate cortex in detecting cognitive conflict and the neural prediction of attitude change.

Model-based choices involve prospective neural activity

PubMed Central

Doll, Bradley B.; Duncan, Katherine D.; Simon, Dylan A.; Shohamy, Daphna; Daw, Nathaniel D.

2015-01-01

Decisions may arise via “model-free” repetition of previously reinforced actions, or by “model-based” evaluation, which is widely thought to follow from prospective anticipation of action consequences using a learned map or model. While choices and neural correlates of decision variables sometimes reflect knowledge of their consequences, it remains unclear whether this actually arises from prospective evaluation. Using functional MRI and a sequential reward-learning task in which paths contained decodable object categories, we found that humans’ model-based choices were associated with neural signatures of future paths observed at decision time, suggesting a prospective mechanism for choice. Prospection also covaried with the degree of model-based influences on neural correlates of decision variables, and was inversely related to prediction error signals thought to underlie model-free learning. These results dissociate separate mechanisms underlying model-based and model-free evaluation and support the hypothesis that model-based influences on choices and neural decision variables result from prospection. PMID:25799041

Expert athletes activate somatosensory and motor planning regions of the brain when passively listening to familiar sports sounds.

PubMed

Woods, Elizabeth A; Hernandez, Arturo E; Wagner, Victoria E; Beilock, Sian L

2014-06-01

The present functional magnetic resonance imaging study examined the neural response to familiar and unfamiliar, sport and non-sport environmental sounds in expert and novice athletes. Results revealed differential neural responses dependent on sports expertise. Experts had greater neural activation than novices in focal sensorimotor areas such as the supplementary motor area, and pre- and postcentral gyri. Novices showed greater activation than experts in widespread areas involved in perception (i.e. supramarginal, middle occipital, and calcarine gyri; precuneus; inferior and superior parietal lobules), and motor planning and processing (i.e. inferior frontal, middle frontal, and middle temporal gyri). These between-group neural differences also appeared as an expertise effect within specific conditions. Experts showed greater activation than novices during the sport familiar condition in regions responsible for auditory and motor planning, including the inferior frontal gyrus and the parietal operculum. Novices only showed greater activation than experts in the supramarginal gyrus and pons during the non-sport unfamiliar condition, and in the middle frontal gyrus during the sport unfamiliar condition. These results are consistent with the view that expert athletes are attuned to only the most familiar, highly relevant sounds and tune out unfamiliar, irrelevant sounds. Furthermore, these findings that athletes show activation in areas known to be involved in action planning when passively listening to sounds suggests that auditory perception of action can lead to the re-instantiation of neural areas involved in producing these actions, especially if someone has expertise performing the actions. Copyright © 2014. Published by Elsevier Inc.

Lateralization in motor facilitation during action observation: a TMS study.

PubMed

Aziz-Zadeh, Lisa; Maeda, Fumiko; Zaidel, Eran; Mazziotta, John; Iacoboni, Marco

2002-05-01

Action observation facilitates corticospinal excitability. This is presumably due to a premotor neural system that is active when we perform actions and when we observe actions performed by others. It has been speculated that this neural system is a precursor of neural systems subserving language. If this theory is true, we may expect hemispheric differences in the motor facilitation produced by action observation, with the language-dominant left hemisphere showing stronger facilitation than the right hemisphere. Furthermore, it has been suggested that body parts are recognized via cortical regions controlling sensory and motor processing associated with that body part. If this is true, then corticospinal facilitation during action observation should be modulated by the laterality of the observed body part. The present study addressed these two issues using TMS for each motor cortex separately as participants observed actions being performed by a left hand, a right hand, or a control stimulus on the computer screen. We found no overall difference between the right and left hemisphere for motor-evoked potential (MEP) size during action observation. However, when TMS was applied to the left motor cortex, MEPs were larger while observing right hand actions. Likewise, when TMS was applied to the right motor cortex, MEPs were larger while observing left hand actions. Our data do not suggest left hemisphere superiority in the facilitating effects of action observation on the motor system. However, they do support the notion of a sensory-motor loop according to which sensory stimulus properties (for example, the image of a left hand or a right hand) directly affect motor cortex activity, even when no motor output is required. The pattern of this effect is congruent with the pattern of motor representation in each hemisphere.

The neural mechanisms of learning from competitors.

PubMed

Howard-Jones, Paul A; Bogacz, Rafal; Yoo, Jee H; Leonards, Ute; Demetriou, Skevi

2010-11-01

Learning from competitors poses a challenge for existing theories of reward-based learning, which assume that rewarded actions are more likely to be executed in the future. Such a learning mechanism would disadvantage a player in a competitive situation because, since the competitor's loss is the player's gain, reward might become associated with an action the player should themselves avoid. Using fMRI, we investigated the neural activity of humans competing with a computer in a foraging task. We observed neural activity that represented the variables required for learning from competitors: the actions of the competitor (in the player's motor and premotor cortex) and the reward prediction error arising from the competitor's feedback. In particular, regions positively correlated with the unexpected loss of the competitor (which was beneficial to the player) included the striatum and those regions previously implicated in response inhibition. Our results suggest that learning in such contexts may involve the competitor's unexpected losses activating regions of the player's brain that subserve response inhibition, as the player learns to avoid the actions that produced them. Copyright 2010 Elsevier Inc. All rights reserved.

Post-movement beta rebound abnormality as indicator of mirror neuron system dysfunction in autistic spectrum disorder: an MEG study.

PubMed

Honaga, Eiko; Ishii, Ryouhei; Kurimoto, Ryu; Canuet, Leonides; Ikezawa, Koji; Takahashi, Hidetoshi; Nakahachi, Takayuki; Iwase, Masao; Mizuta, Ichiro; Yoshimine, Toshiki; Takeda, Masatoshi

2010-07-12

The mu rhythm is regarded as a physiological indicator of the human mirror neuron system (MNS). The dysfunctional MNS hypothesis in patients with autistic spectrum disorder (ASD) has often been tested using EEG and MEG, targeting mu rhythm suppression during action observation/execution, although with controversial results. We explored neural activity related to the MNS in patients with ASD, focusing on power increase in the beta frequency band after observation and execution of movements, known as post-movement beta rebound (PMBR). Multiple source beamformer (MSBF) and BrainVoyager QX were used for MEG source imaging and statistical group analysis, respectively. Seven patients with ASD and ten normal subjects participated in this study. During the MEG recordings, the subjects were asked to observe and later execute object-related hand actions performed by an experimenter. We found that both groups exhibited pronounced PMBR exceeding 20% when observing and executing actions with a similar topographic distribution of maximal activity. However, significantly reduced PMBR was found only during the observation condition in the patients relative to controls in cortical regions within the MNS, namely the sensorimotor area, premotor cortex and superior temporal gyrus. Reduced PMBR during the observation condition was also found in the medial prefrontal cortex. These results support the notion of a dysfunctional execution/observation matching system related to MNS impairment in patients with ASD, and the feasibility of using MEG to detect neural activity, in particular PMBR abnormalities, as an index of MNS dysfunction during performance of motor or cognitive tasks. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Thalamocortical and corticothalamic pathways differentially contribute to goal-directed behaviors in the rat

PubMed Central

Alcaraz, Fabien; Fresno, Virginie; Marchand, Alain R; Kremer, Eric J; Coutureau, Etienne

2018-01-01

Highly distributed neural circuits are thought to support adaptive decision-making in volatile and complex environments. Notably, the functional interactions between prefrontal and reciprocally connected thalamic nuclei areas may be important when choices are guided by current goal value or action-outcome contingency. We examined the functional involvement of selected thalamocortical and corticothalamic pathways connecting the dorsomedial prefrontal cortex (dmPFC) and the mediodorsal thalamus (MD) in the behaving rat. Using a chemogenetic approach to inhibit projection-defined dmPFC and MD neurons during an instrumental learning task, we show that thalamocortical and corticothalamic pathways differentially support goal attributes. Both pathways participate in adaptation to the current goal value, but only thalamocortical neurons are required to integrate current causal relationships. These data indicate that antiparallel flow of information within thalamocortical circuits may convey qualitatively distinct aspects of adaptive decision-making and highlight the importance of the direction of information flow within neural circuits. PMID:29405119

The basolateral amygdala in reward learning and addiction

PubMed Central

Wassum, Kate M.; Izquierdo, Alicia

2015-01-01

Sophisticated behavioral paradigms partnered with the emergence of increasingly selective techniques to target the basolateral amygdala (BLA) have resulted in an enhanced understanding of the role of this nucleus in learning and using reward information. Due to the wide variety of behavioral approaches many questions remain on the circumscribed role of BLA in appetitive behavior. In this review, we integrate conclusions of BLA function in reward-related behavior using traditional interference techniques (lesion, pharmacological inactivation) with those using newer methodological approaches in experimental animals that allow in vivo manipulation of cell type-specific populations and neural recordings. Secondly, from a review of appetitive behavioral tasks in rodents and monkeys and recent computational models of reward procurement, we derive evidence for BLA as a neural integrator of reward value, history, and cost parameters. Taken together, BLA codes specific and temporally dynamic outcome representations in a distributed network to orchestrate adaptive responses. We provide evidence that experiences with opiates and psychostimulants alter these outcome representations in BLA, resulting in long-term modified action. PMID:26341938

Interfacing to the brain’s motor decisions

PubMed Central

2017-01-01

It has been long known that neural activity, recorded with electrophysiological methods, contains rich information about a subject’s motor intentions, sensory experiences, allocation of attention, action planning, and even abstract thoughts. All these functions have been the subject of neurophysiological investigations, with the goal of understanding how neuronal activity represents behavioral parameters, sensory inputs, and cognitive functions. The field of brain-machine interfaces (BMIs) strives for a somewhat different goal: it endeavors to extract information from neural modulations to create a communication link between the brain and external devices. Although many remarkable successes have been already achieved in the BMI field, questions remain regarding the possibility of decoding high-order neural representations, such as decision making. Could BMIs be employed to decode the neural representations of decisions underlying goal-directed actions? In this review we lay out a framework that describes the computations underlying goal-directed actions as a multistep process performed by multiple cortical and subcortical areas. We then discuss how BMIs could connect to different decision-making steps and decode the neural processing ongoing before movements are initiated. Such decision-making BMIs could operate as a system with prediction that offers many advantages, such as shorter reaction time, better error processing, and improved unsupervised learning. To present the current state of the art, we review several recent BMIs incorporating decision-making components. PMID:28003406

Tempo Rubato : Animacy Speeds Up Time in the Brain

PubMed Central

Carrozzo, Mauro; Moscatelli, Alessandro; Lacquaniti, Francesco

2010-01-01

Background How do we estimate time when watching an action? The idea that events are timed by a centralized clock has recently been called into question in favour of distributed, specialized mechanisms. Here we provide evidence for a critical specialization: animate and inanimate events are separately timed by humans. Methodology/Principal Findings In different experiments, observers were asked to intercept a moving target or to discriminate the duration of a stationary flash while viewing different scenes. Time estimates were systematically shorter in the sessions involving human characters moving in the scene than in those involving inanimate moving characters. Remarkably, the animate/inanimate context also affected randomly intermingled trials which always depicted the same still character. Conclusions/Significance The existence of distinct time bases for animate and inanimate events might be related to the partial segregation of the neural networks processing these two categories of objects, and could enhance our ability to predict critically timed actions. PMID:21206749

An Adapting Auditory-motor Feedback Loop Can Contribute to Generating Vocal Repetition

PubMed Central

Brainard, Michael S.; Jin, Dezhe Z.

2015-01-01

Consecutive repetition of actions is common in behavioral sequences. Although integration of sensory feedback with internal motor programs is important for sequence generation, if and how feedback contributes to repetitive actions is poorly understood. Here we study how auditory feedback contributes to generating repetitive syllable sequences in songbirds. We propose that auditory signals provide positive feedback to ongoing motor commands, but this influence decays as feedback weakens from response adaptation during syllable repetitions. Computational models show that this mechanism explains repeat distributions observed in Bengalese finch song. We experimentally confirmed two predictions of this mechanism in Bengalese finches: removal of auditory feedback by deafening reduces syllable repetitions; and neural responses to auditory playback of repeated syllable sequences gradually adapt in sensory-motor nucleus HVC. Together, our results implicate a positive auditory-feedback loop with adaptation in generating repetitive vocalizations, and suggest sensory adaptation is important for feedback control of motor sequences. PMID:26448054

Event-Related Potentials Discriminate Familiar and Unusual Goal Outcomes in 5-Month-Olds and Adults

ERIC Educational Resources Information Center

Michel, Christine; Kaduk, Katharina; Ní Choisdealbha, Áine; Reid, Vincent M.

2017-01-01

Previous event-related potential (ERP) work has indicated that the neural processing of action sequences develops with age. Although adults and 9-month-olds use a semantic processing system, perceiving actions activates attentional processes in 7-month-olds. However, presenting a sequence of action context, action execution and action conclusion…

A Symbiotic Brain-Machine Interface through Value-Based Decision Making

PubMed Central

Mahmoudi, Babak; Sanchez, Justin C.

2011-01-01

Background In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC). Methodology The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. Conclusions Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain. PMID:21423797

Other ways of seeing: From behavior to neural mechanisms in the online “visual” control of action with sensory substitution

PubMed Central

Proulx, Michael J.; Gwinnutt, James; Dell’Erba, Sara; Levy-Tzedek, Shelly; de Sousa, Alexandra A.; Brown, David J.

2015-01-01

Vision is the dominant sense for perception-for-action in humans and other higher primates. Advances in sight restoration now utilize the other intact senses to provide information that is normally sensed visually through sensory substitution to replace missing visual information. Sensory substitution devices translate visual information from a sensor, such as a camera or ultrasound device, into a format that the auditory or tactile systems can detect and process, so the visually impaired can see through hearing or touch. Online control of action is essential for many daily tasks such as pointing, grasping and navigating, and adapting to a sensory substitution device successfully requires extensive learning. Here we review the research on sensory substitution for vision restoration in the context of providing the means of online control for action in the blind or blindfolded. It appears that the use of sensory substitution devices utilizes the neural visual system; this suggests the hypothesis that sensory substitution draws on the same underlying mechanisms as unimpaired visual control of action. Here we review the current state of the art for sensory substitution approaches to object recognition, localization, and navigation, and the potential these approaches have for revealing a metamodal behavioral and neural basis for the online control of action. PMID:26599473

A deep learning framework for causal shape transformation.

PubMed

Lore, Kin Gwn; Stoecklein, Daniel; Davies, Michael; Ganapathysubramanian, Baskar; Sarkar, Soumik

2018-02-01

Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the dependencies typically lie in the latent domain which may not be suitable for applications involving the prediction of a step-wise transformation sequence that is dependent on the previous states only in the visible domain with a known terminal state. We propose a hybrid architecture of convolution neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of causal actions that nonlinearly transform an input visual pattern or distribution into a target visual pattern or distribution with the same support and demonstrated its practicality in a real-world engineering problem involving the physics of fluids. We solved a high-dimensional one-to-many inverse mapping problem concerning microfluidic flow sculpting, where the use of deep learning methods as an inverse map is very seldom explored. This work serves as a fruitful use-case to applied scientists and engineers in how deep learning can be beneficial as a solution for high-dimensional physical problems, and potentially opening doors to impactful advance in fields such as material sciences and medical biology where multistep topological transformations is a key element. Copyright © 2017 Elsevier Ltd. All rights reserved.

Action mechanisms for social cognition: behavioral and neural correlates of developing Theory of Mind

PubMed Central

Bowman, Lindsay C.; Thorpe, Samuel G.; Cannon, Erin N.; Fox, Nathan A.

2016-01-01

Many psychological theories posit foundational links between two fundamental constructs: (1) our ability to produce, perceive, and represent action; and (2) our ability to understand the meaning and motivation behind the action (i.e. Theory of Mind; ToM). This position is contentious, however, and long-standing competing theories of social-cognitive development debate roles for basic action-processing in ToM. Developmental research is key to investigating these hypotheses, but whether individual differences in neural and behavioral measures of motor action relate to social-cognitive development is unknown. We examined 3- to 5-year-old children’s (N = 26) EEG mu-desynchronization during production of object-directed action, and explored associations between mu-desynchronization and children’s behavioral motor skills, behavioral action-representation abilities, and behavioral ToM. For children with high (but not low) mu-desynchronization, motor skill related to action-representation abilities, and action-representation mediated relations between motor skill and ToM. Results demonstrate novel foundational links between action-processing and ToM, suggesting that basic motor action may be a key mechanism for social-cognitive development, thus shedding light on the origins and emergence of higher social cognition. PMID:27573916

Using Arrays of Microelectrodes Implanted in Residual Peripheral Nerves to Provide Dexterous Control of, and Modulated Sensory Feedback from, a Hand Prosthesis

DTIC Science & Technology

2016-10-01

isolated action potentials or multi-action potential activity from residual peripheral nerve while patient intends movements of amputated hand/arm...Subtask 3.1: Mapping of neural activity (Months 4 – 36) • Patients will be asked to intend a number of individual finger and multiple finger flexion...during these intended movements. We will map the different intended movements onto the neural activity recorded on the electrodes of the micro-electrode

Periodicity and global exponential stability of generalized Cohen-Grossberg neural networks with discontinuous activations and mixed delays.

PubMed

Wang, Dongshu; Huang, Lihong

2014-03-01

In this paper, we investigate the periodic dynamical behaviors for a class of general Cohen-Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and the fixed point theorem of multi-valued maps, the existence of periodic solutions for the neural networks is obtained. After that, we derive some sufficient conditions for the global exponential stability and convergence of the neural networks, in terms of nonsmooth analysis theory with generalized Lyapunov approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, our results will also be valid. Moreover, our results extend previous works not only on discrete time-varying and distributed delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete time-varying and distributed delayed neural networks with discontinuous activations. We give some numerical examples to show the applicability and effectiveness of our main results. Copyright © 2013 Elsevier Ltd. All rights reserved.

Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.

PubMed

Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon

2016-01-01

Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.

Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events

PubMed Central

Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon

2016-01-01

Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate. PMID:28066225

Neural reuse of action perception circuits for language, concepts and communication.

PubMed

Pulvermüller, Friedemann

2018-01-01

Neurocognitive and neurolinguistics theories make explicit statements relating specialized cognitive and linguistic processes to specific brain loci. These linking hypotheses are in need of neurobiological justification and explanation. Recent mathematical models of human language mechanisms constrained by fundamental neuroscience principles and established knowledge about comparative neuroanatomy offer explanations for where, when and how language is processed in the human brain. In these models, network structure and connectivity along with action- and perception-induced correlation of neuronal activity co-determine neurocognitive mechanisms. Language learning leads to the formation of action perception circuits (APCs) with specific distributions across cortical areas. Cognitive and linguistic processes such as speech production, comprehension, verbal working memory and prediction are modelled by activity dynamics in these APCs, and combinatorial and communicative-interactive knowledge is organized in the dynamics within, and connections between APCs. The network models and, in particular, the concept of distributionally-specific circuits, can account for some previously not well understood facts about the cortical 'hubs' for semantic processing and the motor system's role in language understanding and speech sound recognition. A review of experimental data evaluates predictions of the APC model and alternative theories, also providing detailed discussion of some seemingly contradictory findings. Throughout, recent disputes about the role of mirror neurons and grounded cognition in language and communication are assessed critically. Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.

Motor resonance facilitates movement execution: an ERP and kinematic study

PubMed Central

Ménoret, Mathilde; Curie, Aurore; des Portes, Vincent; Nazir, Tatjana A.; Paulignan, Yves

2013-01-01

Action observation, simulation and execution share neural mechanisms that allow for a common motor representation. It is known that when these overlapping mechanisms are simultaneously activated by action observation and execution, motor performance is influenced by observation and vice versa. To understand the neural dynamics underlying this influence and to measure how variations in brain activity impact the precise kinematics of motor behavior, we coupled kinematics and electrophysiological recordings of participants while they performed and observed congruent or non-congruent actions or during action execution alone. We found that movement velocities and the trajectory deviations of the executed actions increased during the observation of congruent actions compared to the observation of non-congruent actions or action execution alone. This facilitation was also discernible in the motor-related potentials of the participants; the motor-related potentials were transiently more negative in the congruent condition around the onset of the executed movement, which occurred 300 ms after the onset of the observed movement. This facilitation seemed to depend not only on spatial congruency but also on the optimal temporal relationship of the observation and execution events. PMID:24133437

Neuro-Hypnotism: Prospects for Hypnosis and Neuroscience

PubMed Central

Kihlstrom, John F.

2012-01-01

The neurophysiological substrates of hypnosis have been subject to speculation since the phenomenon got its name. Until recently, much of this research has been geared toward understanding hypnosis itself, including the biological bases of individual differences in hypnotizability, state-dependent changes in cortical activity occurring with the induction of hypnosis, and the neural correlates of response to particular hypnotic suggestions (especially the clinically useful hypnotic analgesia). More recently, hypnosis has begun to be employed as a method for manipulating subjects' mental states, both cognitive and affective, to provide information about the neural substrates of experience, thought, and action. This instrumental use of hypnosis is particularly well-suited for identifying the neural correlates of conscious and unconscious perception and memory, and of voluntary and involuntary action. PMID:22748566

A behavioural and neural evaluation of prospective decision-making under risk

PubMed Central

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

2010-01-01

Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single choice contexts there is a dearth of similar knowledge about sequential choice. Classical utility-based models assume that decision-makers select and follow an optimal pre-determined strategy, irrespective of the particular order in which options are presented. An alternative model involves continuously re-evaluating decision utilities, without prescribing a specific future set of choices. Here, using behavioral and functional magnetic resonance imaging (fMRI) data, we studied human subjects in a sequential choice task and use these data to compare alternative decision models of valuation and strategy selection. We provide evidence that subjects adopt a model of re-evaluating decision utilities, where available strategies are continuously updated and combined in assessing action values. We validate this model by using simultaneously-acquired fMRI data to show that sequential choice evokes a pattern of neural response consistent with a tracking of anticipated distribution of future reward, as expected in such a model. Thus, brain activity evoked at each decision point reflects the expected mean, variance and skewness of possible payoffs, consistent with the idea that sequential choice evokes a prospective evaluation of both available strategies and possible outcomes. PMID:20980595

In defense of free will: Neuroscience and criminal responsibility.

PubMed

Nestor, Paul G

2018-04-20

Is neuroscience the death of free will and if so, does this mean the imminent demise of the psycho-legal practices related to insanity and criminal responsibility? For many scholars of neuro-jurisprudence, recent advances in brain sciences suggesting that the perception of free will is merely illusory, an epiphenomenon of unconscious brain activity, do indeed undermine our traditional understandings of moral and legal responsibility. In this paper, however, we reject this radical claim and argue that neuroscientific evidence can indeed reveal how free will actually works and how its underlying neural and perceptual machinery gives rise to our sense of responsibility for our actions. First, the experience of free will is recast in terms of neuroscientific studies of agency and willed action. Second, evidence is presented of a neural network model linking agency to widely-distributed brain areas encompassing frontal motor and parietal monitoring sites. We then apply these findings to criminal responsibility practices by demonstrating (a) how the experience of intentionality and agency is generated by specific interactions of this discrete frontal-parietal network, (b) how mental disease/defect may compromise this network, and (c) how such pathologies may lead to disturbances in the sense of agency that often are central to the phenomenological experience of psychosis. The paper concludes by examining criminal responsibility practices through the lens of cultural evolution of fairness and cooperation. Copyright © 2018 Elsevier Ltd. All rights reserved.

A behavioral and neural evaluation of prospective decision-making under risk.

PubMed

Symmonds, Mkael; Bossaerts, Peter; Dolan, Raymond J

2010-10-27

Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single-choice contexts, there is a dearth of similar knowledge about sequential choice. Classical utility-based models assume that decision-makers select and follow an optimal predetermined strategy, regardless of the particular order in which options are presented. An alternative model involves continuously reevaluating decision utilities, without prescribing a specific future set of choices. Here, using behavioral and functional magnetic resonance imaging (fMRI) data, we studied human subjects in a sequential choice task and use these data to compare alternative decision models of valuation and strategy selection. We provide evidence that subjects adopt a model of reevaluating decision utilities, in which available strategies are continuously updated and combined in assessing action values. We validate this model by using simultaneously acquired fMRI data to show that sequential choice evokes a pattern of neural response consistent with a tracking of anticipated distribution of future reward, as expected in such a model. Thus, brain activity evoked at each decision point reflects the expected mean, variance, and skewness of possible payoffs, consistent with the idea that sequential choice evokes a prospective evaluation of both available strategies and possible outcomes.

An integrative neural model of social perception, action observation, and theory of mind.

PubMed

Yang, Daniel Y-J; Rosenblau, Gabriela; Keifer, Cara; Pelphrey, Kevin A

2015-04-01

In the field of social neuroscience, major branches of research have been instrumental in describing independent components of typical and aberrant social information processing, but the field as a whole lacks a comprehensive model that integrates different branches. We review existing research related to the neural basis of three key neural systems underlying social information processing: social perception, action observation, and theory of mind. We propose an integrative model that unites these three processes and highlights the posterior superior temporal sulcus (pSTS), which plays a central role in all three systems. Furthermore, we integrate these neural systems with the dual system account of implicit and explicit social information processing. Large-scale meta-analyses based on Neurosynth confirmed that the pSTS is at the intersection of the three neural systems. Resting-state functional connectivity analysis with 1000 subjects confirmed that the pSTS is connected to all other regions in these systems. The findings presented in this review are specifically relevant for psychiatric research especially disorders characterized by social deficits such as autism spectrum disorder. Copyright © 2015 Elsevier Ltd. All rights reserved.

Neural Energy Supply-Consumption Properties Based on Hodgkin-Huxley Model

PubMed Central

2017-01-01

Electrical activity is the foundation of the neural system. Coding theories that describe neural electrical activity by the roles of action potential timing or frequency have been thoroughly studied. However, an alternative method to study coding questions is the energy method, which is more global and economical. In this study, we clearly defined and calculated neural energy supply and consumption based on the Hodgkin-Huxley model, during firing action potentials and subthreshold activities using ion-counting and power-integral model. Furthermore, we analyzed energy properties of each ion channel and found that, under the two circumstances, power synchronization of ion channels and energy utilization ratio have significant differences. This is particularly true of the energy utilization ratio, which can rise to above 100% during subthreshold activity, revealing an overdraft property of energy use. These findings demonstrate the distinct status of the energy properties during neuronal firings and subthreshold activities. Meanwhile, after introducing a synapse energy model, this research can be generalized to energy calculation of a neural network. This is potentially important for understanding the relationship between dynamical network activities and cognitive behaviors. PMID:28316842

An integrative neural model of social perception, action observation, and theory of mind

PubMed Central

Yang, Daniel Y.-J.; Rosenblau, Gabriela; Keifer, Cara; Pelphrey, Kevin A.

2016-01-01

In the field of social neuroscience, major branches of research have been instrumental in describing independent components of typical and aberrant social information processing, but the field as a whole lacks a comprehensive model that integrates different branches. We review existing research related to the neural basis of three key neural systems underlying social information processing: social perception, action observation, and theory of mind. We propose an integrative model that unites these three processes and highlights the posterior superior temporal sulcus (pSTS), which plays a central role in all three systems. Furthermore, we integrate these neural systems with the dual system account of implicit and explicit social information processing. Large-scale meta-analyses based on Neurosynth confirmed that the pSTS is at the intersection of the three neural systems. Resting-state functional connectivity analysis with 1000 subjects confirmed that the pSTS is connected to all other regions in these systems. The findings presented in this review are specifically relevant for psychiatric research especially disorders characterized by social deficits such as autism spectrum disorder. PMID:25660957

Confidence as Bayesian Probability: From Neural Origins to Behavior.

PubMed

Meyniel, Florent; Sigman, Mariano; Mainen, Zachary F

2015-10-07

Research on confidence spreads across several sub-fields of psychology and neuroscience. Here, we explore how a definition of confidence as Bayesian probability can unify these viewpoints. This computational view entails that there are distinct forms in which confidence is represented and used in the brain, including distributional confidence, pertaining to neural representations of probability distributions, and summary confidence, pertaining to scalar summaries of those distributions. Summary confidence is, normatively, derived or "read out" from distributional confidence. Neural implementations of readout will trade off optimality versus flexibility of routing across brain systems, allowing confidence to serve diverse cognitive functions. Copyright © 2015 Elsevier Inc. All rights reserved.

''How To Do Things with Words'': Role of Motor Cortex in Semantic Representation of Action Words

ERIC Educational Resources Information Center

Kana, Rajesh K.; Blum, Elizabeth R.; Ladden, Stacy Levin; Ver Hoef, Lawrence W.

2012-01-01

Language, believed to have originated from actions, not only functions as a medium to access other minds, but it also helps us commit actions and enriches our social life. This fMRI study investigated the semantic and neural representations of actions and mental states. We focused mainly on language semantics (comprehending sentences with "action"…

NeuroGrid: recording action potentials from the surface of the brain.

PubMed

Khodagholy, Dion; Gelinas, Jennifer N; Thesen, Thomas; Doyle, Werner; Devinsky, Orrin; Malliaras, George G; Buzsáki, György

2015-02-01

Recording from neural networks at the resolution of action potentials is critical for understanding how information is processed in the brain. Here, we address this challenge by developing an organic material-based, ultraconformable, biocompatible and scalable neural interface array (the 'NeuroGrid') that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface. Spikes with features of interneurons and pyramidal cells were simultaneously acquired by multiple neighboring electrodes of the NeuroGrid, allowing for the isolation of putative single neurons in rats. Spiking activity demonstrated consistent phase modulation by ongoing brain oscillations and was stable in recordings exceeding 1 week's duration. We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery. The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

PubMed

Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

2018-01-01

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

Distinctive laterality of neural networks supporting action understanding in left- and right-handed individuals: An EEG coherence study.

PubMed

Kelly, Rachel; Mizelle, J C; Wheaton, Lewis A

2015-08-01

Prior work has demonstrated that perspective and handedness of observed actions can affect action understanding differently in right and left-handed persons, suggesting potential differences in the neural networks underlying action understanding between right and left-handed individuals. We sought to evaluate potential differences in these neural networks using electroencephalography (EEG). Right- and left-handed participants observed images of tool-use actions from egocentric and allocentric perspectives, with right- and left-handed actors performing the actions. Participants judged the outcome of the observed actions, and response accuracy and latency were recorded. Behaviorally, the highest accuracy and shortest latency was found in the egocentric perspective for right- and left-handed observers. Handedness of subject showed an effect on accuracy and latency also, where right-handed observers were faster to respond than left-handed observers, but on average were less accurate. Mu band (8-10 Hz) cortico-cortical coherence analysis indicated that right-handed observers have coherence in the motor dominant left parietal-premotor networks when looking at an egocentric right or allocentric left hands. When looking in an egocentric perspective at a left hand or allocentric right hand, coherence was lateralized to right parietal-premotor areas. In left-handed observers, bilateral parietal-premotor coherence patterns were observed regardless of actor handedness. These findings suggest that the cortical networks involved in understanding action outcomes are dependent on hand dominance, and notably right handed participants seem to utilize motor systems based on the limb seen performing the action. The decreased accuracy for right-handed participants on allocentric images could be due to asymmetrical lateralization of encoding action and motoric dominance, which may interfere with translating allocentric limb action outcomes. Further neurophysiological studies will determine the specific processes of how left- and right-handed participants understand actions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

PubMed

Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

2015-11-01

The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

PubMed

Zhang, Huaipin; Yue, Dong; Zhao, Wei; Hu, Songlin; Dou, Chunxia; Huaipin Zhang; Dong Yue; Wei Zhao; Songlin Hu; Chunxia Dou; Hu, Songlin; Zhang, Huaipin; Dou, Chunxia; Yue, Dong; Zhao, Wei

2018-06-01

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

Altered Connectivity and Action Model Formation in Autism Is Autism

PubMed Central

Mostofsky, Stewart H.; Ewen, Joshua B.

2014-01-01

Internal action models refer to sensory-motor programs that form the brain basis for a wide range of skilled behavior and for understanding others’ actions. Development of these action models, particularly those reliant on visual cues from the external world, depends on connectivity between distant brain regions. Studies of children with autism reveal anomalous patterns of motor learning and impaired execution of skilled motor gestures. These findings robustly correlate with measures of social and communicative function, suggesting that anomalous action model formation may contribute to impaired development of social and communicative (as well as motor) capacity in autism. Examination of the pattern of behavioral findings, as well as convergent data from neuroimaging techniques, further suggests that autism-associated action model formation may be related to abnormalities in neural connectivity, particularly decreased function of long-range connections. This line of study can lead to important advances in understanding the neural basis of autism and, more critically, can be used to guide effective therapies targeted at improving social, communicative, and motor function. PMID:21467306

Risk of punishment influences discrete and coordinated encoding of reward-guided actions by prefrontal cortex and VTA neurons

PubMed Central

Park, Junchol

2017-01-01

Actions motivated by rewards are often associated with risk of punishment. Little is known about the neural representation of punishment risk during reward-seeking behavior. We modeled this circumstance in rats by designing a task where actions were consistently rewarded but probabilistically punished. Spike activity and local field potentials were recorded during task performance simultaneously from VTA and mPFC, two reciprocally connected regions implicated in reward-seeking and aversive behaviors. At the single unit level, we found that ensembles of putative dopamine and non-dopamine VTA neurons and mPFC neurons encode the relationship between action and punishment. At the network level, we found that coherent theta oscillations synchronize VTA and mPFC in a bottom-up direction, effectively phase-modulating the neuronal spike activity in the two regions during punishment-free actions. This synchrony declined as a function of punishment probability, suggesting that during reward-seeking actions, risk of punishment diminishes VTA-driven neural synchrony between the two regions. PMID:29058673

Bio-inspired feedback-circuit implementation of discrete, free energy optimizing, winner-take-all computations.

PubMed

Genewein, Tim; Braun, Daniel A

2016-06-01

Bayesian inference and bounded rational decision-making require the accumulation of evidence or utility, respectively, to transform a prior belief or strategy into a posterior probability distribution over hypotheses or actions. Crucially, this process cannot be simply realized by independent integrators, since the different hypotheses and actions also compete with each other. In continuous time, this competitive integration process can be described by a special case of the replicator equation. Here we investigate simple analog electric circuits that implement the underlying differential equation under the constraint that we only permit a limited set of building blocks that we regard as biologically interpretable, such as capacitors, resistors, voltage-dependent conductances and voltage- or current-controlled current and voltage sources. The appeal of these circuits is that they intrinsically perform normalization without requiring an explicit divisive normalization. However, even in idealized simulations, we find that these circuits are very sensitive to internal noise as they accumulate error over time. We discuss in how far neural circuits could implement these operations that might provide a generic competitive principle underlying both perception and action.

Brain stem NOS and ROS in neural mechanisms of hypertension.

PubMed

Chan, Samuel H H; Chan, Julie Y H

2014-01-01

There is now compelling evidence to substantiate the notion that by depressing baroreflex regulation of blood pressure and augmenting central sympathetic outflow through their actions on the nucleus tractus solitarii (NTS) and rostral ventrolateral medulla (RVLM), brain stem nitric oxide synthase (NOS) and reactive oxygen species (ROS) are important contributing factors to neural mechanisms of hypertension. This review summarizes our contemporary views on the impact of NOS and ROS in the NTS and RVLM on neurogenic hypertension, and presents potential antihypertensive strategies that target brain stem NOS/ROS signaling. NO signaling in the brain stem may be pro- or antihypertensive depending on the NOS isoform that generates this gaseous moiety and the site of action. Elevation of the ROS level when its production overbalances its degradation in the NTS and RVLM underlies neurogenic hypertension. Interventional strategies with emphases on alleviating the adverse actions of these molecules on blood pressure regulation have been investigated. The pathological roles of NOS in the RVLM and NTS in neural mechanisms of hypertension are highly complex. Likewise, multiple signaling pathways underlie the deleterious roles of brain-stem ROS in neurogenic hypertension. There are recent indications that interactions between brain stem ROS and NOS may play a contributory role. Given the complicity of action mechanisms of brain-stem NOS and ROS in neural mechanisms of hypertension, additional studies are needed to identify the most crucial therapeutic target that is applicable not only in animal models but also in patients suffering from neurogenic hypertension.

An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation

PubMed Central

Chen, Chang Hao; McCullagh, Elizabeth A.; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C.

2017-01-01

The ability to record and to control action potential firing in neuronal circuits of the brain is critical to understand how the brain functions on the cellular and network levels. Recent development of optogenetic proteins allows direct stimulation or inhibition of action potential firing of neurons upon optical illumination. In this paper, we combined a low-noise and high input impedance (or low input capacitance) neural recording amplifier, and a high current laser/LED driver in a monolithic integrated circuit (IC) for simultaneous neural recording and optogenetic neural control. The low input capacitance of the amplifier (9.7 pF) was achieved through adding a dedicated unity gain input stage optimized for high impedance metal electrodes. The input referred noise of the amplifier was measured to be 4.57 µVrms, which is lower than the estimated thermal noise of the metal electrode. Thus, action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of ~6.6. The LED/laser current driver delivers a maximum current of 330 mA to generate adequate light for optogenetic control. We experimentally tested the functionality of the IC with an anesthetized Mongolian gerbil and recorded auditory stimulated action potentials from the inferior colliculus. Furthermore, we showed that spontaneous firing of 5th (trigeminal) nerve fibers was inhibited using the optogenetic protein Halorhodopsin. A noise model was also derived including the equivalent electronic components of the metal electrode and the high current driver to guide the design. PMID:28221990

4-dimensional functional profiling in the convulsant-treated larval zebrafish brain.

PubMed

Winter, Matthew J; Windell, Dylan; Metz, Jeremy; Matthews, Peter; Pinion, Joe; Brown, Jonathan T; Hetheridge, Malcolm J; Ball, Jonathan S; Owen, Stewart F; Redfern, Will S; Moger, Julian; Randall, Andrew D; Tyler, Charles R

2017-07-26

Functional neuroimaging, using genetically-encoded Ca 2+ sensors in larval zebrafish, offers a powerful combination of high spatiotemporal resolution and higher vertebrate relevance for quantitative neuropharmacological profiling. Here we use zebrafish larvae with pan-neuronal expression of GCaMP6s, combined with light sheet microscopy and a novel image processing pipeline, for the 4D profiling of chemoconvulsant action in multiple brain regions. In untreated larvae, regions associated with autonomic functionality, sensory processing and stress-responsiveness, consistently exhibited elevated spontaneous activity. The application of drugs targeting different convulsant mechanisms (4-Aminopyridine, Pentylenetetrazole, Pilocarpine and Strychnine) resulted in distinct spatiotemporal patterns of activity. These activity patterns showed some interesting parallels with what is known of the distribution of their respective molecular targets, but crucially also revealed system-wide neural circuit responses to stimulation or suppression. Drug concentration-response curves of neural activity were identified in a number of anatomically-defined zebrafish brain regions, and in vivo larval electrophysiology, also conducted in 4dpf larvae, provided additional measures of neural activity. Our quantification of network-wide chemoconvulsant drug activity in the whole zebrafish brain illustrates the power of this approach for neuropharmacological profiling in applications ranging from accelerating studies of drug safety and efficacy, to identifying pharmacologically-altered networks in zebrafish models of human neurological disorders.

Semantic domain-specific functional integration for action-related vs. abstract concepts.

PubMed

Ghio, Marta; Tettamanti, Marco

2010-03-01

A central topic in cognitive neuroscience concerns the representation of concepts and the specific neural mechanisms that mediate conceptual knowledge. Recently proposed modal theories assert that concepts are grounded on the integration of multimodal, distributed representations. The aim of the present work is to complement the available neuropsychological and neuroimaging evidence suggesting partially segregated anatomo-functional correlates for concrete vs. abstract concepts, by directly testing the semantic domain-specific patterns of functional integration between language and modal semantic brain regions. We report evidence from a functional magnetic resonance imaging study, in which healthy participants listened to sentences with either an action-related (actions involving physical entities) or an abstract (no physical entities involved) content. We measured functional integration using dynamic causal modeling, and found that the left superior temporal gyrus was more strongly connected: (1) for action-related vs. abstract sentences, with the left-hemispheric action representation system, including sensorimotor areas; (2) for abstract vs. action-related sentences, with left infero-ventral frontal, temporal, and retrosplenial cingulate areas. A selective directionality effect was observed, with causal modulatory effects exerted by perisylvian language regions on peripheral modal areas, and not vice versa. The observed condition-specific modulatory effects are consistent with embodied and situated language processing theories, and indicate that linguistic areas promote a semantic content-specific reactivation of modal simulations by top-down mechanisms. Copyright 2008 Elsevier Inc. All rights reserved.

The neural basis of the imitation drive

PubMed Central

Sugiura, Motoaki; Nozawa, Takayuki; Kotozaki, Yuka; Yomogida, Yukihito; Ihara, Mizuki; Akimoto, Yoritaka; Thyreau, Benjamin; Izumi, Shinichi; Kawashima, Ryuta

2016-01-01

Spontaneous imitation is assumed to underlie the acquisition of important skills by infants, including language and social interaction. In this study, functional magnetic resonance imaging (fMRI) was used to examine the neural basis of ‘spontaneously’ driven imitation, which has not yet been fully investigated. Healthy participants were presented with movie clips of meaningless bimanual actions and instructed to observe and imitate them during an fMRI scan. The participants were subsequently shown the movie clips again and asked to evaluate the strength of their ‘urge to imitate’ (Urge) for each action. We searched for cortical areas where the degree of activation positively correlated with Urge scores; significant positive correlations were observed in the right supplementary motor area (SMA) and bilateral midcingulate cortex (MCC) under the imitation condition. These areas were not explained by explicit reasons for imitation or the kinematic characteristics of the actions. Previous studies performed in monkeys and humans have implicated the SMA and MCC/caudal cingulate zone in voluntary actions. This study also confirmed the functional connectivity between Urge and imitation performance using a psychophysiological interaction analysis. Thus, our findings reveal the critical neural components that underlie spontaneous imitation and provide possible reasons why infants imitate spontaneously. PMID:26168793

Body posture modulates action perception.

PubMed

Zimmermann, Marius; Toni, Ivan; de Lange, Floris P

2013-04-03

Recent studies have highlighted cognitive and neural similarities between planning and perceiving actions. Given that action planning involves a simulation of potential action plans that depends on the actor's body posture, we reasoned that perceiving actions may also be influenced by one's body posture. Here, we test whether and how this influence occurs by measuring behavioral and cerebral (fMRI) responses in human participants predicting goals of observed actions, while manipulating postural congruency between their own body posture and postures of the observed agents. Behaviorally, predicting action goals is facilitated when the body posture of the observer matches the posture achieved by the observed agent at the end of his action (action's goal posture). Cerebrally, this perceptual postural congruency effect modulates activity in a portion of the left intraparietal sulcus that has previously been shown to be involved in updating neural representations of one's own limb posture during action planning. This intraparietal area showed stronger responses when the goal posture of the observed action did not match the current body posture of the observer. These results add two novel elements to the notion that perceiving actions relies on the same predictive mechanism as planning actions. First, the predictions implemented by this mechanism are based on the current physical configuration of the body. Second, during both action planning and action observation, these predictions pertain to the goal state of the action.

Attention distributed across sensory modalities enhances perceptual performance

PubMed Central

Mishra, Jyoti; Gazzaley, Adam

2012-01-01

This study investigated the interaction between top-down attentional control and multisensory processing in humans. Using semantically congruent and incongruent audiovisual stimulus streams, we found target detection to be consistently improved in the setting of distributed audiovisual attention versus focused visual attention. This performance benefit was manifested as faster reaction times for congruent audiovisual stimuli, and as accuracy improvements for incongruent stimuli, resulting in a resolution of stimulus interference. Electrophysiological recordings revealed that these behavioral enhancements were associated with reduced neural processing of both auditory and visual components of the audiovisual stimuli under distributed vs. focused visual attention. These neural changes were observed at early processing latencies, within 100–300 ms post-stimulus onset, and localized to auditory, visual, and polysensory temporal cortices. These results highlight a novel neural mechanism for top-down driven performance benefits via enhanced efficacy of sensory neural processing during distributed audiovisual attention relative to focused visual attention. PMID:22933811

Towards multifocal ultrasonic neural stimulation: pattern generation algorithms

NASA Astrophysics Data System (ADS)

Hertzberg, Yoni; Naor, Omer; Volovick, Alexander; Shoham, Shy

2010-10-01

Focused ultrasound (FUS) waves directed onto neural structures have been shown to dynamically modulate neural activity and excitability, opening up a range of possible systems and applications where the non-invasiveness, safety, mm-range resolution and other characteristics of FUS are advantageous. As in other neuro-stimulation and modulation modalities, the highly distributed and parallel nature of neural systems and neural information processing call for the development of appropriately patterned stimulation strategies which could simultaneously address multiple sites in flexible patterns. Here, we study the generation of sparse multi-focal ultrasonic distributions using phase-only modulation in ultrasonic phased arrays. We analyse the relative performance of an existing algorithm for generating multifocal ultrasonic distributions and new algorithms that we adapt from the field of optical digital holography, and find that generally the weighted Gerchberg-Saxton algorithm leads to overall superior efficiency and uniformity in the focal spots, without significantly increasing the computational burden. By combining phased-array FUS and magnetic-resonance thermometry we experimentally demonstrate the simultaneous generation of tightly focused multifocal distributions in a tissue phantom, a first step towards patterned FUS neuro-modulation systems and devices.

Evaluation of neural reflex activation as a mode of action for the acute respiratory effects of ozone.

PubMed

Prueitt, Robyn L; Goodman, Julie E

2016-09-01

Exposure to elevated levels of ozone has been associated with a variety of respiratory-related health endpoints in both epidemiology and controlled human exposure studies, including lung function decrements and airway inflammation. A mode of action (MoA) for these effects has not been established, but it has been proposed that they may occur through ozone-induced activation of neural reflexes. We critically reviewed experimental studies of ozone exposure and neural reflex activation and applied the International Programme on Chemical Safety (IPCS) mode-of-action/human relevance framework to evaluate the biological plausibility and human relevance of this proposed MoA. Based on the currently available experimental data, we found that the proposed MoA of neural reflex activation is biologically plausible for the endpoint of ozone-induced lung function decrements at high ozone exposures, but further studies are needed to fill important data gaps regarding the relevance of this MoA at lower exposures. A role for the proposed MoA in ozone-induced airway inflammation is less plausible, as the evidence is conflicting and is also of unclear relevance given the lack of studies conducted at lower exposures. The evidence suggests a different MoA for ozone-induced inflammation that may still be linked to the key events in the proposed MoA, such that neural reflex activation may have some degree of involvement in modulating ozone-induced neutrophil influx, even if it is not a direct role.

GDNF facilitates differentiation of the adult dentate gyrus-derived neural precursor cells into astrocytes via STAT3

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

Boku, Shuken, E-mail: shuboku@med.hokudai.ac.jp; Nakagawa, Shin; Takamura, Naoki

2013-05-17

Highlights: •GDNF has no effect on ADP proliferation and apoptosis. •GDNF increases ADP differentiation into astrocyte. •A specific inhibitor of STAT3 decreases the astrogliogenic effect of GDNF. •STAT3 knockdown by lentiviral shRNA vector also decreases the astrogliogenic effect of GDNF. •GDNF increases the phosphorylation of STAT3. -- Abstract: While the pro-neurogenic actions of antidepressants in the adult hippocampal dentate gyrus (DG) are thought to be one of the mechanisms through which antidepressants exert their therapeutic actions, antidepressants do not increase proliferation of neural precursor cells derived from the adult DG. Because previous studies showed that antidepressants increase the expression andmore » secretion of glial cell line-derived neurotrophic factor (GDNF) in C6 glioma cells derived from rat astrocytes and GDNF increases neurogenesis in adult DG in vivo, we investigated the effects of GDNF on the proliferation, differentiation and apoptosis of cultured neural precursor cells derived from the adult DG. Data showed that GDNF facilitated the differentiation of neural precursor cells into astrocytes but had no effect on their proliferation or apoptosis. Moreover, GDNF increased the phosphorylation of STAT3, and both a specific inhibitor of STAT3 and lentiviral shRNA for STAT3 decreased their differentiation into astrocytes. Taken together, our findings suggest that GDNF facilitates astrogliogenesis from neural precursor cells in adult DG through activating STAT3 and that this action might indirectly affect neurogenesis.« less

A cortical network model of cognitive and emotional influences in human decision making.

PubMed

Nazir, Azadeh Hassannejad; Liljenström, Hans

2015-10-01

Decision making (DM)(2) is a complex process that appears to involve several brain structures. In particular, amygdala, orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) seem to be essential in human decision making, where both emotional and cognitive aspects are taken into account. In this paper, we present a computational network model representing the neural information processing of DM, from perception to behavior. We model the population dynamics of the three neural structures (amygdala, OFC and LPFC), as well as their interaction. In our model, the neurodynamic activity of amygdala and OFC represents the neural correlates of secondary emotion, while the activity of certain neural populations in OFC alone represents the outcome expectancy of different options. The cognitive/rational aspect of DM is associated with LPFC. Our model is intended to give insights on the emotional and cognitive processes involved in DM under various internal and external contexts. Different options for actions are represented by the oscillatory activity of cell assemblies, which may change due to experience and learning. Knowledge and experience of the outcome of our decisions and actions can eventually result in changes in our neural structures, attitudes and behaviors. Simulation results may have implications for how we make decisions for our individual actions, as well as for societal choices, where we take examples from transport and its impact on CO2 emissions and climate change. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Social interaction enhances motor resonance for observed human actions.

PubMed

Hogeveen, Jeremy; Obhi, Sukhvinder S

2012-04-25

Understanding the neural basis of social behavior has become an important goal for cognitive neuroscience and a key aim is to link neural processes observed in the laboratory to more naturalistic social behaviors in real-world contexts. Although it is accepted that mirror mechanisms contribute to the occurrence of motor resonance (MR) and are common to action execution, observation, and imitation, questions remain about mirror (and MR) involvement in real social behavior and in processing nonhuman actions. To determine whether social interaction primes the MR system, groups of participants engaged or did not engage in a social interaction before observing human or robotic actions. During observation, MR was assessed via motor-evoked potentials elicited with transcranial magnetic stimulation. Compared with participants who did not engage in a prior social interaction, participants who engaged in the social interaction showed a significant increase in MR for human actions. In contrast, social interaction did not increase MR for robot actions. Thus, naturalistic social interaction and laboratory action observation tasks appear to involve common MR mechanisms, and recent experience tunes the system to particular agent types.

An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation.

PubMed

Chen, Chang Hao; McCullagh, Elizabeth A; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C

2017-03-01

The ability to record and to control action potential firing in neuronal circuits is critical to understand how the brain functions. The objective of this study is to develop a monolithic integrated circuit (IC) to record action potentials and simultaneously control action potential firing using optogenetics. A low-noise and high input impedance (or low input capacitance) neural recording amplifier is combined with a high current laser/light-emitting diode (LED) driver in a single IC. The low input capacitance of the amplifier (9.7 pF) was achieved by adding a dedicated unity gain stage optimized for high impedance metal electrodes. The input referred noise of the amplifier is [Formula: see text], which is lower than the estimated thermal noise of the metal electrode. Thus, the action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of at least 6.6. The LED/laser current driver delivers a maximum current of 330 mA, which is adequate for optogenetic control. The functionality of the IC was tested with an anesthetized Mongolian gerbil and auditory stimulated action potentials were recorded from the inferior colliculus. Spontaneous firings of fifth (trigeminal) nerve fibers were also inhibited using the optogenetic protein Halorhodopsin. Moreover, a noise model of the system was derived to guide the design. A single IC to measure and control action potentials using optogenetic proteins is realized so that more complicated behavioral neuroscience research and the translational neural disorder treatments become possible in the future.

Frank Beach Award Winner: Steroids as Neuromodulators of Brain Circuits and Behavior

PubMed Central

Remage-Healey, Luke

2014-01-01

Neurons communicate primarily via action potentials that transmit information on the timescale of milliseconds. Neurons also integrate information via alterations in gene transcription and protein translation that are sustained for hours to days after initiation. Positioned between these two signaling timescales are the minute-by-minute actions of neuromodulators. Over the course of minutes, the classical neuromodulators (such as serotonin, dopamine, octopamine, and norepinephrine) can alter and/or stabilize neural circuit patterning as well as behavioral states. Neuromodulators allow many flexible outputs from neural circuits and can encode information content into the firing state of neural networks. The idea that steroid molecules can operate as genuine behavioral neuromodulators - synthesized by and acting within brain circuits on a minute-by-minute timescale - has gained traction in recent years. Evidence for brain steroid synthesis at synaptic terminals has converged with evidence for the rapid actions of brain-derived steroids on neural circuits and behavior. The general principle emerging from this work is that the production of steroid hormones within brain circuits can alter their functional connectivity and shift sensory representations by enhancing their information coding. Steroids produced in the brain can therefore change the information content of neuronal networks to rapidly modulate sensory experience and sensorimotor functions. PMID:25110187

Optimization Methods for Spiking Neurons and Networks

PubMed Central

Russell, Alexander; Orchard, Garrick; Dong, Yi; Mihalaş, Ştefan; Niebur, Ernst; Tapson, Jonathan; Etienne-Cummings, Ralph

2011-01-01

Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual manipulation of parameters is both time consuming and non-intuitive due to the nonlinear relationship between parameters and the neuron’s output. The complexity rises even further as the neurons are networked and the systems often become mathematically intractable. In large circuits, the desired behavior and timing of action potential trains may be known but the timing of the individual action potentials is unknown and unimportant, whereas in single neuron systems the timing of individual action potentials is critical. In this paper, we automate the process of finding parameters. To configure a single neuron we derive a maximum likelihood method for configuring a neuron model, specifically the Mihalas–Niebur Neuron. Similarly, to configure neural circuits, we show how we use genetic algorithms (GAs) to configure parameters for a network of simple integrate and fire with adaptation neurons. The GA approach is demonstrated both in software simulation and hardware implementation on a reconfigurable custom very large scale integration chip. PMID:20959265

Neuroanatomical distribution of five semantic components of verbs: evidence from fMRI.

PubMed

Kemmerer, David; Castillo, Javier Gonzalez; Talavage, Thomas; Patterson, Stephanie; Wiley, Cynthia

2008-10-01

The Simulation Framework, also known as the Embodied Cognition Framework, maintains that conceptual knowledge is grounded in sensorimotor systems. To test several predictions that this theory makes about the neural substrates of verb meanings, we used functional magnetic resonance imaging (fMRI) to scan subjects' brains while they made semantic judgments involving five classes of verbs-specifically, Running verbs (e.g., run, jog, walk), Speaking verbs (e.g., shout, mumble, whisper), Hitting verbs (e.g., hit, poke, jab), Cutting verbs (e.g., cut, slice, hack), and Change of State verbs (e.g., shatter, smash, crack). These classes were selected because they vary with respect to the presence or absence of five distinct semantic components-specifically, ACTION, MOTION, CONTACT, CHANGE OF STATE, and TOOL USE. Based on the Simulation Framework, we hypothesized that the ACTION component depends on the primary motor and premotor cortices, that the MOTION component depends on the posterolateral temporal cortex, that the CONTACT component depends on the intraparietal sulcus and inferior parietal lobule, that the CHANGE OF STATE component depends on the ventral temporal cortex, and that the TOOL USE component depends on a distributed network of temporal, parietal, and frontal regions. Virtually all of the predictions were confirmed. Taken together, these findings support the Simulation Framework and extend our understanding of the neuroanatomical distribution of different aspects of verb meaning.

Decoding natural reach-and-grasp actions from human EEG

NASA Astrophysics Data System (ADS)

Schwarz, Andreas; Ofner, Patrick; Pereira, Joana; Ioana Sburlea, Andreea; Müller-Putz, Gernot R.

2018-02-01

Objective. Despite the high number of degrees of freedom of the human hand, most actions of daily life can be executed incorporating only palmar, pincer and lateral grasp. In this study we attempt to discriminate these three different executed reach-and-grasp actions utilizing their EEG neural correlates. Approach. In a cue-guided experiment, 15 healthy individuals were asked to perform these actions using daily life objects. We recorded 72 trials for each reach-and-grasp condition and from a no-movement condition. Main results. Using low-frequency time domain features from 0.3 to 3 Hz, we achieved binary classification accuracies of 72.4%, STD  ±  5.8% between grasp types, for grasps versus no-movement condition peak performances of 93.5%, STD  ±  4.6% could be reached. In an offline multiclass classification scenario which incorporated not only all reach-and-grasp actions but also the no-movement condition, the highest performance could be reached using a window of 1000 ms for feature extraction. Classification performance peaked at 65.9%, STD  ±  8.1%. Underlying neural correlates of the reach-and-grasp actions, investigated over the primary motor cortex, showed significant differences starting from approximately 800 ms to 1200 ms after the movement onset which is also the same time frame where classification performance reached its maximum. Significance. We could show that it is possible to discriminate three executed reach-and-grasp actions prominent in people’s everyday use from non-invasive EEG. Underlying neural correlates showed significant differences between all tested conditions. These findings will eventually contribute to our attempt of controlling a neuroprosthesis in a natural and intuitive way, which could ultimately benefit motor impaired end users in their daily life actions.

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.

Dissociative States and Neural Complexity

ERIC Educational Resources Information Center

Bob, Petr; Svetlak, Miroslav

2011-01-01

Recent findings indicate that neural mechanisms of consciousness are related to integration of distributed neural assemblies. This neural integration is particularly vulnerable to past stressful experiences that can lead to disintegration and dissociation of consciousness. These findings suggest that dissociation could be described as a level of…

Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.

PubMed

Williams, Alex H; Kim, Tony Hyun; Wang, Forea; Vyas, Saurabh; Ryu, Stephen I; Shenoy, Krishna V; Schnitzer, Mark; Kolda, Tamara G; Ganguli, Surya

2018-06-27

Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning. Copyright © 2018 Elsevier Inc. All rights reserved.

A neural model of hierarchical reinforcement learning.

PubMed

Rasmussen, Daniel; Voelker, Aaron; Eliasmith, Chris

2017-01-01

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain's general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model's behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions.

The neural basis of the bystander effect--the influence of group size on neural activity when witnessing an emergency.

PubMed

Hortensius, Ruud; de Gelder, Beatrice

2014-06-01

Naturalistic observation and experimental studies in humans and other primates show that observing an individual in need automatically triggers helping behavior. The aim of the present study is to clarify the neurofunctional basis of social influences on individual helping behavior. We investigate whether when participants witness an emergency, while performing an unrelated color-naming task in an fMRI scanner, the number of bystanders present at the emergency influences neural activity in regions related to action preparation. The results show a decrease in activity with the increase in group size in the left pre- and postcentral gyri and left medial frontal gyrus. In contrast, regions related to visual perception and attention show an increase in activity. These results demonstrate the neural mechanisms of social influence on automatic action preparation that is at the core of helping behavior when witnessing an emergency. Copyright © 2014 Elsevier Inc. All rights reserved.

Infants' Grip Strength Predicts Mu Rhythm Attenuation during Observation of Lifting Actions with Weighted Blocks

ERIC Educational Resources Information Center

Upshaw, Michaela B.; Bernier, Raphael A.; Sommerville, Jessica A.

2016-01-01

Research has established that the body is fundamentally involved in perception: bodily experience influences activation of the shared neural system underlying action perception and production during action observation, and bodily characteristics influence perception of the spatial environment. However, whether bodily characteristics influence…

Analysis of electrically evoked compound action potential of the auditory nerve in children with bilateral cochlear implants.

PubMed

Caldas, Fernanda Ferreira; Cardoso, Carolina Costa; Barreto, Monique Antunes de Souza Chelminski; Teixeira, Marina Santos; Hilgenberg, Anacléia Melo da Silva; Serra, Lucieny Silva Martins; Bahmad Junior, Fayez

2016-01-01

The cochlear implant device has the capacity to measure the electrically evoked compound action potential of the auditory nerve. The neural response telemetry is used in order to measure the electrically evoked compound action potential of the auditory nerve. To analyze the electrically evoked compound action potential, through the neural response telemetry, in children with bilateral cochlear implants. This is an analytical, prospective, longitudinal, historical cohort study. Six children, aged 1-4 years, with bilateral cochlear implant were assessed at five different intervals during their first year of cochlear implant use. There were significant differences in follow-up time (p=0.0082) and electrode position (p=0.0019) in the T-NRT measure. There was a significant difference in the interaction between time of follow-up and electrode position (p=0.0143) when measuring the N1-P1 wave amplitude between the three electrodes at each time of follow-up. The electrically evoked compound action potential measurement using neural response telemetry in children with bilateral cochlear implants during the first year of follow-up was effective in demonstrating the synchronized bilateral development of the peripheral auditory pathways in the studied population. Copyright © 2015 Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial. Published by Elsevier Editora Ltda. All rights reserved.

Towards a magnetoresistive platform for neural signal recording

NASA Astrophysics Data System (ADS)

Sharma, P. P.; Gervasoni, G.; Albisetti, E.; D'Ercoli, F.; Monticelli, M.; Moretti, D.; Forte, N.; Rocchi, A.; Ferrari, G.; Baldelli, P.; Sampietro, M.; Benfenati, F.; Bertacco, R.; Petti, D.

2017-05-01

A promising strategy to get deeper insight on brain functionalities relies on the investigation of neural activities at the cellular and sub-cellular level. In this framework, methods for recording neuron electrical activity have gained interest over the years. Main technological challenges are associated to finding highly sensitive detection schemes, providing considerable spatial and temporal resolution. Moreover, the possibility to perform non-invasive assays would constitute a noteworthy benefit. In this work, we present a magnetoresistive platform for the detection of the action potential propagation in neural cells. Such platform allows, in perspective, the in vitro recording of neural signals arising from single neurons, neural networks and brain slices.

Infrared neural stimulation (INS) inhibits electrically evoked neural responses in the deaf white cat

NASA Astrophysics Data System (ADS)

Richter, Claus-Peter; Rajguru, Suhrud M.; Robinson, Alan; Young, Hunter K.

2014-03-01

Infrared neural stimulation (INS) has been used in the past to evoke neural activity from hearing and partially deaf animals. All the responses were excitatory. In Aplysia californica, Duke and coworkers demonstrated that INS also inhibits neural responses [1], which similar observations were made in the vestibular system [2, 3]. In deaf white cats that have cochleae with largely reduced spiral ganglion neuron counts and a significant degeneration of the organ of Corti, no cochlear compound action potentials could be observed during INS alone. However, the combined electrical and optical stimulation demonstrated inhibitory responses during irradiation with infrared light.

A Fast Variational Approach for Learning Markov Random Field Language Models

DTIC Science & Technology

2015-01-01

the same distribution as n- gram models, but utilize a non-linear neural network pa- rameterization. NLMs have been shown to produce com- petitive...to either resort to local optimiza- tion methods, such as those used in neural lan- guage models, or work with heavily constrained distributions. In...embeddings learned through neural language models. Central to the language modelling problem is the challenge Proceedings of the 32nd International

The inferior parietal lobule: where action becomes perception.

PubMed

Rizzolatti, Giacomo; Ferrari, Pier Francesco; Rozzi, Stefano; Fogassi, Leonardo

2006-01-01

The view defended in this article is that action and perception share the same neural substrate. To substantiate this view, the anatomical and functional organization of the inferior parietal lobule (IPL) is reviewed. In particular, it will be shown that many IPL neurons discharge selectively when the monkey executes a given motor act (e.g. grasping). Most interestingly, most of them fire only if the coded motor act is followed by a subsequent specific motor act (e.g. placing). Some of these action-constrained motor neurons have mirror properties and selectively discharge during the observation of motor acts when these are embedded in a given action (e.g. grasping for eating, but not grasping for placing). Thus, the activation of these IPL neurons allows the observer not only to recognize the observed motor act, but also to predict what will be the next motor act of the action, that is to understand the intentions of the action's agent. The finding that the same neurons that are active during the execution of specific motor acts also mediate the understanding of the 'what' and the 'why' of others' actions provides strong evidence for a common neural substrate for action and perception.

The neural correlates of ‘vitality form’ recognition: an fMRI study

PubMed Central

Di Cesare, Giuseppe; Di Dio, Cinzia; Rochat, Magali J.; Sinigaglia, Corrado; Bruschweiler-Stern, Nadia; Stern, Daniel N.

2014-01-01

The observation of goal-directed actions performed by another individual allows one to understand what that individual is doing and why he/she is doing it. Important information about others’ behaviour is also carried out by the dynamics of the observed action. Action dynamics characterize the ‘vitality form’ of an action describing the cognitive and affective relation between the performing agent and the action recipient. Here, using the fMRI technique, we assessed the neural correlates of vitality form recognition presenting participants with videos showing two actors executing actions with different vitality forms: energetic and gentle. The participants viewed the actions in two tasks. In one task (what), they had to focus on the goal of the presented action; in the other task (how), they had to focus on the vitality form. For both tasks, activations were found in the action observation/execution circuit. Most interestingly, the contrast how vs what revealed activation in right dorso-central insula, highlighting the involvement, in the recognition of vitality form, of an anatomical region connecting somatosensory areas with the medial temporal region and, in particular, with the hippocampus. This somatosensory-insular-limbic circuit could underlie the observers’ capacity to understand the vitality forms conveyed by the observed action. PMID:23740868

Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates

EPA Science Inventory

We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentiall...

Dynamic Sensor Tasking for Space Situational Awareness via Reinforcement Learning

NASA Astrophysics Data System (ADS)

Linares, R.; Furfaro, R.

2016-09-01

This paper studies the Sensor Management (SM) problem for optical Space Object (SO) tracking. The tasking problem is formulated as a Markov Decision Process (MDP) and solved using Reinforcement Learning (RL). The RL problem is solved using the actor-critic policy gradient approach. The actor provides a policy which is random over actions and given by a parametric probability density function (pdf). The critic evaluates the policy by calculating the estimated total reward or the value function for the problem. The parameters of the policy action pdf are optimized using gradients with respect to the reward function. Both the critic and the actor are modeled using deep neural networks (multi-layer neural networks). The policy neural network takes the current state as input and outputs probabilities for each possible action. This policy is random, and can be evaluated by sampling random actions using the probabilities determined by the policy neural network's outputs. The critic approximates the total reward using a neural network. The estimated total reward is used to approximate the gradient of the policy network with respect to the network parameters. This approach is used to find the non-myopic optimal policy for tasking optical sensors to estimate SO orbits. The reward function is based on reducing the uncertainty for the overall catalog to below a user specified uncertainty threshold. This work uses a 30 km total position error for the uncertainty threshold. This work provides the RL method with a negative reward as long as any SO has a total position error above the uncertainty threshold. This penalizes policies that take longer to achieve the desired accuracy. A positive reward is provided when all SOs are below the catalog uncertainty threshold. An optimal policy is sought that takes actions to achieve the desired catalog uncertainty in minimum time. This work trains the policy in simulation by letting it task a single sensor to "learn" from its performance. The proposed approach for the SM problem is tested in simulation and good performance is found using the actor-critic policy gradient method.

Goal-Directed Decision Making with Spiking Neurons.

PubMed

Friedrich, Johannes; Lengyel, Máté

2016-02-03

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. Copyright © 2016 the authors 0270-6474/16/361529-18$15.00/0.

Goal-Directed Decision Making with Spiking Neurons

PubMed Central

Lengyel, Máté

2016-01-01

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. PMID:26843636

Cognitive control in children with learning disabilities: neuromarker for deficient executive functions.

PubMed

Alahmadi, Nsreen A

2017-08-02

The neural underpinnings of learning disabilities (LD) are still not known. Recent discussions focus over whether domain-specific and/or domain-unspecific reasons might be responsible for LD either alone or in combination with each other. This study applied standard nonverbal Go-NoGo tasks (visual continuous performance test) to LD and healthy control children to examine whether they show deficient executive functions. During this Go-NoGo task, electroencephalogram was measured in addition to reaction times, hits, omissions, and commissions to the Go and NoGo stimuli. It was shown that children with LD reacted slower with variable responses to Go stimuli and made more omission errors in comparison with the healthy control children. The analysis of the event-related potential indicated that the deficient behavior in this task is associated with smaller - and in part nonexistent - P3d amplitudes. This neural activation indicates a different neural activation pattern during action inhibition in LD children. The neural networks involved in controlling action inhibition are mostly located in frontal brain areas, for which it has been shown that children with LD show neural activation deficiencies. This is possibly a consequence of a maturational delay of the frontal cortex.

Rapid whole brain imaging of neural activity in freely behaving larval zebrafish (Danio rerio)

PubMed Central

Shang, Chunfeng; Yang, Wenbin; Bai, Lu; Du, Jiulin

2017-01-01

The internal brain dynamics that link sensation and action are arguably better studied during natural animal behaviors. Here, we report on a novel volume imaging and 3D tracking technique that monitors whole brain neural activity in freely swimming larval zebrafish (Danio rerio). We demonstrated the capability of our system through functional imaging of neural activity during visually evoked and prey capture behaviors in larval zebrafish. PMID:28930070

[Neurodynamic Bases of Imitation Learning and Episodic Memory].

PubMed

Tsukerman, V D

2016-01-01

In this review, three essentially important processes in development of cognitive behavior are considered: knowledge of a spatial environment by means of physical activity, coding and a call of an existential context of episodic memory and imitation learning based on the mirror neural mechanism. The data show that the parietal and frontal system of learning by imitation, allows the developing organism to seize skills of management and motive synergies in perisomatic space, to understand intentions and the purposes of observed actions of other individuals. At the same time a widely distributed parietal and frontal and entorhinal-hippocampal system mediates spatial knowledge and the solution of the navigation tasks important for creation of an existential context of episodic memory.

Understanding emotion with brain networks.

PubMed

Pessoa, Luiz

2018-02-01

Emotional processing appears to be interlocked with perception, cognition, motivation, and action. These interactions are supported by the brain's large-scale non-modular anatomical and functional architectures. An important component of this organization involves characterizing the brain in terms of networks. Two aspects of brain networks are discussed: brain networks should be considered as inherently overlapping (not disjoint) and dynamic (not static). Recent work on multivariate pattern analysis shows that affective dimensions can be detected in the activity of distributed neural systems that span cortical and subcortical regions. More broadly, the paper considers how we should think of causation in complex systems like the brain, so as to inform the relationship between emotion and other mental aspects, such as cognition.

Modelling the control of interceptive actions.

PubMed Central

Beek, P J; Dessing, J C; Peper, C E; Bullock, D

2003-01-01

In recent years, several phenomenological dynamical models have been formulated that describe how perceptual variables are incorporated in the control of motor variables. We call these short-route models as they do not address how perception-action patterns might be constrained by the dynamical properties of the sensory, neural and musculoskeletal subsystems of the human action system. As an alternative, we advocate a long-route modelling approach in which the dynamics of these subsystems are explicitly addressed and integrated to reproduce interceptive actions. The approach is exemplified through a discussion of a recently developed model for interceptive actions consisting of a neural network architecture for the online generation of motor outflow commands, based on time-to-contact information and information about the relative positions and velocities of hand and ball. This network is shown to be consistent with both behavioural and neurophysiological data. Finally, some problems are discussed with regard to the question of how the motor outflow commands (i.e. the intended movement) might be modulated in view of the musculoskeletal dynamics. PMID:14561342

Distinct Sources of Deterministic and Stochastic Components of Action Timing Decisions in Rodent Frontal Cortex.

PubMed

Murakami, Masayoshi; Shteingart, Hanan; Loewenstein, Yonatan; Mainen, Zachary F

2017-05-17

The selection and timing of actions are subject to determinate influences such as sensory cues and internal state as well as to effectively stochastic variability. Although stochastic choice mechanisms are assumed by many theoretical models, their origin and mechanisms remain poorly understood. Here we investigated this issue by studying how neural circuits in the frontal cortex determine action timing in rats performing a waiting task. Electrophysiological recordings from two regions necessary for this behavior, medial prefrontal cortex (mPFC) and secondary motor cortex (M2), revealed an unexpected functional dissociation. Both areas encoded deterministic biases in action timing, but only M2 neurons reflected stochastic trial-by-trial fluctuations. This differential coding was reflected in distinct timescales of neural dynamics in the two frontal cortical areas. These results suggest a two-stage model in which stochastic components of action timing decisions are injected by circuits downstream of those carrying deterministic bias signals. Copyright © 2017 Elsevier Inc. All rights reserved.

A review on cluster estimation methods and their application to neural spike data.

PubMed

Zhang, James; Nguyen, Thanh; Cogill, Steven; Bhatti, Asim; Luo, Lingkun; Yang, Samuel; Nahavandi, Saeid

2018-06-01

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons-'spike sorting'-is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.

A review on cluster estimation methods and their application to neural spike data

NASA Astrophysics Data System (ADS)

Zhang, James; Nguyen, Thanh; Cogill, Steven; Bhatti, Asim; Luo, Lingkun; Yang, Samuel; Nahavandi, Saeid

2018-06-01

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons—‘spike sorting’—is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.

Conscious Action/Zombie Action

PubMed Central

Shepherd, Joshua

2015-01-01

Abstract I argue that the neural realizers of experiences of trying (that is, experiences of directing effort towards the satisfaction of an intention) are not distinct from the neural realizers of actual trying (that is, actual effort directed towards the satisfaction of an intention). I then ask how experiences of trying might relate to the perceptual experiences one has while acting. First, I assess recent zombie action arguments regarding conscious visual experience, and I argue that contrary to what some have claimed, conscious visual experience plays a causal role for action control in some circumstances. Second, I propose a multimodal account of the experience of acting. According to this account, the experience of acting is (at the very least) a temporally extended, co‐conscious collection of agentive and perceptual experiences, functionally integrated and structured both by multimodal perceptual processing as well as by what an agent is, at the time, trying to do. PMID:27667859

Volition and eye movements.

PubMed

Nachev, Parashkev; Husain, Masud; Kennard, Christopher

2008-01-01

Although the conceptual distinction between voluntary and automatic acts seems intuitively obvious, its neural basis remains opaque. Assigning volition--or some paraphrase such as action selection--to discrete parts of the brain arguably tells us nothing about what volition actually is in neural terms. Equally, exploring the relative sensitivity of discrete brain areas to manipulations of action choice, including its asymptote--free choice--would only be informative if voluntary processes could thereby be reliably isolated. Unfortunately, such manipulations are subject to ineliminable confounds, such as the complexity of the underlying condition-action associations. Here we propose an adaptation of a classic oculomotor task--saccadic choice with asynchronous targets--where the processes engaged in free choice manifest as interference in the performance of an automatic task, thereby circumventing the difficulties in parameterising volition. We suggest that this task may be useful in probing deficits in voluntary action in pathological states.

Space-Time Neural Networks

NASA Technical Reports Server (NTRS)

Villarreal, James A.; Shelton, Robert O.

1992-01-01

Concept of space-time neural network affords distributed temporal memory enabling such network to model complicated dynamical systems mathematically and to recognize temporally varying spatial patterns. Digital filters replace synaptic-connection weights of conventional back-error-propagation neural network.

Neural Mechanisms Underlying Action Observation in Adults with Down Syndrome

ERIC Educational Resources Information Center

Virji-Babul, Naznin; Moiseev, Alexander; Cheung, Teresa; Weeks, Daniel J.; Cheyne, Douglas; Ribary, Urs

2010-01-01

Results of a magnetoencephalography (MEG) brain imaging study conducted to examine the cortical responses during action execution and action observation in 10 healthy adults and 8 age-matched adults with Down syndrome are reported. During execution, the motor responses were strongly lateralized on the ipsilateral rather than the contralateral side…

A virtual reality-based system integrated with fmri to study neural mechanisms of action observation-execution: A proof of concept study

PubMed Central

Adamovich, S.V.; August, K.; Merians, A.; Tunik, E.

2017-01-01

Purpose Emerging evidence shows that interactive virtual environments (VEs) may be a promising tool for studying sensorimotor processes and for rehabilitation. However, the potential of VEs to recruit action observation-execution neural networks is largely unknown. For the first time, a functional MRI-compatible virtual reality system (VR) has been developed to provide a window into studying brain-behavior interactions. This system is capable of measuring the complex span of hand-finger movements and simultaneously streaming this kinematic data to control the motion of representations of human hands in virtual reality. Methods In a blocked fMRI design, thirteen healthy subjects observed, with the intent to imitate (OTI), finger sequences performed by the virtual hand avatar seen in 1st person perspective and animated by pre-recorded kinematic data. Following this, subjects imitated the observed sequence while viewing the virtual hand avatar animated by their own movement in real-time. These blocks were interleaved with rest periods during which subjects viewed static virtual hand avatars and control trials in which the avatars were replaced with moving non-anthropomorphic objects. Results We show three main findings. First, both observation with intent to imitate and imitation with real-time virtual avatar feedback, were associated with activation in a distributed frontoparietal network typically recruited for observation and execution of real-world actions. Second, we noted a time-variant increase in activation in the left insular cortex for observation with intent to imitate actions performed by the virtual avatar. Third, imitation with virtual avatar feedback (relative to the control condition) was associated with a localized recruitment of the angular gyrus, precuneus, and extrastriate body area, regions which are (along with insular cortex) associated with the sense of agency. Conclusions Our data suggest that the virtual hand avatars may have served as disembodied training tools in the observation condition and as embodied “extensions” of the subject’s own body (pseudo-tools) in the imitation. These data advance our understanding of the brain-behavior interactions when performing actions in VE and have implications in the development of observation- and imitation-based VR rehabilitation paradigms. PMID:19531876

Neural Network Development in Late Adolescents during Observation of Risk-Taking Action

PubMed Central

Higuchi, Shigekazu; Hida, Akiko; Enomoto, Minori; Umezawa, Jun; Mishima, Kazuo

2012-01-01

Emotional maturity and social awareness are important for adolescents, particularly college students beginning to face the challenges and risks of the adult world. However, there has been relatively little research into personality maturation and psychological development during late adolescence and the neural changes underlying this development. We investigated the correlation between psychological properties (neuroticism, extraversion, anxiety, and depression) and age among late adolescents (n = 25, from 18 years and 1 month to 22 years and 8 months). The results revealed that late adolescents became less neurotic, less anxious, less depressive and more extraverted as they aged. Participants then observed video clips depicting hand movements with and without a risk of harm (risk-taking or safe actions) during functional magnetic resonance imaging (fMRI). The results revealed that risk-taking actions elicited significantly stronger activation in the bilateral inferior parietal lobule, temporal visual regions (superior/middle temporal areas), and parieto-occipital visual areas (cuneus, middle occipital gyri, precuneus). We found positive correlations of age and extraversion with neural activation in the insula, middle temporal gyrus, lingual gyrus, and precuneus. We also found a negative correlation of age and anxiety with activation in the angular gyrus, precentral gyrus, and red nucleus/substantia nigra. Moreover, we found that insula activation mediated the relationship between age and extraversion. Overall, our results indicate that late adolescents become less anxious and more extraverted with age, a process involving functional neural changes in brain networks related to social cognition and emotional processing. The possible neural mechanisms of psychological and social maturation during late adolescence are discussed. PMID:22768085

The science of neural interface systems.

PubMed

Hatsopoulos, Nicholas G; Donoghue, John P

2009-01-01

The ultimate goal of neural interface research is to create links between the nervous system and the outside world either by stimulating or by recording from neural tissue to treat or assist people with sensory, motor, or other disabilities of neural function. Although electrical stimulation systems have already reached widespread clinical application, neural interfaces that record neural signals to decipher movement intentions are only now beginning to develop into clinically viable systems to help paralyzed people. We begin by reviewing state-of-the-art research and early-stage clinical recording systems and focus on systems that record single-unit action potentials. We then address the potential for neural interface research to enhance basic scientific understanding of brain function by offering unique insights in neural coding and representation, plasticity, brain-behavior relations, and the neurobiology of disease. Finally, we discuss technical and scientific challenges faced by these systems before they are widely adopted by severely motor-disabled patients.

The neural basis of the imitation drive.

PubMed

Hanawa, Sugiko; Sugiura, Motoaki; Nozawa, Takayuki; Kotozaki, Yuka; Yomogida, Yukihito; Ihara, Mizuki; Akimoto, Yoritaka; Thyreau, Benjamin; Izumi, Shinichi; Kawashima, Ryuta

2016-01-01

Spontaneous imitation is assumed to underlie the acquisition of important skills by infants, including language and social interaction. In this study, functional magnetic resonance imaging (fMRI) was used to examine the neural basis of 'spontaneously' driven imitation, which has not yet been fully investigated. Healthy participants were presented with movie clips of meaningless bimanual actions and instructed to observe and imitate them during an fMRI scan. The participants were subsequently shown the movie clips again and asked to evaluate the strength of their 'urge to imitate' (Urge) for each action. We searched for cortical areas where the degree of activation positively correlated with Urge scores; significant positive correlations were observed in the right supplementary motor area (SMA) and bilateral midcingulate cortex (MCC) under the imitation condition. These areas were not explained by explicit reasons for imitation or the kinematic characteristics of the actions. Previous studies performed in monkeys and humans have implicated the SMA and MCC/caudal cingulate zone in voluntary actions. This study also confirmed the functional connectivity between Urge and imitation performance using a psychophysiological interaction analysis. Thus, our findings reveal the critical neural components that underlie spontaneous imitation and provide possible reasons why infants imitate spontaneously. © The Author (2015). Published by Oxford University Press.

Multiview fusion for activity recognition using deep neural networks

NASA Astrophysics Data System (ADS)

Kavi, Rahul; Kulathumani, Vinod; Rohit, Fnu; Kecojevic, Vlad

2016-07-01

Convolutional neural networks (ConvNets) coupled with long short term memory (LSTM) networks have been recently shown to be effective for video classification as they combine the automatic feature extraction capabilities of a neural network with additional memory in the temporal domain. This paper shows how multiview fusion can be applied to such a ConvNet LSTM architecture. Two different fusion techniques are presented. The system is first evaluated in the context of a driver activity recognition system using data collected in a multicamera driving simulator. These results show significant improvement in accuracy with multiview fusion and also show that deep learning performs better than a traditional approach using spatiotemporal features even without requiring any background subtraction. The system is also validated on another publicly available multiview action recognition dataset that has 12 action classes and 8 camera views.

Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays.

PubMed

Wang, Leimin; Zeng, Zhigang; Ge, Ming-Feng; Hu, Junhao

2018-05-02

This paper deals with the stabilization problem of memristive recurrent neural networks with inertial items, discrete delays, bounded and unbounded distributed delays. First, for inertial memristive recurrent neural networks (IMRNNs) with second-order derivatives of states, an appropriate variable substitution method is invoked to transfer IMRNNs into a first-order differential form. Then, based on nonsmooth analysis theory, several algebraic criteria are established for the global stabilizability of IMRNNs under proposed feedback control, where the cases with both bounded and unbounded distributed delays are successfully addressed. Finally, the theoretical results are illustrated via the numerical simulations. Copyright © 2018 Elsevier Ltd. All rights reserved.

The basolateral amygdala in reward learning and addiction.

PubMed

Wassum, Kate M; Izquierdo, Alicia

2015-10-01

Sophisticated behavioral paradigms partnered with the emergence of increasingly selective techniques to target the basolateral amygdala (BLA) have resulted in an enhanced understanding of the role of this nucleus in learning and using reward information. Due to the wide variety of behavioral approaches many questions remain on the circumscribed role of BLA in appetitive behavior. In this review, we integrate conclusions of BLA function in reward-related behavior using traditional interference techniques (lesion, pharmacological inactivation) with those using newer methodological approaches in experimental animals that allow in vivo manipulation of cell type-specific populations and neural recordings. Secondly, from a review of appetitive behavioral tasks in rodents and monkeys and recent computational models of reward procurement, we derive evidence for BLA as a neural integrator of reward value, history, and cost parameters. Taken together, BLA codes specific and temporally dynamic outcome representations in a distributed network to orchestrate adaptive responses. We provide evidence that experiences with opiates and psychostimulants alter these outcome representations in BLA, resulting in long-term modified action. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cognitive Activation by Central Thalamic Stimulation: The Yerkes-Dodson Law Revisited.

PubMed Central

Mair, Robert G.; Onos, Kristen D.; Hembrook, Jacqueline R.

2011-01-01

Central thalamus regulates forebrain arousal, influencing activity in distributed neural networks that give rise to organized actions during alert, wakeful states. Central thalamus has been implicated in working memory by the effects of lesions and microinjected drugs in this part of the brain. Lesions and drugs that inhibit neural activity have been found to impair working memory. Drugs that increase activity have been found to enhance and impair memory depending on the dose tested. Electrical deep brain stimulation (DBS) similarly enhances working memory at low stimulating currents and impairs it at higher currents. These effects are time dependent. They were observed when DBS was applied during the memory delay (retention) or choice response (retrieval) but not earlier in trials during the sample (acquisition) phase. The effects of microinjected drugs and DBS are consistent with the Yerkes-Dodson law, which describes an inverted-U relationship between arousal and behavioral performance. Alternatively these results may reflect desensitization associated with higher levels of stimulation, spread of drugs or current to adjacent structures, or activation of less sensitive neurons or receptors at higher DBS currents or drug doses. PMID:22013395

Robustness of a distributed neural network controller for locomotion in a hexapod robot

NASA Technical Reports Server (NTRS)

Chiel, Hillel J.; Beer, Randall D.; Quinn, Roger D.; Espenschied, Kenneth S.

1992-01-01

A distributed neural-network controller for locomotion, based on insect neurobiology, has been used to control a hexapod robot. How robust is this controller? Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.

New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays.

PubMed

Zhang, Guodong; Zeng, Zhigang; Hu, Junhao

2018-01-01

This paper is concerned with the global exponential dissipativity of memristive inertial neural networks with discrete and distributed time-varying delays. By constructing appropriate Lyapunov-Krasovskii functionals, some new sufficient conditions ensuring global exponential dissipativity of memristive inertial neural networks are derived. Moreover, the globally exponential attractive sets and positive invariant sets are also presented here. In addition, the new proposed results here complement and extend the earlier publications on conventional or memristive neural network dynamical systems. Finally, numerical simulations are given to illustrate the effectiveness of obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Exponential Stability of Almost Periodic Solutions for Memristor-Based Neural Networks with Distributed Leakage Delays.

PubMed

Xu, Changjin; Li, Peiluan; Pang, Yicheng

2016-12-01

In this letter, we deal with a class of memristor-based neural networks with distributed leakage delays. By applying a new Lyapunov function method, we obtain some sufficient conditions that ensure the existence, uniqueness, and global exponential stability of almost periodic solutions of neural networks. We apply the results of this solution to prove the existence and stability of periodic solutions for this delayed neural network with periodic coefficients. We then provide an example to illustrate the effectiveness of the theoretical results. Our results are completely new and complement the previous studies Chen, Zeng, and Jiang ( 2014 ) and Jiang, Zeng, and Chen ( 2015 ).

Stimulus-dependent Maximum Entropy Models of Neural Population Codes

PubMed Central

Segev, Ronen; Schneidman, Elad

2013-01-01

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME) model—a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population. PMID:23516339

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

NASA Technical Reports Server (NTRS)

Ross, Muriel D.

1991-01-01

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Hierarchically organized behavior and its neural foundations: A reinforcement-learning perspective

PubMed Central

Botvinick, Matthew M.; Niv, Yael; Barto, Andrew C.

2009-01-01

Research on human and animal behavior has long emphasized its hierarchical structure — the divisibility of ongoing behavior into discrete tasks, which are comprised of subtask sequences, which in turn are built of simple actions. The hierarchical structure of behavior has also been of enduring interest within neuroscience, where it has been widely considered to reflect prefrontal cortical functions. In this paper, we reexamine behavioral hierarchy and its neural substrates from the point of view of recent developments in computational reinforcement learning. Specifically, we consider a set of approaches known collectively as hierarchical reinforcement learning, which extend the reinforcement learning paradigm by allowing the learning agent to aggregate actions into reusable subroutines or skills. A close look at the components of hierarchical reinforcement learning suggests how they might map onto neural structures, in particular regions within the dorsolateral and orbital prefrontal cortex. It also suggests specific ways in which hierarchical reinforcement learning might provide a complement to existing psychological models of hierarchically structured behavior. A particularly important question that hierarchical reinforcement learning brings to the fore is that of how learning identifies new action routines that are likely to provide useful building blocks in solving a wide range of future problems. Here and at many other points, hierarchical reinforcement learning offers an appealing framework for investigating the computational and neural underpinnings of hierarchically structured behavior. PMID:18926527

Functional Specificity and Sex Differences in the Neural Circuits Supporting the Inhibition of Automatic Imitation.

PubMed

Darda, Kohinoor M; Butler, Emily E; Ramsey, Richard

2018-06-01

Humans show an involuntary tendency to copy other people's actions. Although automatic imitation builds rapport and affiliation between individuals, we do not copy actions indiscriminately. Instead, copying behaviors are guided by a selection mechanism, which inhibits some actions and prioritizes others. To date, the neural underpinnings of the inhibition of automatic imitation and differences between the sexes in imitation control are not well understood. Previous studies involved small sample sizes and low statistical power, which produced mixed findings regarding the involvement of domain-general and domain-specific neural architectures. Here, we used data from Experiment 1 ( N = 28) to perform a power analysis to determine the sample size required for Experiment 2 ( N = 50; 80% power). Using independent functional localizers and an analysis pipeline that bolsters sensitivity, during imitation control we show clear engagement of the multiple-demand network (domain-general), but no sensitivity in the theory-of-mind network (domain-specific). Weaker effects were observed with regard to sex differences, suggesting that there are more similarities than differences between the sexes in terms of the neural systems engaged during imitation control. In summary, neurocognitive models of imitation require revision to reflect that the inhibition of imitation relies to a greater extent on a domain-general selection system rather than a domain-specific system that supports social cognition.

Ionic mechanisms subserving mechanosensory transduction and neural integration in statocyst hair cells of Hermissenda

NASA Technical Reports Server (NTRS)

Farley, Joseph

1988-01-01

The neural processing of gravitational-produced sensory stimulation of statocyst hair cells in the nudibranch mollusk Hermissenda was studied. The goal in these studies was to understand how: gravireceptor neurons sense or transduce gravitational forces, gravitational stimulation is integrated so as to produce a graded receptor potential, and ultimately the generation of an action potential, and various neural adaptation phenomena which hair cells exhibit arise. The approach to these problems was primarily electrophysical.

Action Categorization in Rhesus Monkeys: discrimination of grasping from non-grasping manual motor acts.

PubMed

Nelissen, Koen; Vanduffel, Wim

2017-11-08

The ability to recognize others' actions is an important aspect of social behavior. While neurophysiological and behavioral research in monkeys has offered a better understanding of how the primate brain processes this type of information, further insight with respect to the neural correlates of action recognition requires tasks that allow recording of brain activity or perturbing brain regions while monkeys simultaneously make behavioral judgements about certain aspects of observed actions. Here we investigated whether rhesus monkeys could actively discriminate videos showing grasping or non-grasping manual motor acts in a two-alternative categorization task. After monkeys became proficient in this task, we tested their ability to generalize to a number of untrained, novel videos depicting grasps or other manual motor acts. Monkeys generalized to a wide range of novel human or conspecific grasping and non-grasping motor acts. They failed, however, for videos showing unfamiliar actions such as a non-biological effector performing a grasp, or a human hand touching an object with the back of the hand. This study shows the feasibility of training monkeys to perform active judgements about certain aspects of observed actions, instrumental for causal investigations into the neural correlates of action recognition.

The Role of the Human Mirror Neuron System in Supporting Communication in a Digital World.

PubMed

Dickerson, Kelly; Gerhardstein, Peter; Moser, Alecia

2017-01-01

Humans use both verbal and non-verbal communication to interact with others and their environment and increasingly these interactions are occurring in a digital medium. Whether live or digital, learning to communicate requires overcoming the correspondence problem: There is no direct mapping, or correspondence between perceived and self-produced signals. Reconciliation of the differences between perceived and produced actions, including linguistic actions, is difficult and requires integration across multiple modalities and neuro-cognitive networks. Recent work on the neural substrates of social learning suggests that there may be a common mechanism underlying the perception-production cycle for verbal and non-verbal communication. The purpose of this paper is to review evidence supporting the link between verbal and non-verbal communications, and to extend the hMNS literature by proposing that recent advances in communication technology, which at times have had deleterious effects on behavioral and perceptual performance, may disrupt the success of the hMNS in supporting social interactions because these technologies are virtual and spatiotemporal distributed nature.

Periodic bidirectional associative memory neural networks with distributed delays

NASA Astrophysics Data System (ADS)

Chen, Anping; Huang, Lihong; Liu, Zhigang; Cao, Jinde

2006-05-01

Some sufficient conditions are obtained for the existence and global exponential stability of a periodic solution to the general bidirectional associative memory (BAM) neural networks with distributed delays by using the continuation theorem of Mawhin's coincidence degree theory and the Lyapunov functional method and the Young's inequality technique. These results are helpful for designing a globally exponentially stable and periodic oscillatory BAM neural network, and the conditions can be easily verified and be applied in practice. An example is also given to illustrate our results.

Neural basis of understanding communicative actions: Changes associated with knowing the actor's intention and the meanings of the actions.

PubMed

Möttönen, Riikka; Farmer, Harry; Watkins, Kate E

2016-01-29

People can communicate by using hand actions, e.g., signs. Understanding communicative actions requires that the observer knows that the actor has an intention to communicate and the meanings of the actions. Here, we investigated how this prior knowledge affects processing of observed actions. We used functional MRI to determine changes in action processing when non-signers were told that the observed actions are communicative (i.e., signs) and learned the meanings of half of the actions. Processing of hand actions activated the left and right inferior frontal gyrus (IFG, BA 44 and 45) when the communicative intention of the actor was known, even when the meanings of the actions remained unknown. These regions were not active when the observers did not know about the communicative nature of the hand actions. These findings suggest that the left and right IFG play a role in understanding the intention of the actor, but do not process visuospatial features of the communicative actions. Knowing the meanings of the hand actions further enhanced activity in the anterior part of the IFG (BA 45), the inferior parietal lobule and posterior inferior and middle temporal gyri in the left hemisphere. These left-hemisphere language regions could provide a link between meanings and observed actions. In sum, the findings provide evidence for the segregation of the networks involved in the neural processing of visuospatial features of communicative hand actions and those involved in understanding the actor's intention and the meanings of the actions. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Neural representations of kinematic laws of motion: evidence for action-perception coupling.

PubMed

Dayan, Eran; Casile, Antonino; Levit-Binnun, Nava; Giese, Martin A; Hendler, Talma; Flash, Tamar

2007-12-18

Behavioral and modeling studies have established that curved and drawing human hand movements obey the 2/3 power law, which dictates a strong coupling between movement curvature and velocity. Human motion perception seems to reflect this constraint. The functional MRI study reported here demonstrates that the brain's response to this law of motion is much stronger and more widespread than to other types of motion. Compliance with this law is reflected in the activation of a large network of brain areas subserving motor production, visual motion processing, and action observation functions. Hence, these results strongly support the notion of similar neural coding for motion perception and production. These findings suggest that cortical motion representations are optimally tuned to the kinematic and geometrical invariants characterizing biological actions.

Calcium signaling mediates five types of cell morphological changes to form neural rosettes.

PubMed

Hříbková, Hana; Grabiec, Marta; Klemová, Dobromila; Slaninová, Iva; Sun, Yuh-Man

2018-02-12

Neural rosette formation is a critical morphogenetic process during neural development, whereby neural stem cells are enclosed in rosette niches to equipoise proliferation and differentiation. How neural rosettes form and provide a regulatory micro-environment remains to be elucidated. We employed the human embryonic stem cell-based neural rosette system to investigate the structural development and function of neural rosettes. Our study shows that neural rosette formation consists of five types of morphological change: intercalation, constriction, polarization, elongation and lumen formation. Ca 2+ signaling plays a pivotal role in the five steps by regulating the actions of the cytoskeletal complexes, actin, myosin II and tubulin during intercalation, constriction and elongation. These, in turn, control the polarizing elements, ZO-1, PARD3 and β-catenin during polarization and lumen production for neural rosette formation. We further demonstrate that the dismantlement of neural rosettes, mediated by the destruction of cytoskeletal elements, promotes neurogenesis and astrogenesis prematurely, indicating that an intact rosette structure is essential for orderly neural development. © 2018. Published by The Company of Biologists Ltd.

Memory as the "whole brain work": a large-scale model based on "oscillations in super-synergy".

PubMed

Başar, Erol

2005-01-01

According to recent trends, memory depends on several brain structures working in concert across many levels of neural organization; "memory is a constant work-in progress." The proposition of a brain theory based on super-synergy in neural populations is most pertinent for the understanding of this constant work in progress. This report introduces a new model on memory basing on the processes of EEG oscillations and Brain Dynamics. This model is shaped by the following conceptual and experimental steps: 1. The machineries of super-synergy in the whole brain are responsible for formation of sensory-cognitive percepts. 2. The expression "dynamic memory" is used for memory processes that evoke relevant changes in alpha, gamma, theta and delta activities. The concerted action of distributed multiple oscillatory processes provides a major key for understanding of distributed memory. It comprehends also the phyletic memory and reflexes. 3. The evolving memory, which incorporates reciprocal actions or reverberations in the APLR alliance and during working memory processes, is especially emphasized. 4. A new model related to "hierarchy of memories as a continuum" is introduced. 5. The notions of "longer activated memory" and "persistent memory" are proposed instead of long-term memory. 6. The new analysis to recognize faces emphasizes the importance of EEG oscillations in neurophysiology and Gestalt analysis. 7. The proposed basic framework called "Memory in the Whole Brain Work" emphasizes that memory and all brain functions are inseparable and are acting as a "whole" in the whole brain. 8. The role of genetic factors is fundamental in living system settings and oscillations and accordingly in memory, according to recent publications. 9. A link from the "whole brain" to "whole body," and incorporation of vegetative and neurological system, is proposed, EEG oscillations and ultraslow oscillations being a control parameter.

Molecular Basis of Paraltyic Neurotoxin Action on Voltage-Sensitive Sodium Channels

DTIC Science & Technology

1985-10-14

of 9,700 daltons isolated from the coral Goni2oora gy. (1). The toxin enhances neurally mediated contraction of blood vessels and taenia coli of the...sites on the solium channel and to identify the site of GPT action within the structure of the sodium channel protein. 2. Site of Action of Brvyetoxin

Action Mechanisms for Social Cognition: Behavioral and Neural Correlates of Developing Theory of Mind

ERIC Educational Resources Information Center

Bowman, Lindsay C.; Thorpe, Samuel G.; Cannon, Erin N.; Fox, Nathan A.

2017-01-01

Many psychological theories posit foundational links between two fundamental constructs: (1) our ability to produce, perceive, and represent action; and (2) our ability to understand the meaning and motivation behind the action (i.e. Theory of Mind; ToM). This position is contentious, however, and long-standing competing theories of…

Neural Priming in Human Frontal Cortex: Multiple Forms of Learning Reduce Demands on the Prefrontal Executive System

ERIC Educational Resources Information Center

Race, Elizabeth A.; Shanker, Shanti; Wagner, Anthony D.

2009-01-01

Past experience is hypothesized to reduce computational demands in PFC by providing bottom-up predictive information that informs subsequent stimulus-action mapping. The present fMRI study measured cortical activity reductions ("neural priming"/"repetition suppression") during repeated stimulus classification to investigate the mechanisms through…

Behavioral and Neurobiological Mechanisms of Extinction in Pavlovian and Instrumental Learning

PubMed Central

Todd, Travis P.; Vurbic, Drina; Bouton, Mark E.

2013-01-01

This article reviews research on the behavioral and neural mechanisms of extinction as it is represented in both Pavlovian and instrumental learning. In Pavlovian extinction, repeated presentation of a signal without its reinforcer weakens behavior evoked by the signal; in instrumental extinction, repeated occurrence of a voluntary action without its reinforcer weakens the strength of the action. In either case, contemporary research at both the behavioral and neural levels of analysis has been guided by a set of extinction principles that were first generated by research conducted at the behavioral level. The review discusses these principles and illustrates how they have informed the study of both Pavlovian and instrumental extinction. It shows that behavioral and neurobiological research efforts have been tightly linked and that their results are readily integrated. Pavlovian and instrumental extinction are also controlled by compatible behavioral and neural processes. Since many behavioral effects observed in extinction can be multiply determined, we suggest that the current close connection between behavioral-level and neural-level analyses will need to continue. PMID:23999219

A neural model of hierarchical reinforcement learning

PubMed Central

Rasmussen, Daniel; Eliasmith, Chris

2017-01-01

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain’s general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model’s behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions. PMID:28683111

Behavioral and neurobiological mechanisms of extinction in Pavlovian and instrumental learning.

PubMed

Todd, Travis P; Vurbic, Drina; Bouton, Mark E

2014-02-01

This article reviews research on the behavioral and neural mechanisms of extinction as it is represented in both Pavlovian and instrumental learning. In Pavlovian extinction, repeated presentation of a signal without its reinforcer weakens behavior evoked by the signal; in instrumental extinction, repeated occurrence of a voluntary action without its reinforcer weakens the strength of the action. In either case, contemporary research at both the behavioral and neural levels of analysis has been guided by a set of extinction principles that were first generated by research conducted at the behavioral level. The review discusses these principles and illustrates how they have informed the study of both Pavlovian and instrumental extinction. It shows that behavioral and neurobiological research efforts have been tightly linked and that their results are readily integrated. Pavlovian and instrumental extinction are also controlled by compatible behavioral and neural processes. Since many behavioral effects observed in extinction can be multiply determined, we suggest that the current close connection between behavioral-level and neural-level analyses will need to continue. Copyright © 2013 Elsevier Inc. All rights reserved.

Interference from related actions in spoken word production: Behavioural and fMRI evidence.

PubMed

de Zubicaray, Greig; Fraser, Douglas; Ramajoo, Kori; McMahon, Katie

2017-02-01

Few investigations of lexical access in spoken word production have investigated the cognitive and neural mechanisms involved in action naming. These are likely to be more complex than the mechanisms involved in object naming, due to the ways in which conceptual features of action words are represented. The present study employed a blocked cyclic naming paradigm to examine whether related action contexts elicit a semantic interference effect akin to that observed with categorically related objects. Participants named pictures of intransitive actions to avoid a confound with object processing. In Experiment 1, body-part related actions (e.g., running, walking, skating, hopping) were named significantly slower compared to unrelated actions (e.g., laughing, running, waving, hiding). Experiment 2 employed perfusion functional Magnetic Resonance Imaging (fMRI) to investigate the neural mechanisms involved in this semantic interference effect. Compared to unrelated actions, naming related actions elicited significant perfusion signal increases in frontotemporal cortex, including bilateral inferior frontal gyrus (IFG) and hippocampus, and decreases in bilateral posterior temporal, occipital and parietal cortices, including intraparietal sulcus (IPS). The findings demonstrate a role for temporoparietal cortex in conceptual-lexical processing of intransitive action knowledge during spoken word production, and support the proposed involvement of interference resolution and incremental learning mechanisms in the blocked cyclic naming paradigm. Copyright © 2017 Elsevier Ltd. All rights reserved.

Developmental excitatory-to-inhibitory GABA polarity switch is delayed in Ts65Dn mice, a genetic model of Down syndrome.

PubMed

Lysenko, Larisa V; Kim, Jeesun; Madamba, Francisco; Tyrtyshnaia, Anna A; Ruparelia, Aarti; Kleschevnikov, Alexander M

2018-07-01

Down syndrome (DS) is the most frequent genetic cause of developmental abnormalities leading to intellectual disability. One notable phenomenon affecting the formation of nascent neural circuits during late developmental periods is developmental switch of GABA action from depolarizing to hyperpolarizing mode. We examined properties of this switch in DS using primary cultures and acute hippocampal slices from Ts65Dn mice, a genetic model of DS. Cultures of DIV3-DIV13 Ts65Dn and control normosomic (2 N) neurons were loaded with FURA-2 AM, and GABA action was assessed using local applications. In 2 N cultures, the number of GABA-activated cells dropped from ~100% to 20% between postnatal days 3-13 (P3-P13) reflecting the switch in GABA action polarity. In Ts65Dn cultures, the timing of this switch was delayed by 2-3 days. Next, microelectrode recordings of multi-unit activity (MUA) were performed in CA3 slices during bath application of the GABA A agonist isoguvacine. MUA frequency was increased in P8-P12 and reduced in P14-P22 slices reflecting the switch of GABA action from excitatory to inhibitory mode. The timing of this switch was delayed in Ts65Dn by approximately 2 days. Finally, frequency of giant depolarizing potentials (GDPs), a form of primordial neural activity, was significantly increased in slices from Ts65Dn pups at P12 and P14. These experimental evidences show that GABA action polarity switch is delayed in Ts65Dn model of DS, and that these changes lead to a delay in maturation of nascent neural circuits. These alterations may affect properties of neural circuits in adult animals and, therefore, represent a prospective target for pharmacotherapy of cognitive impairment in DS. Copyright © 2018 Elsevier Inc. All rights reserved.

LIN28A enhances the therapeutic potential of cultured neural stem cells in a Parkinson's disease model.

PubMed

Rhee, Yong-Hee; Kim, Tae-Ho; Jo, A-Young; Chang, Mi-Yoon; Park, Chang-Hwan; Kim, Sang-Mi; Song, Jae-Jin; Oh, Sang-Min; Yi, Sang-Hoon; Kim, Hyeon Ho; You, Bo-Hyun; Nam, Jin-Wu; Lee, Sang-Hun

2016-10-01

The original properties of tissue-specific stem cells, regardless of their tissue origins, are inevitably altered during in vitro culturing, lessening the clinical and research utility of stem cell cultures. Specifically, neural stem cells derived from the ventral midbrain lose their dopamine neurogenic potential, ventral midbrain-specific phenotypes, and repair capacity during in vitro cell expansion, all of which are critical concerns in using the cultured neural stem cells in therapeutic approaches for Parkinson's disease. In this study, we observed that the culture-dependent changes of neural stem cells derived from the ventral midbrain coincided with loss of RNA-binding protein LIN28A expression. When LIN28A expression was forced and sustained during neural stem cell expansion using an inducible expression-vector system, loss of dopamine neurogenic potential and midbrain phenotypes after long-term culturing was blocked. Furthermore, dopamine neurons that differentiated from neural stem cells exhibited remarkable survival and resistance against toxic insults. The observed effects were not due to a direct action of LIN28A on the differentiated dopamine neurons, but rather its action on precursor neural stem cells as exogene expression was switched off in the differentiating/differentiated cultures. Remarkable and reproducible behavioural recovery was shown in all Parkinson's disease rats grafted with neural stem cells expanded with LIN28A expression, along with extensive engraftment of dopamine neurons expressing mature neuronal and midbrain-specific markers. These findings suggest that LIN28A expression during stem cell expansion could be used to prepare therapeutically competent donor cells. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.

PubMed

Ly, Cheng; Marsat, Gary

2018-02-01

Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling. Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous neural attributes alter firing rate heterogeneity, accounting for the effect with various sensory stimuli. The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks: the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) when firing rate heterogeneity is low and is negatively correlated with excitability with high firing rate heterogeneity. We also show how our theory can be used to predict effective neural architecture. We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and discuss how it can ultimately shape population codes.

Privacy-preserving backpropagation neural network learning.

PubMed

Chen, Tingting; Zhong, Sheng

2009-10-01

With the development of distributed computing environment , many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an important learning model, multilayer neural networks. We present a privacy-preserving two-party distributed algorithm of backpropagation which allows a neural network to be trained without requiring either party to reveal her data to the other. We provide complete correctness and security analysis of our algorithms. The effectiveness of our algorithms is verified by experiments on various real world data sets.

Neurofeedback Training for BCI Control

NASA Astrophysics Data System (ADS)

Neuper, Christa; Pfurtscheller, Gert

Brain-computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2-4].

Mirror neurons and the social nature of language: the neural exploitation hypothesis.

PubMed

Gallese, Vittorio

2008-01-01

This paper discusses the relevance of the discovery of mirror neurons in monkeys and of the mirror neuron system in humans to a neuroscientific account of primates' social cognition and its evolution. It is proposed that mirror neurons and the functional mechanism they underpin, embodied simulation, can ground within a unitary neurophysiological explanatory framework important aspects of human social cognition. In particular, the main focus is on language, here conceived according to a neurophenomenological perspective, grounding meaning on the social experience of action. A neurophysiological hypothesis--the "neural exploitation hypothesis"--is introduced to explain how key aspects of human social cognition are underpinned by brain mechanisms originally evolved for sensorimotor integration. It is proposed that these mechanisms were later on adapted as new neurofunctional architecture for thought and language, while retaining their original functions as well. By neural exploitation, social cognition and language can be linked to the experiential domain of action.

Intentional attunement: a neurophysiological perspective on social cognition and its disruption in autism.

PubMed

Gallese, Vittorio

2006-03-24

A direct form of experiential understanding of others, "intentional attunement", is achieved by modeling their behavior as intentional experiences on the basis of the activation of shared neural systems underpinning what the others do an feel and what we do and feel. This modeling mechanism is embodied simulation. In parallel with the detached sensory description of the observed social stimuli, internal representations of the body states associated with actions, emotions, and sensations are evoked in the observer, as if he/she would be doing a similar action or experiencing a similar emotion or sensation. Mirror neuron systems are likely the neural correlate of this mechanism. By means of a shared neural state realized in two different bodies, the "objectual other" becomes "another self". A defective intentional attunement caused by a lack of embodies simulation might cause some of the social impairments of autistic individuals.

Evidence for a neural law of effect.

PubMed

Athalye, Vivek R; Santos, Fernando J; Carmena, Jose M; Costa, Rui M

2018-03-02

Thorndike's law of effect states that actions that lead to reinforcements tend to be repeated more often. Accordingly, neural activity patterns leading to reinforcement are also reentered more frequently. Reinforcement relies on dopaminergic activity in the ventral tegmental area (VTA), and animals shape their behavior to receive dopaminergic stimulation. Seeking evidence for a neural law of effect, we found that mice learn to reenter more frequently motor cortical activity patterns that trigger optogenetic VTA self-stimulation. Learning was accompanied by gradual shaping of these patterns, with participating neurons progressively increasing and aligning their covariance to that of the target pattern. Motor cortex patterns that lead to phasic dopaminergic VTA activity are progressively reinforced and shaped, suggesting a mechanism by which animals select and shape actions to reliably achieve reinforcement. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Anger in brain and body: the neural and physiological perturbation of decision-making by emotion

PubMed Central

Zorab, Emma; Navaratnam, Nakulan; Engels, Miriam; Mallorquí-Bagué, Núria; Minati, Ludovico; Dowell, Nicholas G.; Brosschot, Jos F.; Thayer, Julian F.; Critchley, Hugo D.

2016-01-01

Emotion and cognition are dynamically coupled to bodily arousal: the induction of anger, even unconsciously, can reprioritise neural and physiological resources toward action states that bias cognitive processes. Here we examine behavioural, neural and bodily effects of covert anger processing and its influence on cognition, indexed by lexical decision-making. While recording beat-to-beat blood pressure, the words ANGER or RELAX were presented subliminally just prior to rapid word/non-word reaction-time judgements of letter-strings. Subliminal ANGER primes delayed the time taken to reach rapid lexical decisions, relative to RELAX primes. However, individuals with high trait anger were speeded up by subliminal anger primes. ANGER primes increased systolic blood pressure and the magnitude of this increase predicted reaction time prolongation. Within the brain, ANGER trials evoked an enhancement of activity within dorsal pons and an attenuation of activity within visual occipitotemporal and attentional parietal cortices. Activity within periaqueductal grey matter, occipital and parietal regions increased linearly with evoked blood pressure changes, indicating neural substrates through which covert anger impairs semantic decisions, putatively through its expression as visceral arousal. The behavioural and physiological impact of anger states compromises the efficiency of cognitive processing through action-ready changes in autonomic response that skew regional neural activity. PMID:26253525

Tympanal mechanics and neural responses in the ears of a noctuid moth

NASA Astrophysics Data System (ADS)

Ter Hofstede, Hannah M.; Goerlitz, Holger R.; Montealegre-Z, Fernando; Robert, Daniel; Holderied, Marc W.

2011-12-01

Ears evolved in many groups of moths to detect the echolocation calls of predatory bats. Although the neurophysiology of bat detection has been intensively studied in moths for decades, the relationship between sound-induced movement of the noctuid tympanic membrane and action potentials in the auditory sensory cells (A1 and A2) has received little attention. Using laser Doppler vibrometry, we measured the velocity and displacement of the tympanum in response to pure tone pulses for moths that were intact or prepared for neural recording. When recording from the auditory nerve, the displacement of the tympanum at the neural threshold remained constant across frequencies, whereas velocity varied with frequency. This suggests that the key biophysical parameter for triggering action potentials in the sensory cells of noctuid moths is tympanum displacement, not velocity. The validity of studies on the neurophysiology of moth hearing rests on the assumption that the dissection and recording procedures do not affect the biomechanics of the ear. There were no consistent differences in tympanal velocity or displacement when moths were intact or prepared for neural recordings for sound levels close to neural threshold, indicating that this and other neurophysiological studies provide good estimates of what intact moths hear at threshold.

Imitation and observational learning of hand actions: prefrontal involvement and connectivity.

PubMed

Higuchi, S; Holle, H; Roberts, N; Eickhoff, S B; Vogt, S

2012-01-16

The first aim of this event-related fMRI study was to identify the neural circuits involved in imitation learning. We used a rapid imitation task where participants directly imitated pictures of guitar chords. The results provide clear evidence for the involvement of dorsolateral prefrontal cortex, as well as the fronto-parietal mirror circuit (FPMC) during action imitation when the requirements for working memory are low. Connectivity analyses further indicated a robust connectivity between left prefrontal cortex and the components of the FPMC bilaterally. We conclude that a mechanism of automatic perception-action matching alone is insufficient to account for imitation learning. Rather, the motor representation of an observed, complex action, as provided by the FPMC, only serves as the 'raw material' for higher-order supervisory and monitoring operations associated with the prefrontal cortex. The second aim of this study was to assess whether these neural circuits are also recruited during observational practice (OP, without motor execution), or only during physical practice (PP). Whereas prefrontal cortex was not consistently activated in action observation across all participants, prefrontal activation intensities did predict the behavioural practice effects, thus indicating a crucial role of prefrontal cortex also in OP. In addition, whilst OP and PP produced similar activation intensities in the FPMC when assessed during action observation, during imitative execution, the practice-related activation decreases were significantly more pronounced for PP than for OP. This dissociation indicates a lack of execution-related resources in observationally practised actions. More specifically, we found neural efficiency effects in the right motor cingulate-basal ganglia circuit and the FPMC that were only observed after PP but not after OP. Finally, we confirmed that practice generally induced activation decreases in the FPMC during both action observation and imitation sessions and outline a framework explaining the discrepant findings in the literature. Copyright © 2011 Elsevier Inc. All rights reserved.

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 .

Vicariously touching products through observing others' hand actions increases purchasing intention, and the effect of visual perspective in this process: An fMRI study.

PubMed

Liu, Yi; Zang, Xuelian; Chen, Lihan; Assumpção, Leonardo; Li, Hong

2018-01-01

The growth of online shopping increases consumers' dependence on vicarious sensory experiences, such as observing others touching products in commercials. However, empirical evidence on whether observing others' sensory experiences increases purchasing intention is still scarce. In the present study, participants observed others interacting with products in the first- or third-person perspective in video clips, and their neural responses were measured with functional magnetic resonance imaging (fMRI). We investigated (1) whether and how vicariously touching certain products affected purchasing intention, and the neural correlates of this process; and (2) how visual perspective interacts with vicarious tactility. Vicarious tactile experiences were manipulated by hand actions touching or not touching the products, while the visual perspective was manipulated by showing the hand actions either in first- or third-person perspective. During the fMRI scanning, participants watched the video clips and rated their purchasing intention for each product. The results showed that, observing others touching (vs. not touching) the products increased purchasing intention, with vicarious neural responses found in mirror neuron systems (MNS) and lateral occipital complex (LOC). Moreover, the stronger neural activities in MNS was associated with higher purchasing intention. The effects of visual perspectives were found in left superior parietal lobule (SPL), while the interaction of tactility and visual perspective was shown in precuneus and precuneus-LOC connectivity. The present study provides the first evidence that vicariously touching a given product increased purchasing intention and the neural activities in bilateral MNS, LOC, left SPL and precuneus are involved in this process. Hum Brain Mapp 39:332-343, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Decoding Information for Grasping from the Macaque Dorsomedial Visual Stream.

PubMed

Filippini, Matteo; Breveglieri, Rossella; Akhras, M Ali; Bosco, Annalisa; Chinellato, Eris; Fattori, Patrizia

2017-04-19

Neurodecoders have been developed by researchers mostly to control neuroprosthetic devices, but also to shed new light on neural functions. In this study, we show that signals representing grip configurations can be reliably decoded from neural data acquired from area V6A of the monkey medial posterior parietal cortex. Two Macaca fascicularis monkeys were trained to perform an instructed-delay reach-to-grasp task in the dark and in the light toward objects of different shapes. Population neural activity was extracted at various time intervals on vision of the objects, the delay before movement, and grasp execution. This activity was used to train and validate a Bayes classifier used for decoding objects and grip types. Recognition rates were well over chance level for all the epochs analyzed in this study. Furthermore, we detected slightly different decoding accuracies, depending on the task's visual condition. Generalization analysis was performed by training and testing the system during different time intervals. This analysis demonstrated that a change of code occurred during the course of the task. Our classifier was able to discriminate grasp types fairly well in advance with respect to grasping onset. This feature might be important when the timing is critical to send signals to external devices before the movement start. Our results suggest that the neural signals from the dorsomedial visual pathway can be a good substrate to feed neural prostheses for prehensile actions. SIGNIFICANCE STATEMENT Recordings of neural activity from nonhuman primate frontal and parietal cortex have led to the development of methods of decoding movement information to restore coordinated arm actions in paralyzed human beings. Our results show that the signals measured from the monkey medial posterior parietal cortex are valid for correctly decoding information relevant for grasping. Together with previous studies on decoding reach trajectories from the medial posterior parietal cortex, this highlights the medial parietal cortex as a target site for transforming neural activity into control signals to command prostheses to allow human patients to dexterously perform grasping actions. Copyright © 2017 the authors 0270-6474/17/374311-12$15.00/0.

Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control.

PubMed

Guo, Zhenyuan; Yang, Shaofu; Wang, Jun

2016-12-01

This paper presents theoretical results on global exponential synchronization of multiple memristive neural networks in the presence of external noise by means of two types of distributed pinning control. The multiple memristive neural networks are coupled in a general structure via a nonlinear function, which consists of a linear diffusive term and a discontinuous sign term. A pinning impulsive control law is introduced in the coupled system to synchronize all neural networks. Sufficient conditions are derived for ascertaining global exponential synchronization in mean square. In addition, a pinning adaptive control law is developed to achieve global exponential synchronization in mean square. Both pinning control laws utilize only partial state information received from the neighborhood of the controlled neural network. Simulation results are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Caudate Microstimulation Increases Value of Specific Choices.

PubMed

Santacruz, Samantha R; Rich, Erin L; Wallis, Joni D; Carmena, Jose M

2017-11-06

Value-based decision-making involves an assessment of the value of items available and the actions required to obtain them. The basal ganglia are highly implicated in action selection and goal-directed behavior [1-4], and the striatum in particular plays a critical role in arbitrating between competing choices [5-9]. Previous work has demonstrated that neural activity in the caudate nucleus is modulated by task-relevant action values [6, 8]. Nonetheless, how value is represented and maintained in the striatum remains unclear since decision-making in these tasks relied on spatially lateralized responses, confounding the ability to generalize to a more abstract choice task [6, 8, 9]. Here, we investigate striatal value representations by applying caudate electrical stimulation in macaque monkeys (n = 3) to bias decision-making in a task that divorces the value of a stimulus from motor action. Electrical microstimulation is known to induce neural plasticity [10, 11], and caudate microstimulation in primates has been shown to accelerate associative learning [12, 13]. Our results indicate that stimulation paired with a particular stimulus increases selection of that stimulus, and this effect was stimulus dependent and action independent. The modulation of choice behavior using microstimulation was best modeled as resulting from changes in stimulus value. Caudate neural recordings (n = 1) show that changes in value-coding neuron activity are stimulus value dependent. We argue that caudate microstimulation can differentially increase stimulus values independent of action, and unilateral manipulations of value are sufficient to mediate choice behavior. These results support potential future applications of microstimulation to correct maladaptive plasticity underlying dysfunctional decision-making related to neuropsychiatric conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural correlates of emotional action control in anger-prone women with borderline personality disorder.

PubMed

Bertsch, Katja; Roelofs, Karin; Roch, Paul Jonathan; Ma, Bo; Hensel, Saskia; Herpertz, Sabine C; Volman, Inge

2018-05-01

Difficulty in controlling emotional impulses is a crucial component of borderline personality disorder (BPD) that often leads to destructive, impulsive behaviours against others. In line with recent findings in aggressive individuals, deficits in prefrontal amygdala coupling during emotional action control may account for these symptoms. To study the neurobiological correlates of altered emotional action control in individuals with BPD, we asked medication-free, anger-prone, female patients with BPD and age- and intelligence-matched healthy women to take part in an approach-avoidance task while lying in an MRI scanner. The task required controlling fast behavioural tendencies to approach happy and avoid angry faces. Additionally, before the task we collected saliva testosterone and self-reported information on tendencies to act out anger and correlated this with behavioural and functional MRI (fMRI) data. We included 30 patients and 28 controls in our analysis. Patients with BPD reported increased tendencies to act out anger and were faster in approaching than avoiding angry faces than with healthy women, suggesting deficits in emotional action control in women with BPD. On a neural level, controlling fast emotional action tendencies was associated with enhanced activation in the antero- and dorsolateral prefrontal cortex across groups. Healthy women showed a negative coupling between the left dorsolateral prefrontal cortex and right amygdala, whereas this was absent in patients with BPD. Specificity of results to BPD and sex differences remain unknown owing to the lack of clinical control groups and male participants. The results indicate reduced lateral prefrontal-amygdala communication during emotional action control in anger-prone women with BPD. The findings provide a possible neural mechanism underlying difficulties with controlling emotional impulses in patients with BPD.

Particle identification with neural networks using a rotational invariant moment representation

NASA Astrophysics Data System (ADS)

Sinkus, Ralph; Voss, Thomas

1997-02-01

A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. A preprocessing procedure is applied to the spatial energy distribution of the particle shower in order to account for the varying geometry of the calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The distributions of moments exhibit very different scales, thus the multidimensional input distribution for the neural network is transformed via a principal component analysis and rescaled by its respective variances to ensure input values of the order of one. This increases the sensitivity of the network and thus results in better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies.

Distributed computing methodology for training neural networks in an image-guided diagnostic application.

PubMed

Plagianakos, V P; Magoulas, G D; Vrahatis, M N

2006-03-01

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

The Smoke around Mirror Neurons: Goals as Sociocultural and Emotional Organizers of Perception and Action in Learning

ERIC Educational Resources Information Center

Immordino-Yang, Mary Helen

2008-01-01

From the pragmatists to the neo-Piagetians, development has been understood to involve cycles of perception and action--the internalization of interactions with the world and the construction of skills for acting in the world. From a neurobiological standpoint, new evidence suggests that neural activities related to action and perception converge…

Neural Correlates of Action Observation and Execution in 14-Month-Old Infants: An Event-Related EEG Desynchronization Study

ERIC Educational Resources Information Center

Marshall, Peter J.; Young, Thomas; Meltzoff, Andrew N.

2011-01-01

There is increasing interest in neurobiological methods for investigating the shared representation of action perception and production in early development. We explored the extent and regional specificity of EEG desynchronization in the infant alpha frequency range (6-9 Hz) during action observation and execution in 14-month-old infants.…

The Neural Correlates of Infant and Adult Goal Prediction: Evidence for Semantic Processing Systems

ERIC Educational Resources Information Center

Reid, Vincent M.; Hoehl, Stefanie; Grigutsch, Maren; Groendahl, Anna; Parise, Eugenio; Striano, Tricia

2009-01-01

The sequential nature of action ensures that an individual can anticipate the conclusion of an observed action via the use of semantic rules. The semantic processing of language and action has been linked to the N400 component of the event-related potential (ERP). The authors developed an ERP paradigm in which infants and adults observed simple…

Updated energy budgets for neural computation in the neocortex and cerebellum

PubMed Central

Howarth, Clare; Gleeson, Padraig; Attwell, David

2012-01-01

The brain's energy supply determines its information processing power, and generates functional imaging signals. The energy use on the different subcellular processes underlying neural information processing has been estimated previously for the grey matter of the cerebral and cerebellar cortex. However, these estimates need reevaluating following recent work demonstrating that action potentials in mammalian neurons are much more energy efficient than was previously thought. Using this new knowledge, this paper provides revised estimates for the energy expenditure on neural computation in a simple model for the cerebral cortex and a detailed model of the cerebellar cortex. In cerebral cortex, most signaling energy (50%) is used on postsynaptic glutamate receptors, 21% is used on action potentials, 20% on resting potentials, 5% on presynaptic transmitter release, and 4% on transmitter recycling. In the cerebellar cortex, excitatory neurons use 75% and inhibitory neurons 25% of the signaling energy, and most energy is used on information processing by non-principal neurons: Purkinje cells use only 15% of the signaling energy. The majority of cerebellar signaling energy use is on the maintenance of resting potentials (54%) and postsynaptic receptors (22%), while action potentials account for only 17% of the signaling energy use. PMID:22434069

Early oestrogens in shaping reproductive networks: evidence for a potential organisational role of oestradiol in female brain development.

PubMed

Bakker, J; Brock, O

2010-07-01

A central tenet of contemporary theories on mammalian brain and behavioural sexual differentiation is that an organisational action of testosterone, secreted by the male's testes, controls male-typical aspects of brain and behavioural development, whereas no active perinatal sex hormone signalling is required for female-typical sexual differentiation. Furthermore, the available evidence suggests that many, although not all, of the perinatal organisational actions of testosterone on the development of the male brain result from the cellular effects of oestradiol formed via neural aromatisation of testosterone. However, a default developmental programme for the female brain has been criticised. Indeed, we review new results obtained in aromatase knockout mice indicating that oestradiol actively contributes to the differentiation of female-typical aspects of brain and behavioural sexual differentiation. Furthermore, we propose that male-typical neural and behavioural differentiation occurs prenatally in genetic males under the influence of oestradiol, which is avoided in foetal genetic females by the neuroprotective actions of alpha-fetoprotein, whereas female-typical neural and behavioural differentiation normally occurs postnatally in genetic females under the influence of oestradiol that is presumably produced by the ovaries.

On the Control of Social Approach-Avoidance Behavior: Neural and Endocrine Mechanisms.

PubMed

Kaldewaij, Reinoud; Koch, Saskia B J; Volman, Inge; Toni, Ivan; Roelofs, Karin

The ability to control our automatic action tendencies is crucial for adequate social interactions. Emotional events trigger automatic approach and avoidance tendencies. Although these actions may be generally adaptive, the capacity to override these emotional reactions may be key to flexible behavior during social interaction. The present chapter provides a review of the neuroendocrine mechanisms underlying this ability and their relation to social psychopathologies. Aberrant social behavior, such as observed in social anxiety or psychopathy, is marked by abnormalities in approach-avoidance tendencies and the ability to control them. Key neural regions involved in the regulation of approach-avoidance behavior are the amygdala, widely implicated in automatic emotional processing, and the anterior prefrontal cortex, which exerts control over the amygdala. Hormones, especially testosterone and cortisol, have been shown to affect approach-avoidance behavior and the associated neural mechanisms. The present chapter also discusses ways to directly influence social approach and avoidance behavior and will end with a research agenda to further advance this important research field. Control over approach-avoidance tendencies may serve as an exemplar of emotional action regulation and might have a great value in understanding the underlying mechanisms of the development of affective disorders.

Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems

NASA Technical Reports Server (NTRS)

Lima, Pedro; Beard, Randal

1992-01-01

The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a lookup table for the world model and implement the Q function with a neural net. Time limitations prevented the combination of these two approaches. The final section discusses the results and gives clues for future work.

GANEing on emotion and emotion regulation.

PubMed

Hull, Thomas D

2016-01-01

The function of emotion and its underlying neural mechanisms are often left underspecified. I extend the GANE (glutamate amplifies noradrenergic effects) model by examining its success in accounting for findings in emotion regulation. I also identify points of alignment with construction models of emotion and with the hypothesis that emotion states function to push neural activity toward rapid and efficient action.

NeuroCognitive Patterns

DTIC Science & Technology

2016-10-28

that exploits environmental and contextual information to provide likely interpretations for those neural signals. Innovative models for event...users. While the neural signals are vital to this architecture, contextual and environmental information is also needed in order to best anticipate...what action is intended. In order to obtain that contextual and environmental information, a commercially available Kinect Sensor is used to capture

Brain basis of communicative actions in language

PubMed Central

Egorova, Natalia; Shtyrov, Yury; Pulvermüller, Friedemann

2016-01-01

Although language is a key tool for communication in social interaction, most studies in the neuroscience of language have focused on language structures such as words and sentences. Here, the neural correlates of speech acts, that is, the actions performed by using language, were investigated with functional magnetic resonance imaging (fMRI). Participants were shown videos, in which the same critical utterances were used in different communicative contexts, to Name objects, or to Request them from communication partners. Understanding of critical utterances as Requests was accompanied by activation in bilateral premotor, left inferior frontal and temporo-parietal cortical areas known to support action-related and social interactive knowledge. Naming, however, activated the left angular gyrus implicated in linking information about word forms and related reference objects mentioned in critical utterances. These findings show that understanding of utterances as different communicative actions is reflected in distinct brain activation patterns, and thus suggest different neural substrates for different speech act types. PMID:26505303

Brain basis of communicative actions in language.

PubMed

Egorova, Natalia; Shtyrov, Yury; Pulvermüller, Friedemann

2016-01-15

Although language is a key tool for communication in social interaction, most studies in the neuroscience of language have focused on language structures such as words and sentences. Here, the neural correlates of speech acts, that is, the actions performed by using language, were investigated with functional magnetic resonance imaging (fMRI). Participants were shown videos, in which the same critical utterances were used in different communicative contexts, to Name objects, or to Request them from communication partners. Understanding of critical utterances as Requests was accompanied by activation in bilateral premotor, left inferior frontal and temporo-parietal cortical areas known to support action-related and social interactive knowledge. Naming, however, activated the left angular gyrus implicated in linking information about word forms and related reference objects mentioned in critical utterances. These findings show that understanding of utterances as different communicative actions is reflected in distinct brain activation patterns, and thus suggest different neural substrates for different speech act types. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Fuzzy-Neural Controller in Service Requests Distribution Broker for SOA-Based Systems

NASA Astrophysics Data System (ADS)

Fras, Mariusz; Zatwarnicka, Anna; Zatwarnicki, Krzysztof

The evolution of software architectures led to the rising importance of the Service Oriented Architecture (SOA) concept. This architecture paradigm support building flexible distributed service systems. In the paper the architecture of service request distribution broker designed for use in SOA-based systems is proposed. The broker is built with idea of fuzzy control. The functional and non-functional request requirements in conjunction with monitoring of execution and communication links are used to distribute requests. Decisions are made with use of fuzzy-neural network.

Rapid Sensitization of Physiological, Neuronal, and Locomotor Effects of Nicotine: Critical Role of Peripheral Drug Actions

PubMed Central

Lenoir, Magalie; Tang, Jeremy S.; Woods, Amina S.

2013-01-01

Repeated exposure to nicotine and other psychostimulant drugs produces persistent increases in their psychomotor and physiological effects (sensitization), a phenomenon related to the drugs' reinforcing properties and abuse potential. Here we examined the role of peripheral actions of nicotine in nicotine-induced sensitization of centrally mediated physiological parameters (brain, muscle, and skin temperatures), cortical and VTA EEG, neck EMG activity, and locomotion in freely moving rats. Repeated injections of intravenous nicotine (30 μg/kg) induced sensitization of the drug's effects on all these measures. In contrast, repeated injections of the peripherally acting analog of nicotine, nicotine pyrrolidine methiodide (nicotinePM, 30 μg/kg, i.v.) resulted in habituation (tolerance) of the same physiological, neuronal, and behavioral measures. However, after repeated nicotine exposure, acute nicotinePM injections induced nicotine-like physiological responses: powerful cortical and VTA EEG desynchronization, EMG activation, a large brain temperature increase, but weaker hyperlocomotion. Additionally, both the acute locomotor response to nicotine and nicotine-induced locomotor sensitization were attenuated by blockade of peripheral nicotinic receptors by hexamethonium (3 mg/kg, i.v.). These data suggest that the peripheral actions of nicotine, which precede its direct central actions, serve as a conditioned interoceptive cue capable of eliciting nicotine-like physiological and neural responses after repeated nicotine exposure. Thus, by providing a neural signal to the CNS that is repeatedly paired with the direct central effects of nicotine, the drug's peripheral actions play a critical role in the development of nicotine-induced physiological, neural, and behavioral sensitization. PMID:23761889

Rapid sensitization of physiological, neuronal, and locomotor effects of nicotine: critical role of peripheral drug actions.

PubMed

Lenoir, Magalie; Tang, Jeremy S; Woods, Amina S; Kiyatkin, Eugene A

2013-06-12

Repeated exposure to nicotine and other psychostimulant drugs produces persistent increases in their psychomotor and physiological effects (sensitization), a phenomenon related to the drugs' reinforcing properties and abuse potential. Here we examined the role of peripheral actions of nicotine in nicotine-induced sensitization of centrally mediated physiological parameters (brain, muscle, and skin temperatures), cortical and VTA EEG, neck EMG activity, and locomotion in freely moving rats. Repeated injections of intravenous nicotine (30 μg/kg) induced sensitization of the drug's effects on all these measures. In contrast, repeated injections of the peripherally acting analog of nicotine, nicotine pyrrolidine methiodide (nicotine(PM), 30 μg/kg, i.v.) resulted in habituation (tolerance) of the same physiological, neuronal, and behavioral measures. However, after repeated nicotine exposure, acute nicotine(PM) injections induced nicotine-like physiological responses: powerful cortical and VTA EEG desynchronization, EMG activation, a large brain temperature increase, but weaker hyperlocomotion. Additionally, both the acute locomotor response to nicotine and nicotine-induced locomotor sensitization were attenuated by blockade of peripheral nicotinic receptors by hexamethonium (3 mg/kg, i.v.). These data suggest that the peripheral actions of nicotine, which precede its direct central actions, serve as a conditioned interoceptive cue capable of eliciting nicotine-like physiological and neural responses after repeated nicotine exposure. Thus, by providing a neural signal to the CNS that is repeatedly paired with the direct central effects of nicotine, the drug's peripheral actions play a critical role in the development of nicotine-induced physiological, neural, and behavioral sensitization.

Neural Encoding and Integration of Learned Probabilistic Sequences in Avian Sensory-Motor Circuitry

PubMed Central

Brainard, Michael S.

2013-01-01

Many complex behaviors, such as human speech and birdsong, reflect a set of categorical actions that can be flexibly organized into variable sequences. However, little is known about how the brain encodes the probabilities of such sequences. Behavioral sequences are typically characterized by the probability of transitioning from a given action to any subsequent action (which we term “divergence probability”). In contrast, we hypothesized that neural circuits might encode the probability of transitioning to a given action from any preceding action (which we term “convergence probability”). The convergence probability of repeatedly experienced sequences could naturally become encoded by Hebbian plasticity operating on the patterns of neural activity associated with those sequences. To determine whether convergence probability is encoded in the nervous system, we investigated how auditory-motor neurons in vocal premotor nucleus HVC of songbirds encode different probabilistic characterizations of produced syllable sequences. We recorded responses to auditory playback of pseudorandomly sequenced syllables from the bird's repertoire, and found that variations in responses to a given syllable could be explained by a positive linear dependence on the convergence probability of preceding sequences. Furthermore, convergence probability accounted for more response variation than other probabilistic characterizations, including divergence probability. Finally, we found that responses integrated over >7–10 syllables (∼700–1000 ms) with the sign, gain, and temporal extent of integration depending on convergence probability. Our results demonstrate that convergence probability is encoded in sensory-motor circuitry of the song-system, and suggest that encoding of convergence probability is a general feature of sensory-motor circuits. PMID:24198363

Distributed formation control of nonholonomic autonomous vehicle via RBF neural network

NASA Astrophysics Data System (ADS)

Yang, Shichun; Cao, Yaoguang; Peng, Zhaoxia; Wen, Guoguang; Guo, Konghui

2017-03-01

In this paper, RBF neural network consensus-based distributed control scheme is proposed for nonholonomic autonomous vehicles in a pre-defined formation along the specified reference trajectory. A variable transformation is first designed to convert the formation control problem into a state consensus problem. Then, the complete dynamics of the vehicles including inertia, Coriolis, friction model and unmodeled bounded disturbances are considered, which lead to the formation unstable when the distributed kinematic controllers are proposed based on the kinematics. RBF neural network torque controllers are derived to compensate for them. Some sufficient conditions are derived to accomplish the asymptotically stability of the systems based on algebraic graph theory, matrix theory, and Lyapunov theory. Finally, simulation examples illustrate the effectiveness of the proposed controllers.

Characterization of TLX expression in neural stem cells and progenitor cells in adult brains.

PubMed

Li, Shengxiu; Sun, Guoqiang; Murai, Kiyohito; Ye, Peng; Shi, Yanhong

2012-01-01

TLX has been shown to play an important role in regulating the self-renewal and proliferation of neural stem cells in adult brains. However, the cellular distribution of endogenous TLX protein in adult brains remains to be elucidated. In this study, we used immunostaining with a TLX-specific antibody to show that TLX is expressed in both neural stem cells and transit-amplifying neural progenitor cells in the subventricular zone (SVZ) of adult mouse brains. Then, using a double thymidine analog labeling approach, we showed that almost all of the self-renewing neural stem cells expressed TLX. Interestingly, most of the TLX-positive cells in the SVZ represented the thymidine analog-negative, relatively quiescent neural stem cell population. Using cell type markers and short-term BrdU labeling, we demonstrated that TLX was also expressed in the Mash1+ rapidly dividing type C cells. Furthermore, loss of TLX expression dramatically reduced BrdU label-retaining neural stem cells and the actively dividing neural progenitor cells in the SVZ, but substantially increased GFAP staining and extended GFAP processes. These results suggest that TLX is essential to maintain the self-renewing neural stem cells in the SVZ and that the GFAP+ cells in the SVZ lose neural stem cell property upon loss of TLX expression. Understanding the cellular distribution of TLX and its function in specific cell types may provide insights into the development of therapeutic tools for neurodegenerative diseases by targeting TLX in neural stem/progenitors cells.

Characterization of TLX Expression in Neural Stem Cells and Progenitor Cells in Adult Brains

PubMed Central

Li, Shengxiu; Sun, Guoqiang; Murai, Kiyohito; Ye, Peng; Shi, Yanhong

2012-01-01

TLX has been shown to play an important role in regulating the self-renewal and proliferation of neural stem cells in adult brains. However, the cellular distribution of endogenous TLX protein in adult brains remains to be elucidated. In this study, we used immunostaining with a TLX-specific antibody to show that TLX is expressed in both neural stem cells and transit-amplifying neural progenitor cells in the subventricular zone (SVZ) of adult mouse brains. Then, using a double thymidine analog labeling approach, we showed that almost all of the self-renewing neural stem cells expressed TLX. Interestingly, most of the TLX-positive cells in the SVZ represented the thymidine analog-negative, relatively quiescent neural stem cell population. Using cell type markers and short-term BrdU labeling, we demonstrated that TLX was also expressed in the Mash1+ rapidly dividing type C cells. Furthermore, loss of TLX expression dramatically reduced BrdU label-retaining neural stem cells and the actively dividing neural progenitor cells in the SVZ, but substantially increased GFAP staining and extended GFAP processes. These results suggest that TLX is essential to maintain the self-renewing neural stem cells in the SVZ and that the GFAP+ cells in the SVZ lose neural stem cell property upon loss of TLX expression.Understanding the cellular distribution of TLX and its function in specific cell types may provide insights into the development of therapeutic tools for neurodegenerative diseases by targeting TLX in neural stem/progenitors cells. PMID:22952666

The neural correlates of 'vitality form' recognition: an fMRI study: this work is dedicated to Daniel Stern, whose immeasurable contribution to science has inspired our research.

PubMed

Di Cesare, Giuseppe; Di Dio, Cinzia; Rochat, Magali J; Sinigaglia, Corrado; Bruschweiler-Stern, Nadia; Stern, Daniel N; Rizzolatti, Giacomo

2014-07-01

The observation of goal-directed actions performed by another individual allows one to understand what that individual is doing and why he/she is doing it. Important information about others' behaviour is also carried out by the dynamics of the observed action. Action dynamics characterize the 'vitality form' of an action describing the cognitive and affective relation between the performing agent and the action recipient. Here, using the fMRI technique, we assessed the neural correlates of vitality form recognition presenting participants with videos showing two actors executing actions with different vitality forms: energetic and gentle. The participants viewed the actions in two tasks. In one task (what), they had to focus on the goal of the presented action; in the other task (how), they had to focus on the vitality form. For both tasks, activations were found in the action observation/execution circuit. Most interestingly, the contrast how vs what revealed activation in right dorso-central insula, highlighting the involvement, in the recognition of vitality form, of an anatomical region connecting somatosensory areas with the medial temporal region and, in particular, with the hippocampus. This somatosensory-insular-limbic circuit could underlie the observers' capacity to understand the vitality forms conveyed by the observed action. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Sensitivity to structure in action sequences: An infant event-related potential study.

PubMed

Monroy, Claire D; Gerson, Sarah A; Domínguez-Martínez, Estefanía; Kaduk, Katharina; Hunnius, Sabine; Reid, Vincent

2017-05-06

Infants are sensitive to structure and patterns within continuous streams of sensory input. This sensitivity relies on statistical learning, the ability to detect predictable regularities in spatial and temporal sequences. Recent evidence has shown that infants can detect statistical regularities in action sequences they observe, but little is known about the neural process that give rise to this ability. In the current experiment, we combined electroencephalography (EEG) with eye-tracking to identify electrophysiological markers that indicate whether 8-11-month-old infants detect violations to learned regularities in action sequences, and to relate these markers to behavioral measures of anticipation during learning. In a learning phase, infants observed an actor performing a sequence featuring two deterministic pairs embedded within an otherwise random sequence. Thus, the first action of each pair was predictive of what would occur next. One of the pairs caused an action-effect, whereas the second did not. In a subsequent test phase, infants observed another sequence that included deviant pairs, violating the previously observed action pairs. Event-related potential (ERP) responses were analyzed and compared between the deviant and the original action pairs. Findings reveal that infants demonstrated a greater Negative central (Nc) ERP response to the deviant actions for the pair that caused the action-effect, which was consistent with their visual anticipations during the learning phase. Findings are discussed in terms of the neural and behavioral processes underlying perception and learning of structured action sequences. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hadoop neural network for parallel and distributed feature selection.

PubMed

Hodge, Victoria J; O'Keefe, Simon; Austin, Jim

2016-06-01

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

TRH regulates action potential shape in cerebral cortex pyramidal neurons.

PubMed

Rodríguez-Molina, Víctor; Patiño, Javier; Vargas, Yamili; Sánchez-Jaramillo, Edith; Joseph-Bravo, Patricia; Charli, Jean-Louis

2014-07-07

Thyrotropin releasing hormone (TRH) is a neuropeptide with a wide neural distribution and a variety of functions. It modulates neuronal electrophysiological properties, including resting membrane potential, as well as excitatory postsynaptic potential and spike frequencies. We explored, with whole-cell patch clamp, TRH effect on action potential shape in pyramidal neurons of the sensorimotor cortex. TRH reduced spike and after hyperpolarization amplitudes, and increased spike half-width. The effect varied with dose, time and cortical layer. In layer V, 0.5µM of TRH induced a small increase in spike half-width, while 1 and 5µM induced a strong but transient change in spike half-width, and amplitude; after hyperpolarization amplitude was modified at 5µM of TRH. Cortical layers III and VI neurons responded intensely to 0.5µM TRH; layer II neurons response was small. The effect of 1µM TRH on action potential shape in layer V neurons was blocked by G-protein inhibition. Inhibition of the activity of the TRH-degrading enzyme pyroglutamyl peptidase II (PPII) reproduced the effect of TRH, with enhanced spike half-width. Many cortical PPII mRNA+ cells were VGLUT1 mRNA+, and some GAD mRNA+. These data show that TRH regulates action potential shape in pyramidal cortical neurons, and are consistent with the hypothesis that PPII controls its action in this region. Copyright © 2014 Elsevier B.V. All rights reserved.

Neural basis of postural focus effect on concurrent postural and motor tasks: phase-locked electroencephalogram responses.

PubMed

Huang, Cheng-Ya; Zhao, Chen-Guang; Hwang, Ing-Shiou

2014-11-01

Dual-task performance is strongly affected by the direction of attentional focus. This study investigated neural control of a postural-suprapostural procedure when postural focus strategy varied. Twelve adults concurrently conducted force-matching and maintained stabilometer stance with visual feedback on ankle movement (visual internal focus, VIF) and on stabilometer movement (visual external focus, VEF). Force-matching error, dynamics of ankle and stabilometer movements, and event-related potentials (ERPs) were registered. Postural control with VEF caused superior force-matching performance, more complex ankle movement, and stronger kinematic coupling between the ankle and stabilometer movements than postural control with VIF. The postural focus strategy also altered ERP temporal-spatial patterns. Postural control with VEF resulted in later N1 with less negativity around the bilateral fronto-central and contralateral sensorimotor areas, earlier P2 deflection with more positivity around the bilateral fronto-central and ipsilateral temporal areas, and late movement-related potential commencing in the left frontal-central area, as compared with postural control with VIF. The time-frequency distribution of the ERP principal component revealed phase-locked neural oscillations in the delta (1-4Hz), theta (4-7Hz), and beta (13-35Hz) rhythms. The delta and theta rhythms were more pronounced prior to the timing of P2 positive deflection, and beta rebound was greater after the completion of force-matching in VEF condition than VIF condition. This study is the first to reveal the neural correlation of postural focusing effect on a postural-suprapostural task. Postural control with VEF takes advantage of efficient task-switching to facilitate autonomous postural response, in agreement with the "constrained-action" hypothesis. Copyright © 2014 Elsevier B.V. All rights reserved.

A Prospective Longitudinal Study of Perceived Infant Outcomes at 18–24 Months: Neural and Psychological Correlates of Parental Thoughts and Actions Assessed during the First Month Postpartum

PubMed Central

Kim, Pilyoung; Rigo, Paola; Leckman, James F.; Mayes, Linda C.; Cole, Pamela M.; Feldman, Ruth; Swain, James E.

2015-01-01

The first postpartum months constitute a critical period for parents to establish an emotional bond with their infants. Neural responses to infant-related stimuli have been associated with parental sensitivity. However, the associations among these neural responses, parenting, and later infant outcomes for mothers and fathers are unknown. In the current longitudinal study, we investigated the relationships between parental thoughts/actions and neural activation in mothers and fathers in the neonatal period with infant outcomes at the toddler stage. At the first month postpartum, mothers (n = 21) and fathers (n = 19) underwent a neuroimaging session during which they listened to their own and unfamiliar baby’s cry. Parenting-related thoughts/behaviors were assessed by interview twice at the first month and 3–4 months postpartum and infants’ socioemotional outcomes were reported by mothers and fathers at 18–24 months postpartum. In mothers, higher levels of anxious thoughts/actions about parenting at the first month postpartum, but not at 3–4 months postpartum, were associated with infant’s low socioemotional competencies at 18–24 months. Anxious thoughts/actions were also associated with heightened responses in the motor cortex and reduced responses in the substantia nigra to own infant cry sounds. On the other hand, in fathers, higher levels of positive perception of being a parent at the first month postpartum, but not at 3–4 months postpartum, were associated with higher infant socioemotional competencies at 18–24 months. Positive thoughts were associated with heightened responses in the auditory cortex and caudate to own infant cry sounds. The current study provides evidence that parental thoughts are related to concurrent neural responses to their infants at the first month postpartum as well as their infant’s future socioemotional outcome at 18–24 months. Parent differences suggest that anxious thoughts in mothers and positive thoughts in fathers may be the targets for parenting-focused interventions very early postpartum. PMID:26635679

Self-organizing neural integration of pose-motion features for human action recognition

PubMed Central

Parisi, German I.; Weber, Cornelius; Wermter, Stefan

2015-01-01

The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented toward human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR) networks that obtain progressively generalized representations of sensory inputs and learn inherent spatio-temporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions. PMID:26106323

A neural network model of causative actions.

PubMed

Lee-Hand, Jeremy; Knott, Alistair

2015-01-01

A common idea in models of action representation is that actions are represented in terms of their perceptual effects (see e.g., Prinz, 1997; Hommel et al., 2001; Sahin et al., 2007; Umiltà et al., 2008; Hommel, 2013). In this paper we extend existing models of effect-based action representations to account for a novel distinction. Some actions bring about effects that are independent events in their own right: for instance, if John smashes a cup, he brings about the event of the cup smashing. Other actions do not bring about such effects. For instance, if John grabs a cup, this action does not cause the cup to "do" anything: a grab action has well-defined perceptual effects, but these are not registered by the perceptual system that detects independent events involving external objects in the world. In our model, effect-based actions are implemented in several distinct neural circuits, which are organized into a hierarchy based on the complexity of their associated perceptual effects. The circuit at the top of this hierarchy is responsible for actions that bring about independently perceivable events. This circuit receives input from the perceptual module that recognizes arbitrary events taking place in the world, and learns movements that reliably cause such events. We assess our model against existing experimental observations about effect-based motor representations, and make some novel experimental predictions. We also consider the possibility that the "causative actions" circuit in our model can be identified with a motor pathway reported in other work, specializing in "functional" actions on manipulable tools (Bub et al., 2008; Binkofski and Buxbaum, 2013).

Analysis of fault-tolerant neurocontrol architectures

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1992-01-01

The fault-tolerance of analog parallel distributed implementations of a multivariable aircraft neurocontroller is analyzed by simulating weight and neuron failures in a simplified scheme of analog processing based on the functional architecture of the ETANN chip (Electrically Trainable Artificial Neural Network). The neural information processing is found to be only partially distributed throughout the set of weights of the neurocontroller synthesized with the backpropagation algorithm. Although the degree of distribution of the neural processing, and consequently the fault-tolerance of the neurocontroller, could be enhanced using Locally Distributed Weight and Neuron Approaches, a satisfactory level of fault-tolerance could only be obtained by retraining the degrated VLSI neurocontroller. The possibility of maintaining neurocontrol performance and stability in the presence of single weight of neuron failures was demonstrated through an automated retraining procedure of the neurocontroller based on a pre-programmed choice and sequence of the training parameters.

[Head and neck paragangliomas. Embryological origin and anatomical characteristics: topographic distribution and vascularization pattern].

PubMed

Carretero González, José; Blanco Pérez, Pedro; Vázquez Osorio, María Teresa; Benito González, Fernando; Sañudo Tejedo, José Ramón

2009-02-01

Paragangliomas are tumors that arise in the extraadrenal paraganglia and result from migration of neural crest cells during embryonic development. Based on their anatomical distribution, innervation and microscopic structure, these tumors can be classified into interrelated families: branchiomeric paraganglia (related to the branchial clefts and arches), intravagal, aortic-sympathetic and visceral-autonomic. Head and neck paragangliomas belong mainly to the first two of these families. The present article is divided into two parts. The first part reviews the embryological origin of these tumors. Special emphasis is placed on the process of neurulation or neural tube formation, neurosegmentation (with a summary of the mechanisms involved in the initial segmentation of the neural tube and of the hindbrain and spinal medulla), and the development of the sensory placodes and secondary inductions in the cranial region. Subsequently, the neural crest is analyzed, with special attention paid to the cranial neural crest. The embryonogenesis of paragangliomas is also described. The second part describes the topographical distribution of head and neck paragangliomas according to their localization: jugulotympanic, orbit, intercarotid, subclavian and laryngeal. The embryonogenesis and most important anatomical characteristics are described for each type.

The influence of action observation on action execution: Dissociating the contribution of action on perception, perception on action, and resolving conflict.

PubMed

Deschrijver, Eliane; Wiersema, Jan R; Brass, Marcel

2017-04-01

For more than 15 years, motor interference paradigms have been used to investigate the influence of action observation on action execution. Most research on so-called automatic imitation has focused on variables that play a modulating role or investigated potential confounding factors. Interestingly, furthermore, a number of functional magnetic resonance imaging (fMRI) studies have tried to shed light on the functional mechanisms and neural correlates involved in imitation inhibition. However, these fMRI studies, presumably due to poor temporal resolution, have primarily focused on high-level processes and have neglected the potential role of low-level motor and perceptual processes. In the current EEG study, we therefore aimed to disentangle the influence of low-level perceptual and motoric mechanisms from high-level cognitive mechanisms. We focused on potential congruency differences in the visual N190 - a component related to the processing of biological motion, the Readiness Potential - a component related to motor preparation, and the high-level P3 component. Interestingly, we detected congruency effects in each of these components, suggesting that the interference effect in an automatic imitation paradigm is not only related to high-level processes such as self-other distinction but also to more low-level influences of perception on action and action on perception. Moreover, we documented relationships of the neural effects with (autistic) behavior.

What Are You Doing with That Object? Comparing the Neural Responses of Action Understanding in Adolescents with and without Autism

ERIC Educational Resources Information Center

Pokorny, Jennifer J.; Hatt, Naomi V.; Rogers, Sally J.; Rivera, Susan M.

2018-01-01

Understanding another's actions, including what they are doing and why they are doing it, can be difficult for individuals with autism spectrum disorder (ASD). This understanding is supported by the action observation (AON) and mentalizing (MZN) networks, as well as the superior temporal sulcus. We examined these areas in children with ASD and…

Machinery Monitoring and Diagnostics Using Pseudo Wigner-Ville Distribution and Backpropagation Neural Network

DTIC Science & Technology

1993-09-01

frequency, which when used as an input to an artificial neural network will aide in the detection of location and severity of machinery faults...Research is presented where the union of an artificial neural network , utilizing the highly successful backpropagation paradigm, and the pseudo wigner

Critical Branching Neural Networks

ERIC Educational Resources Information Center

Kello, Christopher T.

2013-01-01

It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

Neurophysiological correlates of linearization in language production

PubMed Central

Habets, Boukje; Jansma, Bernadette M; Münte, Thomas F

2008-01-01

Background During speech production the planning of a description of several events requires, among other things, a verbal sequencing of these events. During this process, referred to as linearization during conceptualization, the speaker can choose between different types of temporal connectives such as 'Before' X did A, Y did B' or 'After' Y did B, X did A'. To capture the neural events of such linearization processes, event-related potentials (ERP) were measured in native speakers of German. Utterances were elicited by presenting a sequence of two pictures on a video screen. Each picture consists of an object that is associated with a particular action (e.g. book = reading). A coloured vocalization cue indicated to describe the sequence of two actions associated with the objects in chronological (e.g. red cue: 'After' I drove the car, I read a book) or reversed order (yellow cue). Results Brain potentials showed reliable differences between the two conditions from 180 ms after the onset of the vocalization prompt, with ERPs from the 'After' condition being more negative. This 'Before/After' difference showed a fronto-central distribution between 180 and 230 ms. From 300 ms onwards, a parietal distribution was observed. The latter effect is interpreted as an instance of the P300 response, which is known to be modulated by task difficulty. Conclusion ERPs preceding overt sentence production are sensitive to conceptual linearization. The observed early, more fronto-centrally distributed variation could be interpreted as involvement of working memory needed to order the events according to the instruction. The later parietal distributed variation relates to the complexity in linearization, with the non-chronological order being more demanding during the updating of the concepts in working memory. PMID:18681961

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

PubMed

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

2018-05-22

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

Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

PubMed

Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

2017-08-01

This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

Extracting an evaluative feedback from the brain for adaptation of motor neuroprosthetic decoders.

PubMed

Mahmoudi, Babak; Principe, Jose C; Sanchez, Justin C

2010-01-01

The design of Brain-Machine Interface (BMI) neural decoders that have robust performance in changing environments encountered in daily life activity is a challenging problem. One solution to this problem is the design of neural decoders that are able to assist and adapt to the user by participating in their perception-action-reward cycle (PARC). Using inspiration both from artificial intelligence and neurobiology reinforcement learning theories, we have designed a novel decoding architecture that enables a symbiotic relationship between the user and an Intelligent Assistant (IA). By tapping into the motor and reward centers in the brain, the IA adapts the process of decoding neural motor commands into prosthetic actions based on the user's goals. The focus of this paper is on extraction of goal information directly from the brain and making it accessible to the IA as an evaluative feedback for adaptation. We have recorded the neural activity of the Nucleus Accumbens in behaving rats during a reaching task. The peri-event time histograms demonstrate a rich representation of the reward prediction in this subcortical structure that can be modeled on a single trial basis as a scalar evaluative feedback with high precision.

Neural circuits and motivational processes for hunger

PubMed Central

Sternson, Scott M; Betley, J Nicholas; Huang Cao, Zhen Fang

2014-01-01

How does an organism’s internal state direct its actions? At one moment an animal forages for food with acrobatic feats such as tree climbing and jumping between branches. At another time, it travels along the ground to find water or a mate, exposing itself to predators along the way. These behaviors are costly in terms of energy or physical risk, and the likelihood of performing one set of actions relative to another is strongly modulated by internal state. For example, an animal in energy deficit searches for food and a dehydrated animal looks for water. The crosstalk between physiological state and motivational processes influences behavioral intensity and intent, but the underlying neural circuits are poorly understood. Molecular genetics along with optogenetic and pharmacogenetic tools for perturbing neuron function have enabled cell type-selective dissection of circuits that mediate behavioral responses to physiological state changes. Here, we review recent progress into neural circuit analysis of hunger in the mouse by focusing on a starvation-sensitive neuron population in the hypothalamus that is sufficient to promote voracious eating. We also consider research into the motivational processes that are thought to underlie hunger in order to outline considerations for bridging the gap between homeostatic and motivational neural circuits. PMID:23648085

Particle identification with neural networks using a rotational invariant moment representation

NASA Astrophysics Data System (ADS)

Sinkus, R.; Voss, T.

1997-02-01

A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The multidimensional input distribution for the neural network is transformed via a principle component analysis and rescaled by its respective variances to ensure input values of the order of one. This results is a better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies.

LMI-based approach to stability analysis for fractional-order neural networks with discrete and distributed delays

NASA Astrophysics Data System (ADS)

Zhang, Hai; Ye, Renyu; Liu, Song; Cao, Jinde; Alsaedi, Ahmad; Li, Xiaodi

2018-02-01

This paper is concerned with the asymptotic stability of the Riemann-Liouville fractional-order neural networks with discrete and distributed delays. By constructing a suitable Lyapunov functional, two sufficient conditions are derived to ensure that the addressed neural network is asymptotically stable. The presented stability criteria are described in terms of the linear matrix inequalities. The advantage of the proposed method is that one may avoid calculating the fractional-order derivative of the Lyapunov functional. Finally, a numerical example is given to show the validity and feasibility of the theoretical results.

Statistical methods and neural network approaches for classification of data from multiple sources

NASA Technical Reports Server (NTRS)

Benediktsson, Jon Atli; Swain, Philip H.

1990-01-01

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Anger in brain and body: the neural and physiological perturbation of decision-making by emotion.

PubMed

Garfinkel, Sarah N; Zorab, Emma; Navaratnam, Nakulan; Engels, Miriam; Mallorquí-Bagué, Núria; Minati, Ludovico; Dowell, Nicholas G; Brosschot, Jos F; Thayer, Julian F; Critchley, Hugo D

2016-01-01

Emotion and cognition are dynamically coupled to bodily arousal: the induction of anger, even unconsciously, can reprioritise neural and physiological resources toward action states that bias cognitive processes. Here we examine behavioural, neural and bodily effects of covert anger processing and its influence on cognition, indexed by lexical decision-making. While recording beat-to-beat blood pressure, the words ANGER or RELAX were presented subliminally just prior to rapid word/non-word reaction-time judgements of letter-strings. Subliminal ANGER primes delayed the time taken to reach rapid lexical decisions, relative to RELAX primes. However, individuals with high trait anger were speeded up by subliminal anger primes. ANGER primes increased systolic blood pressure and the magnitude of this increase predicted reaction time prolongation. Within the brain, ANGER trials evoked an enhancement of activity within dorsal pons and an attenuation of activity within visual occipitotemporal and attentional parietal cortices. Activity within periaqueductal grey matter, occipital and parietal regions increased linearly with evoked blood pressure changes, indicating neural substrates through which covert anger impairs semantic decisions, putatively through its expression as visceral arousal. The behavioural and physiological impact of anger states compromises the efficiency of cognitive processing through action-ready changes in autonomic response that skew regional neural activity. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A Neural Model for Temporal Order Judgments and Their Active Recalibration: A Common Mechanism for Space and Time?

PubMed Central

Cai, Mingbo; Stetson, Chess; Eagleman, David M.

2012-01-01

When observers experience a constant delay between their motor actions and sensory feedback, their perception of the temporal order between actions and sensations adapt (Stetson et al., 2006). We present here a novel neural model that can explain temporal order judgments (TOJs) and their recalibration. Our model employs three ubiquitous features of neural systems: (1) information pooling, (2) opponent processing, and (3) synaptic scaling. Specifically, the model proposes that different populations of neurons encode different delays between motor-sensory events, the outputs of these populations feed into rivaling neural populations (encoding “before” and “after”), and the activity difference between these populations determines the perceptual judgment. As a consequence of synaptic scaling of input weights, motor acts which are consistently followed by delayed sensory feedback will cause the network to recalibrate its point of subjective simultaneity. The structure of our model raises the possibility that recalibration of TOJs is a temporal analog to the motion aftereffect (MAE). In other words, identical neural mechanisms may be used to make perceptual determinations about both space and time. Our model captures behavioral recalibration results for different numbers of adapting trials and different adapting delays. In line with predictions of the model, we additionally demonstrate that temporal recalibration can last through time, in analogy to storage of the MAE. PMID:23130010

Movement Interference in Autism-Spectrum Disorder

ERIC Educational Resources Information Center

Gowen, E.; Stanley, J.; Miall, R. C.

2008-01-01

Movement interference occurs when concurrently observing and executing incompatible actions and is believed to be due to co-activation of conflicting populations of mirror neurons. It has also been suggested that mirror neurons contribute towards the imitation of observed actions. However, the exact neural substrate of imitation may depend on task…

Notochord-derived Shh concentrates in close association with the apically positioned basal body in neural target cells and forms a dynamic gradient during neural patterning.

PubMed

Chamberlain, Chester E; Jeong, Juhee; Guo, Chaoshe; Allen, Benjamin L; McMahon, Andrew P

2008-03-01

Sonic hedgehog (Shh) ligand secreted by the notochord induces distinct ventral cell identities in the adjacent neural tube by a concentration-dependent mechanism. To study this process, we genetically engineered mice that produce bioactive, fluorescently labeled Shh from the endogenous locus. We show that Shh ligand concentrates in close association with the apically positioned basal body of neural target cells, forming a dynamic, punctate gradient in the ventral neural tube. Both ligand lipidation and target field response influence the gradient profile, but not the ability of Shh to concentrate around the basal body. Further, subcellular analysis suggests that Shh from the notochord might traffic into the neural target field by means of an apical-to-basal-oriented microtubule scaffold. This study, in which we directly observe, measure, localize and modify notochord-derived Shh ligand in the context of neural patterning, provides several new insights into mechanisms of Shh morphogen action.

Corticosteroids: way upstream

PubMed Central

2010-01-01

Studies into the mechanisms of corticosteroid action continue to be a rich bed of research, spanning the fields of neuroscience and endocrinology through to immunology and metabolism. However, the vast literature generated, in particular with respect to corticosteroid actions in the brain, tends to be contentious, with some aspects suffering from loose definitions, poorly-defined models, and appropriate dissection kits. Here, rather than presenting a comprehensive review of the subject, we aim to present a critique of key concepts that have emerged over the years so as to stimulate new thoughts in the field by identifying apparent shortcomings. This article will draw on experience and knowledge derived from studies of the neural actions of other steroid hormones, in particular estrogens, not only because there are many parallels but also because 'learning from differences' can be a fruitful approach. The core purpose of this review is to consider the mechanisms through which corticosteroids might act rapidly to alter neural signaling. PMID:20180948

Neural Basis of Reinforcement Learning and Decision Making

PubMed Central

Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan

2012-01-01

Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain. PMID:22462543

[Hardware Implementation of Numerical Simulation Function of Hodgkin-Huxley Model Neurons Action Potential Based on Field Programmable Gate Array].

PubMed

Wang, Jinlong; Lu, Mai; Hu, Yanwen; Chen, Xiaoqiang; Pan, Qiangqiang

2015-12-01

Neuron is the basic unit of the biological neural system. The Hodgkin-Huxley (HH) model is one of the most realistic neuron models on the electrophysiological characteristic description of neuron. Hardware implementation of neuron could provide new research ideas to clinical treatment of spinal cord injury, bionics and artificial intelligence. Based on the HH model neuron and the DSP Builder technology, in the present study, a single HH model neuron hardware implementation was completed in Field Programmable Gate Array (FPGA). The neuron implemented in FPGA was stimulated by different types of current, the action potential response characteristics were analyzed, and the correlation coefficient between numerical simulation result and hardware implementation result were calculated. The results showed that neuronal action potential response of FPGA was highly consistent with numerical simulation result. This work lays the foundation for hardware implementation of neural network.

Semantic Interpretation of An Artificial Neural Network

DTIC Science & Technology

1995-12-01

ARTIFICIAL NEURAL NETWORK .7,’ THESIS Stanley Dale Kinderknecht Captain, USAF 770 DEAT7ET77,’H IR O C 7... ARTIFICIAL NEURAL NETWORK THESIS Stanley Dale Kinderknecht Captain, USAF AFIT/GCS/ENG/95D-07 Approved for public release; distribution unlimited The views...Government. AFIT/GCS/ENG/95D-07 SEMANTIC INTERPRETATION OF AN ARTIFICIAL NEURAL NETWORK THESIS Presented to the Faculty of the School of Engineering of

Cognitive and Neural Sciences Division 1991 Programs

DTIC Science & Technology

1991-08-01

FUNDING NUMBERS Cognitive and Neural Sciences Division 1991 Programs PE 61153N 6. AUTHOR(S) Edited by Willard S. Vaughan 7. PERFORMING ORGANIZATION...NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER Office of Naval Research 0CNR !1491-19 Cognitive and Neural Sciences Division Code 1142...NOTES iN This is a compilation of abstracts representing R&D sponsored by the ONR Cognitive and Neural Sciences Division. 12a. DISTRIBUTION

What can autism teach us about the role of sensorimotor systems in higher cognition? New clues from studies on language, action semantics, and abstract emotional concept processing.

PubMed

Moseley, Rachel L; Pulvermüller, Friedemann

2018-03-01

Within the neurocognitive literature there is much debate about the role of the motor system in language, social communication and conceptual processing. We suggest, here, that autism spectrum conditions (ASC) may afford an excellent test case for investigating and evaluating contemporary neurocognitive models, most notably a neurobiological theory of action perception integration where widely-distributed cell assemblies linking neurons in action and perceptual brain regions act as the building blocks of many higher cognitive functions. We review a literature of functional motor abnormalities in ASC, following this with discussion of their neural correlates and aberrancies in language development, explaining how these might arise with reference to the typical formation of cell assemblies linking action and perceptual brain regions. This model gives rise to clear hypotheses regarding language comprehension, and we highlight a recent set of studies reporting differences in brain activation and behaviour in the processing of action-related and abstract-emotional concepts in individuals with ASC. At the neuroanatomical level, we discuss structural differences in long-distance frontotemporal and frontoparietal connections in ASC, such as would compromise information transfer between sensory and motor regions. This neurobiological model of action perception integration may shed light on the cognitive and social-interactive symptoms of ASC, building on and extending earlier proposals linking autistic symptomatology to motor disorder and dysfunction in action perception integration. Further investigating the contribution of motor dysfunction to higher cognitive and social impairment, we suggest, is timely and promising as it may advance both neurocognitive theory and the development of new clinical interventions for this population and others characterised by early and pervasive motor disruption. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

How the Human Brain Represents Perceived Dangerousness or “Predacity” of Animals

PubMed Central

Sha, Long; Guntupalli, J. Swaroop; Oosterhof, Nikolaas; Halchenko, Yaroslav O.; Nastase, Samuel A.; di Oleggio Castello, Matteo Visconti; Abdi, Hervé; Jobst, Barbara C.; Gobbini, M. Ida; Haxby, James V.

2016-01-01

Common or folk knowledge about animals is dominated by three dimensions: (1) level of cognitive complexity or “animacy;” (2) dangerousness or “predacity;” and (3) size. We investigated the neural basis of the perceived dangerousness or aggressiveness of animals, which we refer to more generally as “perception of threat.” Using functional magnetic resonance imaging (fMRI), we analyzed neural activity evoked by viewing images of animal categories that spanned the dissociable semantic dimensions of threat and taxonomic class. The results reveal a distributed network for perception of threat extending along the right superior temporal sulcus. We compared neural representational spaces with target representational spaces based on behavioral judgments and a computational model of early vision and found a processing pathway in which perceived threat emerges as a dominant dimension: whereas visual features predominate in early visual cortex and taxonomy in lateral occipital and ventral temporal cortices, these dimensions fall away progressively from posterior to anterior temporal cortices, leaving threat as the dominant explanatory variable. Our results suggest that the perception of threat in the human brain is associated with neural structures that underlie perception and cognition of social actions and intentions, suggesting a broader role for these regions than has been thought previously, one that includes the perception of potential threat from agents independent of their biological class. SIGNIFICANCE STATEMENT For centuries, philosophers have wondered how the human mind organizes the world into meaningful categories and concepts. Today this question is at the core of cognitive science, but our focus has shifted to understanding how knowledge manifests in dynamic activity of neural systems in the human brain. This study advances the young field of empirical neuroepistemology by characterizing the neural systems engaged by an important dimension in our cognitive representation of the animal kingdom ontological subdomain: how the brain represents the perceived threat, dangerousness, or “predacity” of animals. Our findings reveal how activity for domain-specific knowledge of animals overlaps the social perception networks of the brain, suggesting domain-general mechanisms underlying the representation of conspecifics and other animals. PMID:27170133

Spatiotemporal dynamics of rhythmic spinal interneurons measured with two-photon calcium imaging and coherence analysis.

PubMed

Kwan, Alex C; Dietz, Shelby B; Zhong, Guisheng; Harris-Warrick, Ronald M; Webb, Watt W

2010-12-01

In rhythmic neural circuits, a neuron often fires action potentials with a constant phase to the rhythm, a timing relationship that can be functionally significant. To characterize these phase preferences in a large-scale, cell type-specific manner, we adapted multitaper coherence analysis for two-photon calcium imaging. Analysis of simulated data showed that coherence is a simple and robust measure of rhythmicity for calcium imaging data. When applied to the neonatal mouse hindlimb spinal locomotor network, the phase relationships between peak activity of >1,000 ventral spinal interneurons and motor output were characterized. Most interneurons showed rhythmic activity that was coherent and in phase with the ipsilateral motor output during fictive locomotion. The phase distributions of two genetically identified classes of interneurons were distinct from the ensemble population and from each other. There was no obvious spatial clustering of interneurons with similar phase preferences. Together, these results suggest that cell type, not neighboring neuron activity, is a better indicator of an interneuron's response during fictive locomotion. The ability to measure the phase preferences of many neurons with cell type and spatial information should be widely applicable for studying other rhythmic neural circuits.

Action and semantic tool knowledge - Effective connectivity in the underlying neural networks.

PubMed

Kleineberg, Nina N; Dovern, Anna; Binder, Ellen; Grefkes, Christian; Eickhoff, Simon B; Fink, Gereon R; Weiss, Peter H

2018-04-26

Evidence from neuropsychological and imaging studies indicate that action and semantic knowledge about tools draw upon distinct neural substrates, but little is known about the underlying interregional effective connectivity. With fMRI and dynamic causal modeling (DCM) we investigated effective connectivity in the left-hemisphere (LH) while subjects performed (i) a function knowledge and (ii) a value knowledge task, both addressing semantic tool knowledge, and (iii) a manipulation (action) knowledge task. Overall, the results indicate crosstalk between action nodes and semantic nodes. Interestingly, effective connectivity was weakened between semantic nodes and action nodes during the manipulation task. Furthermore, pronounced modulations of effective connectivity within the fronto-parietal action system of the LH (comprising lateral occipito-temporal cortex, intraparietal sulcus, supramarginal gyrus, inferior frontal gyrus) were observed in a bidirectional manner during the processing of action knowledge. In contrast, the function and value knowledge tasks resulted in a significant strengthening of the effective connectivity between visual cortex and fusiform gyrus. Importantly, this modulation was present in both semantic tasks, indicating that processing different aspects of semantic knowledge about tools evokes similar effective connectivity patterns. Data revealed that interregional effective connectivity during the processing of tool knowledge occurred in a bidirectional manner with a weakening of connectivity between areas engaged in action and semantic knowledge about tools during the processing of action knowledge. Moreover, different semantic tool knowledge tasks elicited similar effective connectivity patterns. © 2018 Wiley Periodicals, Inc.

The computational neurobiology of learning and reward.

PubMed

Daw, Nathaniel D; Doya, Kenji

2006-04-01

Following the suggestion that midbrain dopaminergic neurons encode a signal, known as a 'reward prediction error', used by artificial intelligence algorithms for learning to choose advantageous actions, the study of the neural substrates for reward-based learning has been strongly influenced by computational theories. In recent work, such theories have been increasingly integrated into experimental design and analysis. Such hybrid approaches have offered detailed new insights into the function of a number of brain areas, especially the cortex and basal ganglia. In part this is because these approaches enable the study of neural correlates of subjective factors (such as a participant's beliefs about the reward to be received for performing some action) that the computational theories purport to quantify.

Two distinct modes of forebrain circuit dynamics underlie temporal patterning in the vocalizations of young songbirds

PubMed Central

Aronov, Dmitriy; Veit, Lena; Goldberg, Jesse H.; Fee, Michale S.

2011-01-01

Accurate timing is a critical aspect of motor control, yet the temporal structure of many mature behaviors emerges during learning from highly variable exploratory actions. How does a developing brain acquire the precise control of timing in behavioral sequences? To investigate the development of timing, we analyzed the songs of young juvenile zebra finches. These highly variable vocalizations, akin to human babbling, gradually develop into temporally-stereotyped adult songs. We find that the durations of syllables and silences in juvenile singing are formed by a mixture of two distinct modes of timing – a random mode producing broadly-distributed durations early in development, and a stereotyped mode underlying the gradual emergence of stereotyped durations. Using lesions, inactivations, and localized brain cooling we investigated the roles of neural dynamics within two premotor cortical areas in the production of these temporal modes. We find that LMAN (lateral magnocellular nucleus of the nidopallium) is required specifically for the generation of the random mode of timing, and that mild cooling of LMAN causes an increase in the durations produced by this mode. On the contrary, HVC (used as a proper name) is required specifically for producing the stereotyped mode of timing, and its cooling causes a slowing of all stereotyped components. These results show that two neural pathways contribute to the timing of juvenile songs, and suggest an interesting organization in the forebrain, whereby different brain areas are specialized for the production of distinct forms of neural dynamics. PMID:22072687

A Neural Theory of Visual Attention: Bridging Cognition and Neurophysiology

ERIC Educational Resources Information Center

Bundesen, Claus; Habekost, Thomas; Kyllingsbaek, Soren

2005-01-01

A neural theory of visual attention (NTVA) is presented. NTVA is a neural interpretation of C. Bundesen's (1990) theory of visual attention (TVA). In NTVA, visual processing capacity is distributed across stimuli by dynamic remapping of receptive fields of cortical cells such that more processing resources (cells) are devoted to behaviorally…

The hidden side of drug action: Brain temperature changes induced by neuroactive drugs

PubMed Central

Kiyatkin, Eugene A.

2013-01-01

Rationale Most neuroactive drugs affect brain metabolism as well as systemic and cerebral blood flow, thus altering brain temperature. Although this aspect of drug action usually remains in the shadows, drug-induced alterations in brain temperature reflect their metabolic neural effects and affect neural activity and neural functions. Objectives Here, I review brain temperature changes induced by neuroactive drugs, which are used therapeutically (general anesthetics), as a research tool (dopamine agonists and antagonists), and self-administered to induce desired psychic effects (cocaine, methamphetamine, ecstasy). I consider the mechanisms underlying these temperature fluctuations and their influence on neural, physiological, and behavioral effects of these drugs. Results By interacting with neural mechanisms regulating metabolic activity and heat exchange between the brain and the rest of the body, neuroactive drugs either increase or decrease brain temperatures both within (35-39°C) and exceeding the range of physiological fluctuations. These temperature effects differ drastically depending upon the environmental conditions and activity state during drug administration. This state-dependence is especially important for drugs of abuse that are usually taken by humans during psycho-physiological activation and in environments that prevent proper heat dissipation from the brain. Under these conditions, amphetamine-like stimulants induce pathological brain hyperthermia (>40°C) associated with leakage of the blood-brain barrier and structural abnormalities of brain cells. Conclusions The knowledge on brain temperature fluctuations induced by neuroactive drugs provides new information to understand how they influence metabolic neural activity, why their effects depend upon the behavioral context of administration, and the mechanisms underlying adverse drug effects including neurotoxicity PMID:23274506

An ultra low-power CMOS automatic action potential detector.

PubMed

Gosselin, Benoit; Sawan, Mohamad

2009-08-01

We present a low-power complementary metal-oxide semiconductor (CMOS) analog integrated biopotential detector intended for neural recording in wireless multichannel implants. The proposed detector can achieve accurate automatic discrimination of action potential (APs) from the background activity by means of an energy-based preprocessor and a linear delay element. This strategy improves detected waveforms integrity and prompts for better performance in neural prostheses. The delay element is implemented with a low-power continuous-time filter using a ninth-order equiripple allpass transfer function. All circuit building blocks use subthreshold OTAs employing dedicated circuit techniques for achieving ultra low-power and high dynamic range. The proposed circuit function in the submicrowatt range as the implemented CMOS 0.18- microm chip dissipates 780 nW, and it features a size of 0.07 mm(2). So it is suitable for massive integration in a multichannel device with modest overhead. The fabricated detector succeeds to automatically detect APs from underlying background activity. Testbench validation results obtained with synthetic neural waveforms are presented.

Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation.

PubMed

Balasubramanian, Karthikeyan; Southerland, Joshua; Vaidya, Mukta; Qian, Kai; Eleryan, Ahmed; Fagg, Andrew H; Sluzky, Marc; Oweiss, Karim; Hatsopoulos, Nicholas

2013-01-01

Operant conditioning with biofeedback has been shown to be an effective method to modify neural activity to generate goal-directed actions in a brain-machine interface. It is particularly useful when neural activity cannot be mathematically mapped to motor actions of the actual body such as in the case of amputation. Here, we implement an operant conditioning approach with visual feedback in which an amputated monkey is trained to control a multiple degree-of-freedom robot to perform a reach-to-grasp behavior. A key innovation is that each controlled dimension represents a behaviorally relevant synergy among a set of joint degrees-of-freedom. We present a number of behavioral metrics by which to assess improvements in BMI control with exposure to the system. The use of non-human primates with chronic amputation is arguably the most clinically-relevant model of human amputation that could have direct implications for developing a neural prosthesis to treat humans with missing upper limbs.

Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

NASA Astrophysics Data System (ADS)

Li, Zhaoyu; Xu, Rui; Cui, Pingyuan; Zhu, Shengying

2018-04-01

The ability to plan and react fast in dynamic space environments is central to intelligent behavior of spacecraft. For space and robotic applications, many planners have been used. But it is difficult to encode the domain knowledge and directly use existing techniques such as heuristic to improve the performance of the application systems. Therefore, regarding planning as an advanced control problem, this paper first proposes an autonomous mission planning and action selection mechanism through a multiple layer perceptron neural network approach to select actions in planning process and improve efficiency. To prove the availability and effectiveness, we use autonomous mission planning problems of the spacecraft, which is a sophisticated system with complex subsystems and constraints as an example. Simulation results have shown that artificial neural networks (ANNs) are usable for planning problems. Compared with the existing planning method in EUROPA, the mechanism using ANNs is more efficient and can guarantee stable performance. Therefore, the mechanism proposed in this paper is more suitable for planning problems of spacecraft that require real time and stability.

Context and hand posture modulate the neural dynamics of tool-object perception.

PubMed

Natraj, Nikhilesh; Poole, Victoria; Mizelle, J C; Flumini, Andrea; Borghi, Anna M; Wheaton, Lewis A

2013-02-01

Prior research has linked visual perception of tools with plausible motor strategies. Thus, observing a tool activates the putative action-stream, including the left posterior parietal cortex. Observing a hand functionally grasping a tool involves the inferior frontal cortex. However, tool-use movements are performed in a contextual and grasp specific manner, rather than relative isolation. Our prior behavioral data has demonstrated that the context of tool-use (by pairing the tool with different objects) and varying hand grasp postures of the tool can interact to modulate subjects' reaction times while evaluating tool-object content. Specifically, perceptual judgment was delayed in the evaluation of functional tool-object pairings (Correct context) when the tool was non-functionally (Manipulative) grasped. Here, we hypothesized that this behavioral interference seen with the Manipulative posture would be due to increased and extended left parietofrontal activity possibly underlying motor simulations when resolving action conflict due to this particular grasp at time scales relevant to the behavioral data. Further, we hypothesized that this neural effect will be restricted to the Correct tool-object context wherein action affordances are at a maximum. 64-channel electroencephalography (EEG) was recorded from 16 right-handed subjects while viewing images depicting three classes of tool-object contexts: functionally Correct (e.g. coffee pot-coffee mug), functionally Incorrect (e.g. coffee pot-marker) and Spatial (coffee pot-milk). The Spatial context pairs a tool and object that would not functionally match, but may commonly appear in the same scene. These three contexts were modified by hand interaction: No Hand, Static Hand near the tool, Functional Hand posture and Manipulative Hand posture. The Manipulative posture is convenient for relocating a tool but does not afford a functional engagement of the tool on the target object. Subjects were instructed to visually assess whether the pictures displayed correct tool-object associations. EEG data was analyzed in time-voltage and time-frequency domains. Overall, Static Hand, Functional and Manipulative postures cause early activation (100-400ms post image onset) of parietofrontal areas, to varying intensity in each context, when compared to the No Hand control condition. However, when context is Correct, only the Manipulative Posture significantly induces extended neural responses, predominantly over right parietal and right frontal areas [400-600ms post image onset]. Significant power increase was observed in the theta band [4-8Hz] over the right frontal area, [0-500ms]. In addition, when context is Spatial, Manipulative posture alone significantly induces extended neural responses, over bilateral parietofrontal and left motor areas [400-600ms]. Significant power decrease occurred primarily in beta bands [12-16, 20-25Hz] over the aforementioned brain areas [400-600ms]. Here, we demonstrate that the neural processing of tool-object perception is sensitive to several factors. While both Functional and Manipulative postures in Correct context engage predominantly an early left parietofrontal circuit, the Manipulative posture alone extends the neural response and transitions to a late right parietofrontal network. This suggests engagement of a right neural system to evaluate action affordances when hand posture does not support action (Manipulative). Additionally, when tool-use context is ambiguous (Spatial context), there is increased bilateral parietofrontal activation and, extended neural response for the Manipulative posture. These results point to the existence of other networks evaluating tool-object associations when motoric affordances are not readily apparent and underlie corresponding delayed perceptual judgment in our prior behavioral data wherein Manipulative postures had exclusively interfered in judging tool-object content. Copyright © 2012 Elsevier Ltd. All rights reserved.

Neural substrates of interpreting actions and emotions from body postures.

PubMed

Kana, Rajesh K; Travers, Brittany G

2012-04-01

Accurately reading the body language of others may be vital for navigating the social world, and this ability may be influenced by factors, such as our gender, personality characteristics and neurocognitive processes. This fMRI study examined the brain activation of 26 healthy individuals (14 women and 12 men) while they judged the action performed or the emotion felt by stick figure characters appearing in different postures. In both tasks, participants activated areas associated with visual representation of the body, motion processing and emotion recognition. Behaviorally, participants demonstrated greater ease in judging the physical actions of the characters compared to judging their emotional states, and participants showed more activation in areas associated with emotion processing in the emotion detection task, whereas they showed more activation in visual, spatial and action-related areas in the physical action task. Gender differences emerged in brain responses, such that men showed greater activation than women in the left dorsal premotor cortex in both tasks. Finally, participants higher in self-reported empathy demonstrated greater activation in areas associated with self-referential processing and emotion interpretation. These results suggest that empathy levels and sex of the participant may affect neural responses to emotional body language.

Signed language and human action processing: evidence for functional constraints on the human mirror-neuron system.

PubMed

Corina, David P; Knapp, Heather Patterson

2008-12-01

In the quest to further understand the neural underpinning of human communication, researchers have turned to studies of naturally occurring signed languages used in Deaf communities. The comparison of the commonalities and differences between spoken and signed languages provides an opportunity to determine core neural systems responsible for linguistic communication independent of the modality in which a language is expressed. The present article examines such studies, and in addition asks what we can learn about human languages by contrasting formal visual-gestural linguistic systems (signed languages) with more general human action perception. To understand visual language perception, it is important to distinguish the demands of general human motion processing from the highly task-dependent demands associated with extracting linguistic meaning from arbitrary, conventionalized gestures. This endeavor is particularly important because theorists have suggested close homologies between perception and production of actions and functions of human language and social communication. We review recent behavioral, functional imaging, and neuropsychological studies that explore dissociations between the processing of human actions and signed languages. These data suggest incomplete overlap between the mirror-neuron systems proposed to mediate human action and language.

Neural substrates of interpreting actions and emotions from body postures

PubMed Central

Travers, Brittany G.

2012-01-01

Accurately reading the body language of others may be vital for navigating the social world, and this ability may be influenced by factors, such as our gender, personality characteristics and neurocognitive processes. This fMRI study examined the brain activation of 26 healthy individuals (14 women and 12 men) while they judged the action performed or the emotion felt by stick figure characters appearing in different postures. In both tasks, participants activated areas associated with visual representation of the body, motion processing and emotion recognition. Behaviorally, participants demonstrated greater ease in judging the physical actions of the characters compared to judging their emotional states, and participants showed more activation in areas associated with emotion processing in the emotion detection task, whereas they showed more activation in visual, spatial and action-related areas in the physical action task. Gender differences emerged in brain responses, such that men showed greater activation than women in the left dorsal premotor cortex in both tasks. Finally, participants higher in self-reported empathy demonstrated greater activation in areas associated with self-referential processing and emotion interpretation. These results suggest that empathy levels and sex of the participant may affect neural responses to emotional body language. PMID:21504992

Subthalamic nucleus stimulation influences expression and suppression of impulsive behaviour in Parkinson’s disease

PubMed Central

Ridderinkhof, K. Richard; Elias, William J.; Frysinger, Robert C.; Bashore, Theodore R.; Downs, Kara E.; van Wouwe, Nelleke C.; van den Wildenberg, Wery P. M.

2010-01-01

Past studies show beneficial as well as detrimental effects of subthalamic nucleus deep-brain stimulation on impulsive behaviour. We address this paradox by investigating individuals with Parkinson’s disease treated with subthalamic nucleus stimulation (n = 17) and healthy controls without Parkinson’s disease (n = 17) on performance in a Simon task. In this reaction time task, conflict between premature response impulses and goal-directed action selection is manipulated. We applied distributional analytic methods to separate the strength of the initial response impulse from the proficiency of inhibitory control engaged subsequently to suppress the impulse. Patients with Parkinson’s disease were tested when stimulation was either turned on or off. Mean conflict interference effects did not differ between controls and patients, or within patients when stimulation was on versus off. In contrast, distributional analyses revealed two dissociable effects of subthalamic nucleus stimulation. Fast response errors indicated that stimulation increased impulsive, premature responding in high conflict situations. Later in the reaction process, however, stimulation improved the proficiency with which inhibitory control was engaged to suppress these impulses selectively, thereby facilitating selection of the correct action. This temporal dissociation supports a conceptual framework for resolving past paradoxical findings and further highlights that dynamic aspects of impulse and inhibitory control underlying goal-directed behaviour rely in part on neural circuitry inclusive of the subthalamic nucleus. PMID:20861152

Children's neural processing of moral scenarios provides insight into the formation and reduction of in-group biases.

PubMed

Meidenbauer, Kimberly L; Cowell, Jason M; Decety, Jean

2018-04-25

Survival is dependent on sociality within groups which ensure sustenance and protection. From an early age, children show a natural tendency to sort people into groups and discriminate among them. The computations guiding evaluation of third-party behaviors are complex, requiring integration of intent, consequences, and knowledge of group affiliation. This study examined how perceiving third-party morally laden behavior influences children's likelihood to exhibit or reduce group bias. Following a minimal group paradigm assignment, young children (4-7 years) performed a moral evaluation task where group affiliations and moral actions were systematically juxtaposed, so that they were exposed to disproportionately antisocial in-group and prosocial out-group scenarios. Electroencephalography was recorded, and group preference was assessed with a resource allocation game before and after the EEG session. Across all children, evaluations of others' moral actions arose from early and automatic processing (~150 ms), followed by later interactive processing of affiliation and moral valence (~500 ms). Importantly, individual differences in bias manifestation and attitude change were predicted by children's neural responses. Children with high baseline bias selectively exhibited a rapid detection (~200 ms) of scenarios inconsistent with their bias (in-group harm and out-group help). Changes in bias corresponded to distinct patterns in longer latency neural processing. These new developmental neuroscience findings elucidate the multifaceted processing involved in moral evaluation of others' actions, their group affiliations, the nature of the integration of both into full judgments, and the relation of individual differences in neural responses to social decision-making in childhood. © 2018 John Wiley & Sons Ltd.

Goal-Directed Behavior and Instrumental Devaluation: A Neural System-Level Computational Model

PubMed Central

Mannella, Francesco; Mirolli, Marco; Baldassarre, Gianluca

2016-01-01

Devaluation is the key experimental paradigm used to demonstrate the presence of instrumental behaviors guided by goals in mammals. We propose a neural system-level computational model to address the question of which brain mechanisms allow the current value of rewards to control instrumental actions. The model pivots on and shows the computational soundness of the hypothesis for which the internal representation of instrumental manipulanda (e.g., levers) activate the representation of rewards (or “action-outcomes”, e.g., foods) while attributing to them a value which depends on the current internal state of the animal (e.g., satiation for some but not all foods). The model also proposes an initial hypothesis of the integrated system of key brain components supporting this process and allowing the recalled outcomes to bias action selection: (a) the sub-system formed by the basolateral amygdala and insular cortex acquiring the manipulanda-outcomes associations and attributing the current value to the outcomes; (b) three basal ganglia-cortical loops selecting respectively goals, associative sensory representations, and actions; (c) the cortico-cortical and striato-nigro-striatal neural pathways supporting the selection, and selection learning, of actions based on habits and goals. The model reproduces and explains the results of several devaluation experiments carried out with control rats and rats with pre- and post-training lesions of the basolateral amygdala, the nucleus accumbens core, the prelimbic cortex, and the dorso-medial striatum. The results support the soundness of the hypotheses of the model and show its capacity to integrate, at the system-level, the operations of the key brain structures underlying devaluation. Based on its hypotheses and predictions, the model also represents an operational framework to support the design and analysis of new experiments on the motivational aspects of goal-directed behavior. PMID:27803652

Cortico-basal ganglia networks subserving goal-directed behavior mediated by conditional visuo-goal association

PubMed Central

Hoshi, Eiji

2013-01-01

Action is often executed according to information provided by a visual signal. As this type of behavior integrates two distinct neural representations, perception and action, it has been thought that identification of the neural mechanisms underlying this process will yield deeper insights into the principles underpinning goal-directed behavior. Based on a framework derived from conditional visuomotor association, prior studies have identified neural mechanisms in the dorsal premotor cortex (PMd), dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), and basal ganglia (BG). However, applications resting solely on this conceptualization encounter problems related to generalization and flexibility, essential processes in executive function, because the association mode involves a direct one-to-one mapping of each visual signal onto a particular action. To overcome this problem, we extend this conceptualization and postulate a more general framework, conditional visuo-goal association. According to this new framework, the visual signal identifies an abstract behavioral goal, and an action is subsequently selected and executed to meet this goal. Neuronal activity recorded from the four key areas of the brains of monkeys performing a task involving conditional visuo-goal association revealed three major mechanisms underlying this process. First, visual-object signals are represented primarily in the vlPFC and BG. Second, all four areas are involved in initially determining the goals based on the visual signals, with the PMd and dlPFC playing major roles in maintaining the salience of the goals. Third, the cortical areas play major roles in specifying action, whereas the role of the BG in this process is restrictive. These new lines of evidence reveal that the four areas involved in conditional visuomotor association contribute to goal-directed behavior mediated by conditional visuo-goal association in an area-dependent manner. PMID:24155692

Neural correlates of cognitive dissonance and choice-induced preference change

PubMed Central

Izuma, Keise; Matsumoto, Madoka; Murayama, Kou; Samejima, Kazuyuki; Sadato, Norihiro; Matsumoto, Kenji

2010-01-01

According to many modern economic theories, actions simply reflect an individual's preferences, whereas a psychological phenomenon called “cognitive dissonance” claims that actions can also create preference. Cognitive dissonance theory states that after making a difficult choice between two equally preferred items, the act of rejecting a favorite item induces an uncomfortable feeling (cognitive dissonance), which in turn motivates individuals to change their preferences to match their prior decision (i.e., reducing preference for rejected items). Recently, however, Chen and Risen [Chen K, Risen J (2010) J Pers Soc Psychol 99:573–594] pointed out a serious methodological problem, which casts a doubt on the very existence of this choice-induced preference change as studied over the past 50 y. Here, using a proper control condition and two measures of preferences (self-report and brain activity), we found that the mere act of making a choice can change self-report preference as well as its neural representation (i.e., striatum activity), thus providing strong evidence for choice-induced preference change. Furthermore, our data indicate that the anterior cingulate cortex and dorsolateral prefrontal cortex tracked the degree of cognitive dissonance on a trial-by-trial basis. Our findings provide important insights into the neural basis of how actions can alter an individual's preferences. PMID:21135218

Distinct dynamical patterns that distinguish willed and forced actions.

PubMed

Garcia Dominguez, Luis; Kostelecki, Wojciech; Wennberg, Richard; Perez Velazquez, Jose L

2011-03-01

The neural pathways for generating willed actions have been increasingly investigated since the famous pioneering work by Benjamin Libet on the nature of free will. To better understand what differentiates the brain states underlying willed and forced behaviours, we performed a study of chosen and forced actions over a binary choice scenario. Magnetoencephalography recordings were obtained from six subjects during a simple task in which the subject presses a button with the left or right finger in response to a cue that either (1) specifies the finger with which the button should be pressed or (2) instructs the subject to press a button with a finger of their own choosing. Three independent analyses were performed to investigate the dynamical patterns of neural activity supporting willed and forced behaviours during the preparatory period preceding a button press. Each analysis offered similar findings in the temporal and spatial domains and in particular, a high accuracy in the classification of single trials was obtained around 200 ms after cue presentation with an overall average of 82%. During this period, the majority of the discriminatory power comes from differential neural processes observed bilaterally in the parietal lobes, as well as some differences in occipital and temporal lobes, suggesting a contribution of these regions to willed and forced behaviours.

Neural correlates of cognitive dissonance and choice-induced preference change.

PubMed

Izuma, Keise; Matsumoto, Madoka; Murayama, Kou; Samejima, Kazuyuki; Sadato, Norihiro; Matsumoto, Kenji

2010-12-21

According to many modern economic theories, actions simply reflect an individual's preferences, whereas a psychological phenomenon called "cognitive dissonance" claims that actions can also create preference. Cognitive dissonance theory states that after making a difficult choice between two equally preferred items, the act of rejecting a favorite item induces an uncomfortable feeling (cognitive dissonance), which in turn motivates individuals to change their preferences to match their prior decision (i.e., reducing preference for rejected items). Recently, however, Chen and Risen [Chen K, Risen J (2010) J Pers Soc Psychol 99:573-594] pointed out a serious methodological problem, which casts a doubt on the very existence of this choice-induced preference change as studied over the past 50 y. Here, using a proper control condition and two measures of preferences (self-report and brain activity), we found that the mere act of making a choice can change self-report preference as well as its neural representation (i.e., striatum activity), thus providing strong evidence for choice-induced preference change. Furthermore, our data indicate that the anterior cingulate cortex and dorsolateral prefrontal cortex tracked the degree of cognitive dissonance on a trial-by-trial basis. Our findings provide important insights into the neural basis of how actions can alter an individual's preferences.

Angle of Arrival Detection Through Artificial Neural Network Analysis of Optical Fiber Intensity Patterns

DTIC Science & Technology

1990-12-01

ARTIFICIAL NEURAL NETWORK ANALYSIS OF OPTICAL FIBER INTENSITY PATTERNS THESIS Scott Thomas Captain, USAF AFIT/GE/ENG/90D-62 DTIC...ELECTE ao • JAN08 1991 Approved for public release; distribution unlimited. AFIT/GE/ENG/90D-62 ANGLE OF ARRIVAL DETECTION THROUGH ARTIFICIAL NEURAL NETWORK ANALYSIS... ARTIFICIAL NEURAL NETWORK ANALYSIS OF OPTICAL FIBER INTENSITY PATTERNS L Introduction The optical sensors of United States Air Force reconnaissance

Understanding Return on Investment for Data Center Consolidation

DTIC Science & Technology

2013-09-01

Channel over Ethernet FDCCI Federal Data Center Consolidation Initiative GAO Government Accountability Office GDA Government Directed Actions GIG ...to judge how each stakeholder group will benefit from it. Such measures as lower risk, greater control, better economies of scale, better utilization...NMS product by Kratos Networks called Neural Star to manage the Global Information Grid ( GIG ) (Kratos, 2013). DISA uses Neural Star as the primary

Translational studies of goal-directed action as a framework for classifying deficits across psychiatric disorders

PubMed Central

Griffiths, Kristi R.; Morris, Richard W.; Balleine, Bernard W.

2014-01-01

The ability to learn contingencies between actions and outcomes in a dynamic environment is critical for flexible, adaptive behavior. Goal-directed actions adapt to changes in action-outcome contingencies as well as to changes in the reward-value of the outcome. When networks involved in reward processing and contingency learning are maladaptive, this fundamental ability can be lost, with detrimental consequences for decision-making. Impaired decision-making is a core feature in a number of psychiatric disorders, ranging from depression to schizophrenia. The argument can be developed, therefore, that seemingly disparate symptoms across psychiatric disorders can be explained by dysfunction within common decision-making circuitry. From this perspective, gaining a better understanding of the neural processes involved in goal-directed action, will allow a comparison of deficits observed across traditional diagnostic boundaries within a unified theoretical framework. This review describes the key processes and neural circuits involved in goal-directed decision-making using evidence from animal studies and human neuroimaging. Select studies are discussed to outline what we currently know about causal judgments regarding actions and their consequences, action-related reward evaluation, and, most importantly, how these processes are integrated in goal-directed learning and performance. Finally, we look at how adaptive decision-making is impaired across a range of psychiatric disorders and how deepening our understanding of this circuitry may offer insights into phenotypes and more targeted interventions. PMID:24904322

Cocaine action on peripheral, non-monoamine neural substrates as a trigger of EEG desynchronization and EMG activation following intravenous administration in freely moving rats

PubMed Central

Smirnov, Michael S.; Kiyatkin, Eugene A.

2009-01-01

Many important physiological, behavioral and subjective effects of intravenous (iv) cocaine (COC) are exceptionally rapid and transient, suggesting a possible involvement of peripheral neural substrates in their triggering. In the present study, we used high-speed EEG and EMG recordings (4-s resolution) in freely moving rats to characterize the central electrophysiological effects of iv COC at low doses within a self-administration range (0.25-1.0 mg/kg). We found that COC induces rapid, strong, and prolonged desynchronization of cortical EEG (decrease in alpha and increase in beta and gamma activity) and activation of the neck EMG that begin within 2-6 s following the start of a 10-s injection; immediate components of both effects were dose-independent. The rapid effects of COC were mimicked by iv COC methiodide, a derivative that cannot cross the blood-brain barrier. At equimolar doses (0.33-1.33 mg/kg), COC methiodide had equally fast and strong effects on EEG and EMG total powers, decreasing alpha and increasing beta and gamma activities. Rapid EEG desynchronization and EMG activation was also induced by iv procaine, a structurally similar, short-acting local anesthetic with virtually no effects on monoamine uptake; at equipotential doses (1.25-5.0 mg/kg), these effects were weaker and shorter in duration than those of COC. Surprisingly, iv saline injection delivered during slow-wave sleep (but not during quiet wakefulness) also induced a transient EEG desynchronization but without changes in EMG and motor activity; these effects were significantly weaker and much shorter than those induced by all tested drugs. These data suggest that in awake animals, iv COC induces rapid cortical activation and a subsequent motor response via its action on peripheral non-monoamine neural elements, involving neural transmission via visceral sensory pathways. By providing a rapid neural signal and triggering neural activation, such an action might play a crucial role in the sensory effects of COC, thus contributing to the learning and development of drug-taking behavior. PMID:19861149

Cocaine action on peripheral, non-monoamine neural substrates as a trigger of electroencephalographic desynchronization and electromyographic activation following i.v. administration in freely moving rats.

PubMed

Smirnov, M S; Kiyatkin, E A

2010-01-20

Many important physiological, behavioral and subjective effects of i.v. cocaine (COC) are exceptionally rapid and transient, suggesting a possible involvement of peripheral neural substrates in their triggering. In the present study, we used high-speed electroencephalographic (EEG) and electromyographic (EMG) recordings (4-s resolution) in freely moving rats to characterize the central electrophysiological effects of i.v. COC at low doses within a self-administration range (0.25-1.0 mg/kg). We found that COC induces rapid, strong, and prolonged desynchronization of cortical EEG (decrease in alpha and increase in beta and gamma activity) and activation of the neck EMG that begin within 2-6 s following the start of a 10-s injection; immediate components of both effects were dose-independent. The rapid effects of COC were mimicked by i.v. COC methiodide (COC-MET), a derivative that cannot cross the blood-brain barrier. At equimolar doses (0.33-1.33 mg/kg), COC-MET had equally fast and strong effects on EEG and EMG total powers, decreasing alpha and increasing beta and gamma activities. Rapid EEG desynchronization and EMG activation was also induced by i.v. procaine, a structurally similar, short-acting local anesthetic with virtually no effects on monoamine uptake; at equipotential doses (1.25-5.0 mg/kg), these effects were weaker and shorter in duration than those of COC. Surprisingly, i.v. saline injection delivered during slow-wave sleep (but not during quiet wakefulness) also induced a transient EEG desynchronization but without changes in EMG and motor activity; these effects were significantly weaker and much shorter than those induced by all tested drugs. These data suggest that in awake animals, i.v. COC induces rapid cortical activation and a subsequent motor response via its action on peripheral non-monoamine neural elements, involving neural transmission via visceral sensory pathways. By providing a rapid neural signal and triggering neural activation, such an action might play a crucial role in the sensory effects of COC, thus contributing to the learning and development of drug-taking behavior.

Action observation has a positive impact on rehabilitation of motor deficits after stroke.

PubMed

Ertelt, Denis; Small, Steven; Solodkin, Ana; Dettmers, Christian; McNamara, Adam; Binkofski, Ferdinand; Buccino, Giovanni

2007-01-01

Evidence exists that the observation of actions activates the same cortical motor areas that are involved in the performance of the observed actions. The neural substrate for this is the mirror neuron system. We harness this neuronal system and its ability to re-enact stored motor representations as a means for rehabilitating motor control. We combined observation of daily actions with concomitant physical training of the observed actions in a new neurorehabilitative program (action observation therapy). Eight stroke patients with moderate, chronic motor deficit of the upper limb as a consequence of medial artery infarction participated. A significant improvement of motor functions in the course of a 4-week treatment, as compared to the stable pre-treatment baseline, and compared with a control group have been found. The improvement lasted for at least 8 weeks after the end of the intervention. Additionally, the effects of action observation therapy on the reorganization of the motor system were investigated by functional magnetic resonance imaging (fMRI), using an independent sensorimotor task consisting of object manipulation. The direct comparison of neural activations between experimental and control groups after training with those elicited by the same task before training yielded a significant rise in activity in the bilateral ventral premotor cortex, bilateral superior temporal gyrus, the supplementary motor area (SMA) and the contralateral supramarginal gyrus. Our results provide pieces of evidence that action observation has a positive additional impact on recovery of motor functions after stroke by reactivation of motor areas, which contain the action observation/action execution matching system.

Neural network and its application to CT imaging

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

Nikravesh, M.; Kovscek, A.R.; Patzek, T.W.

We present an integrated approach to imaging the progress of air displacement by spontaneous imbibition of oil into sandstone. We combine Computerized Tomography (CT) scanning and neural network image processing. The main aspects of our approach are (I) visualization of the distribution of oil and air saturation by CT, (II) interpretation of CT scans using neural networks, and (III) reconstruction of 3-D images of oil saturation from the CT scans with a neural network model. Excellent agreement between the actual images and the neural network predictions is found.

Neural evidence for the intrinsic value of action as motivation for behavior.

PubMed

Miura, Naoki; Tanabe, Hiroki C; Sasaki, Akihiro T; Harada, Tokiko; Sadato, Norihiro

2017-06-03

The intrinsic value of an action refers to the inherent sense that experiencing a behavior is enjoyable even if it has no explicit outcome. Previous research has suggested that a common valuation mechanism within the reward network may be responsible for processing the intrinsic value of achieving both the outcome and external rewards. However, how the intrinsic value of action is neurally represented remains unknown. We hypothesized that the intrinsic value of action is determined by an action-outcome contingency indicating the behavior is controllable and that the outcome of the action can be evaluated by this feedback. Consequently, the reward network should be activated, reflecting the generation of the intrinsic value of action. To test this hypothesis, we conducted a functional magnetic resonance imaging (fMRI) investigation of a stopwatch game in which the action-outcome contingency was manipulated. This experiment involved 36 healthy volunteers and four versions of a stopwatch game that manipulated controllability (the feeling that participants were controlling the stopwatch themselves) and outcome (a signal allowing participants to see the result of their action). A free-choice experiment was administered after the fMRI to explore preference levels for each game. The results showed that the stopwatch game with the action-outcome contingency evoked a greater degree of enjoyment because the participants chose this condition over those that lacked such a contingency. The ventral striatum and midbrain were activated only when action-outcome contingency was present. Thus, the intrinsic value of action was represented by an increase in ventral striatal and midbrain activation. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Neural decoding with kernel-based metric learning.

PubMed

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

2014-06-01

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

Neural Bases of Sequence Processing in Action and Language

ERIC Educational Resources Information Center

Carota, Francesca; Sirigu, Angela

2008-01-01

Real-time estimation of what we will do next is a crucial prerequisite of purposive behavior. During the planning of goal-oriented actions, for instance, the temporal and causal organization of upcoming subsequent moves needs to be predicted based on our knowledge of events. A forward computation of sequential structure is also essential for…

Typical Neural Representations of Action Verbs Develop without Vision

PubMed Central

Caramazza, A.; Pascual-Leone, A.; Saxe, R.

2012-01-01

Many empiricist theories hold that concepts are composed of sensory–motor primitives. For example, the meaning of the word “run” is in part a visual image of running. If action concepts are partly visual, then the concepts of congenitally blind individuals should be altered in that they lack these visual features. We compared semantic judgments and neural activity during action verb comprehension in congenitally blind and sighted individuals. Participants made similarity judgments about pairs of nouns and verbs that varied in the visual motion they conveyed. Blind adults showed the same pattern of similarity judgments as sighted adults. We identified the left middle temporal gyrus (lMTG) brain region that putatively stores visual–motion features relevant to action verbs. The functional profile and location of this region was identical in sighted and congenitally blind individuals. Furthermore, the lMTG was more active for all verbs than nouns, irrespective of visual–motion features. We conclude that the lMTG contains abstract representations of verb meanings rather than visual–motion images. Our data suggest that conceptual brain regions are not altered by the sensory modality of learning. PMID:21653285

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

PubMed

Zhuang, Ning; Zeng, Ying; Yang, Kai; Zhang, Chi; Tong, Li; Yan, Bin

2018-03-12

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods.

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

PubMed Central

Zeng, Ying; Yang, Kai; Tong, Li; Yan, Bin

2018-01-01

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. PMID:29534515

Livermore Big Artificial Neural Network Toolkit

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

Essen, Brian Van; Jacobs, Sam; Kim, Hyojin

2016-07-01

LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

Using goal- and grip-related information for understanding the correctness of other's actions: an ERP study.

PubMed

van Elk, Michiel; Bousardt, Roel; Bekkering, Harold; van Schie, Hein T

2012-01-01

Detecting errors in other's actions is of pivotal importance for joint action, competitive behavior and observational learning. Although many studies have focused on the neural mechanisms involved in detecting low-level errors, relatively little is known about error-detection in everyday situations. The present study aimed to identify the functional and neural mechanisms whereby we understand the correctness of other's actions involving well-known objects (e.g. pouring coffee in a cup). Participants observed action sequences in which the correctness of the object grasped and the grip applied to a pair of objects were independently manipulated. Observation of object violations (e.g. grasping the empty cup instead of the coffee pot) resulted in a stronger P3-effect than observation of grip errors (e.g. grasping the coffee pot at the upper part instead of the handle), likely reflecting a reorienting response, directing attention to the relevant location. Following the P3-effect, a parietal slow wave positivity was observed that persisted for grip-errors, likely reflecting the detection of an incorrect hand-object interaction. These findings provide new insight in the functional significance of the neurophysiological markers associated with the observation of incorrect actions and suggest that the P3-effect and the subsequent parietal slow wave positivity may reflect the detection of errors at different levels in the action hierarchy. Thereby this study elucidates the cognitive processes that support the detection of action violations in the selection of objects and grips.

Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays via RBF neural networks.

PubMed

Yi, Qu; Zhan-ming, Li; Er-chao, Li

2012-11-01

A new fault detection and diagnosis (FDD) problem via the output probability density functions (PDFs) for non-gausian stochastic distribution systems (SDSs) is investigated. The PDFs can be approximated by radial basis functions (RBFs) neural networks. Different from conventional FDD problems, the measured information for FDD is the output stochastic distributions and the stochastic variables involved are not confined to Gaussian ones. A (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. In this work, a nonlinear adaptive observer-based fault detection and diagnosis algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the fault. Stability and Convergency analysis is performed in fault detection and fault diagnosis analysis for the error dynamic system. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Alpha, delta and theta rhythms in a neural net model. Comparison with MEG data.

PubMed

Kotini, A; Anninos, P

2016-01-07

The aim of this study is to provide information regarding the comparison of a neural model to MEG measurements. Our study population consisted of 10 epileptic patients and 10 normal subjects. The epileptic patients had high MEG amplitudes characterized with θ (4-7 Hz) or δ (2-3 Hz) rhythms and absence of α-rhythm (8-13 Hz). The statistical analysis of such activities corresponded to Poisson distribution. Conversely, the MEG from normal subjects had low amplitudes, higher frequencies and presence of α-rhythm (8-13 Hz). Such activities were not synchronized and their distributions were Gauss. These findings were in agreement with our theoretical neural model. The comparison of the neural network with MEG data provides information about the status of brain function in epileptic and normal states. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

PubMed

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

2011-01-01

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

Voluntary control of a phantom limb.

PubMed

Walsh, E; Long, C; Haggard, P

2015-08-01

Voluntary actions are often accompanied by a conscious experience of intention. The content of this experience, and its neural basis, remain controversial. On one view, the mind just retrospectively ascribes intentions to explain the occurrence of actions that lack obvious triggering stimuli. Here, we use EEG frequency analysis of sensorimotor rhythms to investigate brain activity when a participant (CL, co-author of this paper) with congenital absence of the left hand and arm, prepared and made a voluntary action with the right or the phantom "left hand". CL reported the moment she experienced the intention to press a key. This timepoint was then used as a marker for aligning and averaging EEG. In a second condition, CL was asked to prepare the action on all trials, but then, on some trials, to cancel the action at the last moment. For the right hand, we observed a typical reduction in beta-band spectral power prior to movement, followed by beta rebound after movement. When CL prepared but then cancelled a movement, we found a characteristic EEG pattern reported previously, namely a left frontal increase in spectral power close to the time of the perceived intention to move. Interestingly, the same neural signatures of positive and inhibitory volition were also present when CL prepared and inhibited movements with her phantom left hand. These EEG signals were all similar to those reported previously in a group of 14 healthy volunteers. Our results suggest that conscious intention may depend on preparatory brain activity, and not on making, or ever having made, the corresponding physical body movement. Accounts that reduce conscious volition to mere retrospective confabulation cannot easily explain our participant's neurophenomenology of action and inhibition. In contrast, the results are consistent with the view that specific neural events prior to movement may generate conscious experiences of positive and negative volition. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

DTIC Science & Technology

1990-06-01

NAVAL POSTGRADUATE SCHOOL Monterey, California ’-DTIC 0 ELECT f NMARO 5 191 N S, U, THESIS B . AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR...NO. NO. NO ACCESSION NO 11. TITLE (Include Security Classification) AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR SPACECRAFT ATTITUDE STABILIZATION...obsolete a U.S. G v pi.. iim n P.. oiice! toog-eo.5s43 i Approved for public release; distribution is unlimited. AN ARTIFICIAL NEURAL NETWORK CONTROL

Neurale Netwerken en Radarsystemen (Neural Networks and Radar Systems)

DTIC Science & Technology

1989-08-01

general issues in cognitive science", Parallel distributed processing, Vol 1: Foundations, Rumelhart et al. 1986 pp 110-146 THO rapport Pagina 151 36 D.E...34Neural networks (part 2)",Expert Focus, IEEE Expert, Spring 1988. 61 J.A. Anderson, " Cognitive and Psychological Computations with Neural Models", IEEE...Pagina 154 69 David H. Ackley, Geoffrey E. Hinton and Terrence J. Sejnowski, "A Learning Algorithm for Boltzmann machines", cognitive science 9, 147-169

Frequency modulation entrains slow neural oscillations and optimizes human listening behavior

PubMed Central

Henry, Molly J.; Obleser, Jonas

2012-01-01

The human ability to continuously track dynamic environmental stimuli, in particular speech, is proposed to profit from “entrainment” of endogenous neural oscillations, which involves phase reorganization such that “optimal” phase comes into line with temporally expected critical events, resulting in improved processing. The current experiment goes beyond previous work in this domain by addressing two thus far unanswered questions. First, how general is neural entrainment to environmental rhythms: Can neural oscillations be entrained by temporal dynamics of ongoing rhythmic stimuli without abrupt onsets? Second, does neural entrainment optimize performance of the perceptual system: Does human auditory perception benefit from neural phase reorganization? In a human electroencephalography study, listeners detected short gaps distributed uniformly with respect to the phase angle of a 3-Hz frequency-modulated stimulus. Listeners’ ability to detect gaps in the frequency-modulated sound was not uniformly distributed in time, but clustered in certain preferred phases of the modulation. Moreover, the optimal stimulus phase was individually determined by the neural delta oscillation entrained by the stimulus. Finally, delta phase predicted behavior better than stimulus phase or the event-related potential after the gap. This study demonstrates behavioral benefits of phase realignment in response to frequency-modulated auditory stimuli, overall suggesting that frequency fluctuations in natural environmental input provide a pacing signal for endogenous neural oscillations, thereby influencing perceptual processing. PMID:23151506

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network

PubMed Central

You, Hongjian

2018-01-01

Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach. PMID:29364194

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network.

PubMed

An, Quanzhi; Pan, Zongxu; You, Hongjian

2018-01-24

Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach.

Joint probabilities and quantum cognition

NASA Astrophysics Data System (ADS)

de Barros, J. Acacio

2012-12-01

In this paper we discuss the existence of joint probability distributions for quantumlike response computations in the brain. We do so by focusing on a contextual neural-oscillator model shown to reproduce the main features of behavioral stimulus-response theory. We then exhibit a simple example of contextual random variables not having a joint probability distribution, and describe how such variables can be obtained from neural oscillators, but not from a quantum observable algebra.

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback

PubMed Central

2018-01-01

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence of neural fluctuations, across the brain, on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone. PMID:29342146

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback.

PubMed

Buckley, Christopher L; Toyoizumi, Taro

2018-01-01

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence of neural fluctuations, across the brain, on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone.

Microscopic neural image registration based on the structure of mitochondria

NASA Astrophysics Data System (ADS)

Cao, Huiwen; Han, Hua; Rao, Qiang; Xiao, Chi; Chen, Xi

2017-02-01

Microscopic image registration is a key component of the neural structure reconstruction with serial sections of neural tissue. The goal of microscopic neural image registration is to recover the 3D continuity and geometrical properties of specimen. During image registration, various distortions need to be corrected, including image rotation, translation, tissue deformation et.al, which come from the procedure of sample cutting, staining and imaging. Furthermore, there is only certain similarity between adjacent sections, and the degree of similarity depends on local structure of the tissue and the thickness of the sections. These factors make the microscopic neural image registration a challenging problem. To tackle the difficulty of corresponding landmarks extraction, we introduce a novel image registration method for Scanning Electron Microscopy (SEM) images of serial neural tissue sections based on the structure of mitochondria. The ellipsoidal shape of mitochondria ensures that the same mitochondria has similar shape between adjacent sections, and its characteristic of broad distribution in the neural tissue guarantees that landmarks based on the mitochondria distributed widely in the image. The proposed image registration method contains three parts: landmarks extraction between adjacent sections, corresponding landmarks matching and image deformation based on the correspondences. We demonstrate the performance of our method with SEM images of drosophila brain.

Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox

PubMed Central

Marshall, Najja; Timme, Nicholas M.; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M.

2016-01-01

Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of “neural avalanches” (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods—power-law fitting, avalanche shape collapse, and neural complexity—have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox. PMID:27445842

Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox.

PubMed

Marshall, Najja; Timme, Nicholas M; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M

2016-01-01

Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of "neural avalanches" (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods-power-law fitting, avalanche shape collapse, and neural complexity-have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox.

Machine learning action parameters in lattice quantum chromodynamics

NASA Astrophysics Data System (ADS)

Shanahan, Phiala E.; Trewartha, Daniel; Detmold, William

2018-05-01

Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. The high information content and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.

A spiking neural network based on the basal ganglia functional anatomy.

PubMed

Baladron, Javier; Hamker, Fred H

2015-07-01

We introduce a spiking neural network of the basal ganglia capable of learning stimulus-action associations. We model learning in the three major basal ganglia pathways, direct, indirect and hyperdirect, by spike time dependent learning and considering the amount of dopamine available (reward). Moreover, we allow to learn a cortico-thalamic pathway that bypasses the basal ganglia. As a result the system develops new functionalities for the different basal ganglia pathways: The direct pathway selects actions by disinhibiting the thalamus, the hyperdirect one suppresses alternatives and the indirect pathway learns to inhibit common mistakes. Numerical experiments show that the system is capable of learning sets of either deterministic or stochastic rules. Copyright © 2015 Elsevier Ltd. All rights reserved.

You Can't Think and Hit at the Same Time: Neural Correlates of Baseball Pitch Classification.

PubMed

Sherwin, Jason; Muraskin, Jordan; Sajda, Paul

2012-01-01

Hitting a baseball is often described as the most difficult thing to do in sports. A key aptitude of a good hitter is the ability to determine which pitch is coming. This rapid decision requires the batter to make a judgment in a fraction of a second based largely on the trajectory and spin of the ball. When does this decision occur relative to the ball's trajectory and is it possible to identify neural correlates that represent how the decision evolves over a split second? Using single-trial analysis of electroencephalography (EEG) we address this question within the context of subjects discriminating three types of pitches (fastball, curveball, slider) based on pitch trajectories. We find clear neural signatures of pitch classification and, using signal detection theory, we identify the times of discrimination on a trial-to-trial basis. Based on these neural signatures we estimate neural discrimination distributions as a function of the distance the ball is from the plate. We find all three pitches yield unique distributions, namely the timing of the discriminating neural signatures relative to the position of the ball in its trajectory. For instance, fastballs are discriminated at the earliest points in their trajectory, relative to the two other pitches, which is consistent with the need for some constant time to generate and execute the motor plan for the swing (or inhibition of the swing). We also find incorrect discrimination of a pitch (errors) yields neural sources in Brodmann Area 10, which has been implicated in prospective memory, recall, and task difficulty. In summary, we show that single-trial analysis of EEG yields informative distributions of the relative point in a baseball's trajectory when the batter makes a decision on which pitch is coming.

Kernel Temporal Differences for Neural Decoding

PubMed Central

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Concepts in context: Processing mental state concepts with internal or external focus involves different neural systems

PubMed Central

Oosterwijk, Suzanne; Mackey, Scott; Wilson-Mendenhall, Christine; Winkielman, Piotr; Paulus, Martin P.

2015-01-01

According to embodied cognition theories concepts are contextually-situated and grounded in neural systems that produce experiential states. This view predicts that processing mental state concepts recruits neural regions associated with different aspects of experience depending on the context in which people understand a concept. This neuroimaging study tested this prediction using a set of sentences that described emotional (e.g., fear, joy) and non-emotional (e.g., thinking, hunger) mental states with internal focus (i.e. focusing on bodily sensations and introspection) or external focus (i.e. focusing on expression and action). Consistent with our predictions, data suggested that the inferior frontal gyrus, a region associated with action representation, was engaged more by external than internal sentences. By contrast, the ventromedial prefrontal cortex, a region associated with the generation of internal states, was engaged more by internal emotion sentences than external sentence categories. Similar patterns emerged when we examined the relationship between neural activity and independent ratings of sentence focus. Furthermore, ratings of emotion were associated with activation in the medial prefrontal cortex, whereas ratings of activity were associated with activation in the inferior frontal gyrus. These results suggest that mental state concepts are represented in a dynamic way, using context-relevant interoceptive and sensorimotor resources. PMID:25748274

Automated implementation of rule-based expert systems with neural networks for time-critical applications

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.; Huang, Song; Govind, Girish

1991-01-01

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

Neural differences between intrinsic reasons for doing versus extrinsic reasons for doing: an fMRI study.

PubMed

Lee, Woogul; Reeve, Johnmarshall; Xue, Yiqun; Xiong, Jinhu

2012-05-01

The contemporary neural understanding of motivation is based almost exclusively on the neural mechanisms of incentive motivation. Recognizing this as a limitation, we used event-related functional magnetic resonance imaging (fMRI) to pursue the viability of expanding the neural understanding of motivation by initiating a pioneering study of intrinsic motivation by scanning participants' neural activity when they decided to act for intrinsic reasons versus when they decided to act for extrinsic reasons. As expected, intrinsic reasons for acting more recruited insular cortex activity while extrinsic reasons for acting more recruited posterior cingulate cortex (PCC) activity. The results demonstrate that engagement decisions based on intrinsic motivation are more determined by weighing the presence of spontaneous self-satisfactions such as interest and enjoyment while engagement decisions based on extrinsic motivation are more determined by weighing socially-acquired stored values as to whether the environmental incentive is attractive enough to warrant action.

Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems.

PubMed

Zumsteg, Zachary S; Kemere, Caleb; O'Driscoll, Stephen; Santhanam, Gopal; Ahmed, Rizwan E; Shenoy, Krishna V; Meng, Teresa H

2005-09-01

A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that state-of-the-art spike sorting algorithms are not only feasible using modern complementary metal oxide semiconductor very large scale integration processes, but may represent the best option for extracting large amounts of data in implantable neural prosthetic interfaces.

Early changes in emotional processing as a marker of clinical response to SSRI treatment in depression.

PubMed

Godlewska, B R; Browning, M; Norbury, R; Cowen, P J; Harmer, C J

2016-11-22

Antidepressant treatment reduces behavioural and neural markers of negative emotional bias early in treatment and has been proposed as a mechanism of antidepressant drug action. Here, we provide a critical test of this hypothesis by assessing whether neural markers of early emotional processing changes predict later clinical response in depression. Thirty-five unmedicated patients with major depression took the selective serotonin re-uptake inhibitor (SSRI), escitalopram (10 mg), over 6 weeks, and were classified as responders (22 patients) versus non-responders (13 patients), based on at least a 50% reduction in symptoms by the end of treatment. The neural response to fearful and happy emotional facial expressions was assessed before and after 7 days of treatment using functional magnetic resonance imaging. Changes in the neural response to these facial cues after 7 days of escitalopram were compared in patients as a function of later clinical response. A sample of healthy controls was also assessed. At baseline, depressed patients showed greater activation to fear versus happy faces than controls in the insula and dorsal anterior cingulate. Depressed patients who went on to respond to the SSRI had a greater reduction in neural activity to fearful versus happy facial expressions after just 7 days of escitalopram across a network of regions including the anterior cingulate, insula, amygdala and thalamus. Mediation analysis confirmed that the direct effect of neural change on symptom response was not mediated by initial changes in depressive symptoms. These results support the hypothesis that early changes in emotional processing with antidepressant treatment are the basis of later clinical improvement. As such, early correction of negative bias may be a key mechanism of antidepressant drug action and a potentially useful predictor of therapeutic response.

Evaluation of the legal consequences of action affects neural activity and emotional experience during the resolution of moral dilemmas.

PubMed

Pletti, Carolina; Sarlo, Michela; Palomba, Daniela; Rumiati, Rino; Lotto, Lorella

2015-03-01

In any modern society killing is regarded as a severe violation of the legal codes that is subjected to penal judgment. Therefore, it is likely that people take legal consequences into account when deciding about the hypothetical killing of one person in classic moral dilemmas, with legal concerns contributing to decision-making. In particular, by differing for the degree of intentionality and emotional salience, Footbridge- and Trolley-type dilemmas might promote differential assignment of blame and punishment while implicating the same severity of harm. The present study was aimed at comparing the neural activity, subjective emotional reactions, and behavioral choices in two groups of participants who either took (Legal group) or did not take (No Legal group) legal consequences into account when deciding on Footbridge-type and Trolley-type moral dilemmas. Stimulus- and response-locked ERPs were measured to investigate the neural activity underlying two separate phases of the decision process. No difference in behavioral choices was found between groups. However, the No Legal group reported greater overall emotional impact, associated with lower preparation for action, suggesting greater conflict between alternative motor responses representing the different decision choices. In contrast, the Legal group showed an overall dampened affective experience during decision-making associated with greater overall action readiness and intention to act, reflecting lower conflict in responding. On these bases, we suggest that in moral dilemmas legal consequences of actions provide a sort of reference point on which people can rely to support a decision, independent of dilemma type. Copyright © 2015 Elsevier Inc. All rights reserved.

Selective Androgen Receptor Modulator RAD140 Is Neuroprotective in Cultured Neurons and Kainate-Lesioned Male Rats

PubMed Central

Jayaraman, Anusha; Christensen, Amy; Moser, V. Alexandra; Vest, Rebekah S.; Miller, Chris P.; Hattersley, Gary

2014-01-01

The decline in testosterone levels in men during normal aging increases risks of dysfunction and disease in androgen-responsive tissues, including brain. The use of testosterone therapy has the potential to increase the risks for developing prostate cancer and or accelerating its progression. To overcome this limitation, novel compounds termed “selective androgen receptor modulators” (SARMs) have been developed that lack significant androgen action in prostate but exert agonist effects in select androgen-responsive tissues. The efficacy of SARMs in brain is largely unknown. In this study, we investigate the SARM RAD140 in cultured rat neurons and male rat brain for its ability to provide neuroprotection, an important neural action of endogenous androgens that is relevant to neural health and resilience to neurodegenerative diseases. In cultured hippocampal neurons, RAD140 was as effective as testosterone in reducing cell death induced by apoptotic insults. Mechanistically, RAD140 neuroprotection was dependent upon MAPK signaling, as evidenced by elevation of ERK phosphorylation and inhibition of protection by the MAPK kinase inhibitor U0126. Importantly, RAD140 was also neuroprotective in vivo using the rat kainate lesion model. In experiments with gonadectomized, adult male rats, RAD140 was shown to exhibit peripheral tissue-specific androgen action that largely spared prostate, neural efficacy as demonstrated by activation of androgenic gene regulation effects, and neuroprotection of hippocampal neurons against cell death caused by systemic administration of the excitotoxin kainate. These novel findings demonstrate initial preclinical efficacy of a SARM in neuroprotective actions relevant to Alzheimer's disease and related neurodegenerative diseases. PMID:24428527

Beamforming approaches for untethered, ultrasonic neural dust motes for cortical recording: a simulation study.

PubMed

Bertrand, Alexander; Seo, Dongjin; Maksimovic, Filip; Carmena, Jose M; Maharbiz, Michel M; Alon, Elad; Rabaey, Jan M

2014-01-01

In this paper, we examine the use of beamforming techniques to interrogate a multitude of neural implants in a distributed, ultrasound-based intra-cortical recording platform known as Neural Dust. We propose a general framework to analyze system design tradeoffs in the ultrasonic beamformer that extracts neural signals from modulated ultrasound waves that are backscattered by free-floating neural dust (ND) motes. Simulations indicate that high-resolution linearly-constrained minimum variance beamforming sufficiently suppresses interference from unselected ND motes and can be incorporated into the ND-based cortical recording system.

Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex.

PubMed

Salmi, Juha; Koistinen, Olli-Pekka; Glerean, Enrico; Jylänki, Pasi; Vehtari, Aki; Jääskeläinen, Iiro P; Mäkelä, Sasu; Nummenmaa, Lauri; Nummi-Kuisma, Katarina; Nummi, Ilari; Sams, Mikko

2017-08-15

During a conversation or when listening to music, auditory and visual information are combined automatically into audiovisual objects. However, it is still poorly understood how specific type of visual information shapes neural processing of sounds in lifelike stimulus environments. Here we applied multi-voxel pattern analysis to investigate how naturally matching visual input modulates supratemporal cortex activity during processing of naturalistic acoustic speech, singing and instrumental music. Bayesian logistic regression classifiers with sparsity-promoting priors were trained to predict whether the stimulus was audiovisual or auditory, and whether it contained piano playing, speech, or singing. The predictive performances of the classifiers were tested by leaving one participant at a time for testing and training the model using the remaining 15 participants. The signature patterns associated with unimodal auditory stimuli encompassed distributed locations mostly in the middle and superior temporal gyrus (STG/MTG). A pattern regression analysis, based on a continuous acoustic model, revealed that activity in some of these MTG and STG areas were associated with acoustic features present in speech and music stimuli. Concurrent visual stimulus modulated activity in bilateral MTG (speech), lateral aspect of right anterior STG (singing), and bilateral parietal opercular cortex (piano). Our results suggest that specific supratemporal brain areas are involved in processing complex natural speech, singing, and piano playing, and other brain areas located in anterior (facial speech) and posterior (music-related hand actions) supratemporal cortex are influenced by related visual information. Those anterior and posterior supratemporal areas have been linked to stimulus identification and sensory-motor integration, respectively. Copyright © 2017 Elsevier Inc. All rights reserved.

Associative, Bidirectional Changes in Neural Signaling Utilizing NMDA Receptor- and Endocannabinoid-Dependent Mechanisms

ERIC Educational Resources Information Center

Li, Qin; Burrell, Brian D.

2011-01-01

Persistent, bidirectional changes in synaptic signaling (that is, potentiation and depression of the synapse) can be induced by the precise timing of individual pre- and postsynaptic action potentials. However, far less attention has been paid to the ability of paired trains of action potentials to elicit persistent potentiation or depression. We…

Development of Action Monitoring through Adolescence into Adulthood: ERP and Source Localization

ERIC Educational Resources Information Center

Ladouceur, Cecile D.; Dahl, Ronald E.; Carter, Cameron S.

2007-01-01

In this study we examined the development of three action monitoring event-related potentials (ERPs)--the error-related negativity (ERN/Ne), error positivity (P[subscript E]) and the N2--and estimated their neural sources. These ERPs were recorded during a flanker task in the following groups: early adolescents (mean age = 12 years), late…

A Computational Model of Inhibitory Control in Frontal Cortex and Basal Ganglia

ERIC Educational Resources Information Center

Wiecki, Thomas V.; Frank, Michael J.

2013-01-01

Planning and executing volitional actions in the face of conflicting habitual responses is a critical aspect of human behavior. At the core of the interplay between these 2 control systems lies an override mechanism that can suppress the habitual action selection process and allow executive control to take over. Here, we construct a neural circuit…

International Neural Network Society Annual Meeting (1994) Held in San Diego, California on 5-9 June 1994. Volume 3.

DTIC Science & Technology

1994-06-09

Competitive Neural Nets Speed Complex Fluid Flow Calculations 1-366 T. Long, E. Hanzevack Neural Networks for Steam Boiler MIMO Modeling and Advisory Control...Gallinr The Cochlear Nucleus and Primary Cortex as a Sequence of Distributed Neural Filters in Phoneme IV-607 Perception J. Antrobus, C. Tarshish, S...propulsion linear model, a fuel flow actuator modelled as a linear second order system with position and rate limits, and a thrust vectoring actuator

[Multi-channel in vivo recording techniques: signal processing of action potentials and local field potentials].

PubMed

Xu, Jia-Min; Wang, Ce-Qun; Lin, Long-Nian

2014-06-25

Multi-channel in vivo recording techniques are used to record ensemble neuronal activity and local field potentials (LFP) simultaneously. One of the key points for the technique is how to process these two sets of recorded neural signals properly so that data accuracy can be assured. We intend to introduce data processing approaches for action potentials and LFP based on the original data collected through multi-channel recording system. Action potential signals are high-frequency signals, hence high sampling rate of 40 kHz is normally chosen for recording. Based on waveforms of extracellularly recorded action potentials, tetrode technology combining principal component analysis can be used to discriminate neuronal spiking signals from differently spatially distributed neurons, in order to obtain accurate single neuron spiking activity. LFPs are low-frequency signals (lower than 300 Hz), hence the sampling rate of 1 kHz is used for LFPs. Digital filtering is required for LFP analysis to isolate different frequency oscillations including theta oscillation (4-12 Hz), which is dominant in active exploration and rapid-eye-movement (REM) sleep, gamma oscillation (30-80 Hz), which is accompanied by theta oscillation during cognitive processing, and high frequency ripple oscillation (100-250 Hz) in awake immobility and slow wave sleep (SWS) state in rodent hippocampus. For the obtained signals, common data post-processing methods include inter-spike interval analysis, spike auto-correlation analysis, spike cross-correlation analysis, power spectral density analysis, and spectrogram analysis.

Regulating prefrontal cortex activation: an emerging role for the 5-HT₂A serotonin receptor in the modulation of emotion-based actions?

PubMed

Aznar, Susana; Klein, Anders B

2013-12-01

The prefrontal cortex (PFC) is involved in mediating important higher-order cognitive processes such as decision making, prompting thereby our actions. At the same time, PFC activation is strongly influenced by emotional reactions through its functional interaction with the amygdala and the striatal circuitry, areas involved in emotion and reward processing. The PFC, however, is able to modulate amygdala reactivity via a feedback loop to this area. A role for serotonin in adjusting for this circuitry of cognitive regulation of emotion has long been suggested based primarily on the positive pharmacological effect of elevating serotonin levels in anxiety regulation. Recent animal and human functional magnetic resonance studies have pointed to a specific involvement of the 5-hydroxytryptamine (5-HT)2A serotonin receptor in the PFC feedback regulatory projection onto the amygdala. This receptor is highly expressed in the prefrontal cortex areas, playing an important role in modulating cortical activity and neural oscillations (brain waves). This makes it an interesting potential pharmacological target for the treatment of neuropsychiatric modes characterized by lack of inhibitory control of emotion-based actions, such as addiction and other impulse-related behaviors. In this review, we give an overview of the 5-HT2A receptor distribution (neuronal, intracellular, and anatomical) along with its functional and physiological effect on PFC activation, and how that relates to more recent findings of a regulatory effect of the PFC on the emotional control of our actions.

The dissociation between command following and communication in disorders of consciousness: an fMRI study in healthy subjects.

PubMed

Osborne, Natalie R; Owen, Adrian M; Fernández-Espejo, Davinia

2015-01-01

Neuroimaging studies have identified a subgroup of patients with a Disorder of Consciousness (DOC) who, while being behaviorally non-responsive, are nevertheless able to follow commands by modulating their brain activity in motor imagery (MI) tasks. These techniques have even allowed for binary communication in a small number of DOC patients. However, the majority of patients who can follow commands are unable to use their responses to communicate. A similar dissociation between present command following (CF) and absent communication abilities has been reported in overt behavioral assessments. However, the neural correlates of this dissociation in both overt and covert modalities are unknown. Here, we used functional magnetic resonance imaging (fMRI) to explore the neural mechanisms underlying CF and selection of responses for binary communication using either executed or imagined movements. Fifteen healthy participants executed or imagined two different types of arm movements that were either pre-determined by the experimenters (CF) or decided by them (action selection, AS). Action selection involved greater activity in high-level associative areas in frontal and parietal regions than CF. Additionally, motor execution (ME), as compared to MI, activated contralateral motor cortex, while the opposite contrast revealed activation in the ipsilateral sensorimotor cortex and the left inferior frontal gyrus. Importantly, there was no interaction between the task (CF/AS) and modality (MI/ME). Our results suggest that the neural processes involved in following a motor command or selecting between two motor actions are not dependent on how the response is expressed (via ME/MI). They also suggest a potential neural basis for the distinction in cognitive abilities seen in DOC patients.

The dissociation between command following and communication in disorders of consciousness: an fMRI study in healthy subjects

PubMed Central

Osborne, Natalie R.; Owen, Adrian M.; Fernández-Espejo, Davinia

2015-01-01

Neuroimaging studies have identified a subgroup of patients with a Disorder of Consciousness (DOC) who, while being behaviorally non-responsive, are nevertheless able to follow commands by modulating their brain activity in motor imagery (MI) tasks. These techniques have even allowed for binary communication in a small number of DOC patients. However, the majority of patients who can follow commands are unable to use their responses to communicate. A similar dissociation between present command following (CF) and absent communication abilities has been reported in overt behavioral assessments. However, the neural correlates of this dissociation in both overt and covert modalities are unknown. Here, we used functional magnetic resonance imaging (fMRI) to explore the neural mechanisms underlying CF and selection of responses for binary communication using either executed or imagined movements. Fifteen healthy participants executed or imagined two different types of arm movements that were either pre-determined by the experimenters (CF) or decided by them (action selection, AS). Action selection involved greater activity in high-level associative areas in frontal and parietal regions than CF. Additionally, motor execution (ME), as compared to MI, activated contralateral motor cortex, while the opposite contrast revealed activation in the ipsilateral sensorimotor cortex and the left inferior frontal gyrus. Importantly, there was no interaction between the task (CF/AS) and modality (MI/ME). Our results suggest that the neural processes involved in following a motor command or selecting between two motor actions are not dependent on how the response is expressed (via ME/MI). They also suggest a potential neural basis for the distinction in cognitive abilities seen in DOC patients. PMID:26441593

The neural exploitation hypothesis and its implications for an embodied approach to language and cognition: Insights from the study of action verbs processing and motor disorders in Parkinson's disease.

PubMed

Gallese, Vittorio; Cuccio, Valentina

2018-03-01

As it is widely known, Parkinson's disease is clinically characterized by motor disorders such as the loss of voluntary movement control, including resting tremor, postural instability, and bradykinesia (Bocanegra et al., 2015; Helmich, Hallett, Deuschl, Toni, & Bloem, 2012; Liu et al., 2006; Rosin, Topka, & Dichgans, 1997). In the last years, many empirical studies (e.g., Bocanegra et al., 2015; Spadacenta et al., 2012) have also shown that the processing of action verbs is selectively impaired in patients affected by this neurodegenerative disorder. In the light of these findings, it has been suggested that Parkinson disorder can be interpreted within an embodied cognition framework (e.g., Bocanegra et al., 2015). The central tenet of any embodied approach to language and cognition is that high order cognitive functions are grounded in the sensory-motor system. With regard to this point, Gallese (2008) proposed the neural exploitation hypothesis to account for, at the phylogenetic level, how key aspects of human language are underpinned by brain mechanisms originally evolved for sensory-motor integration. Glenberg and Gallese (2012) also applied the neural exploitation hypothesis to the ontogenetic level. On the basis of these premises, they developed a theory of language acquisition according to which, sensory-motor mechanisms provide a neurofunctional architecture for the acquisition of language, while retaining their original functions as well. The neural exploitation hypothesis is here applied to interpret the profile of patients affected by Parkinson's disease. It is suggested that action semantic impairments directly tap onto motor disorders. Finally, a discussion of what theory of language is needed to account for the interactions between language and movement disorders is presented. Copyright © 2018 Elsevier Ltd. All rights reserved.

Preliminary functional MRI neural correlates of executive functioning and empathy in children with obstructive sleep apnea.

PubMed

Kheirandish-Gozal, Leila; Yoder, Keith; Kulkarni, Richa; Gozal, David; Decety, Jean

2014-03-01

Pediatric obstructive sleep apnea (OSA) is associated with neurocognitive deficits. However, the neural substrates underlying such deficits remain unknown. To examine executive control and emotional processing in OSA, 10 children age 7 to 11 y with polysomnographically diagnosed OSA and 7 age- and sex-matched controls underwent a color-word Stroop task and an empathy task consisting of dynamic visual scenarios depicting interpersonal harm or neutral actions in a magnetic resonance imaging (MRI) scanner. Functional MRI data were processed using MATLAB 7.12 with SPM8 for region of interest (ROI) analyses, and a general linear model was used with regressors for each trial type in each task. For the Stroop task, accuracy was similar in the two groups, with no differences in the effect of incongruency on success rates. OSA showed greater neural activity than controls in eight ROI clusters for incongruent versus congruent trials (P < 0.001). Within the a priori ROIs, the anterior cingulate cortex was significantly different between groups (P < 0.05). For perceiving harm versus neutral actions, ROI analysis revealed a significant correlation between apnea-hypopnea index and left amygdala activity in harm versus neutral actions (r = -0.71, P < 0.05). These results provide the first functional MRI evidence that cognitive and empathetic processing is influenced by obstructive sleep apnea (OSA) in children. Children with OSA show greater neural recruitment of regions implicated in cognitive control, conflict monitoring, and attentional allocation in order to perform at the same level as children without OSA. When viewing empathy-eliciting scenarios, the severity of OSA predicted less sensitivity to harm in the left amygdala.

Genetic influences on functional connectivity associated with feedback processing and prediction error: Phase coupling of theta-band oscillations in twins.

PubMed

Demiral, Şükrü Barış; Golosheykin, Simon; Anokhin, Andrey P

2017-05-01

Detection and evaluation of the mismatch between the intended and actually obtained result of an action (reward prediction error) is an integral component of adaptive self-regulation of behavior. Extensive human and animal research has shown that evaluation of action outcome is supported by a distributed network of brain regions in which the anterior cingulate cortex (ACC) plays a central role, and the integration of distant brain regions into a unified feedback-processing network is enabled by long-range phase synchronization of cortical oscillations in the theta band. Neural correlates of feedback processing are associated with individual differences in normal and abnormal behavior, however, little is known about the role of genetic factors in the cerebral mechanisms of feedback processing. Here we examined genetic influences on functional cortical connectivity related to prediction error in young adult twins (age 18, n=399) using event-related EEG phase coherence analysis in a monetary gambling task. To identify prediction error-specific connectivity pattern, we compared responses to loss and gain feedback. Monetary loss produced a significant increase of theta-band synchronization between the frontal midline region and widespread areas of the scalp, particularly parietal areas, whereas gain resulted in increased synchrony primarily within the posterior regions. Genetic analyses showed significant heritability of frontoparietal theta phase synchronization (24 to 46%), suggesting that individual differences in large-scale network dynamics are under substantial genetic control. We conclude that theta-band synchronization of brain oscillations related to negative feedback reflects genetically transmitted differences in the neural mechanisms of feedback processing. To our knowledge, this is the first evidence for genetic influences on task-related functional brain connectivity assessed using direct real-time measures of neuronal synchronization. Copyright © 2016 Elsevier B.V. All rights reserved.

The Suppression of Beta Oscillations in the Primate Supplementary Motor Complex Reflects a Volatile State During the Updating of Action Sequences.

PubMed

Hosaka, Ryosuke; Nakajima, Toshi; Aihara, Kazuyuki; Yamaguchi, Yoko; Mushiake, Hajime

2016-08-01

The medial motor areas play crucial but flexible roles in the temporal organizations of multiple movements. The beta oscillation of local field potentials is the predominant oscillatory activity in the motor areas, but the manner in which increases and decreases in beta power contribute to updating of multiple action plans is not yet fully understood. In the present study, beta and high-gamma activities in the supplementary motor area (SMA) and pre-SMA of monkeys were analyzed during performance of a bimanual motor sequence task that required updating and maintenance of the memory of action sequences. Beta power was attenuated during early delay periods of updating trials but was increased during maintenance trials, while there was a reciprocal increase in high-gamma power during updating trials. Moreover, transient attenuation of beta power during maintenance trials resulted in the erroneous selection of an action sequence. Therefore, it was concluded that the suppression of beta power during the early delay period reflects volatility of neural representation of the action sequence. This neural representation would be properly updated to the appropriate instructed action sequence via increases in high-gamma power in updating trials whereas it would be erroneously updated without the appropriate updating signal in maintenance trials. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition

PubMed Central

Shu, Na; Gao, Zhiyong; Chen, Xiangan; Liu, Haihua

2015-01-01

Humans can easily understand other people’s actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model. PMID:26132270

Grasping actions and social interaction: neural bases and anatomical circuitry in the monkey

PubMed Central

Rozzi, Stefano; Coudé, Gino

2015-01-01

The study of the neural mechanisms underlying grasping actions showed that cognitive functions are deeply embedded in motor organization. In the first part of this review, we describe the anatomical structure of the motor cortex in the monkey and the cortical and sub-cortical connections of the different motor areas. In the second part, we review the neurophysiological literature showing that motor neurons are not only involved in movement execution, but also in the transformation of object physical features into motor programs appropriate to grasp them (through visuo-motor transformations). We also discuss evidence indicating that motor neurons can encode the goal of motor acts and the intention behind action execution. Then, we describe one of the mechanisms—the mirror mechanism—considered to be at the basis of action understanding and intention reading, and describe the anatomo-functional pathways through which information about the social context can reach the areas containing mirror neurons. Finally, we briefly show that a clear similarity exists between monkey and human in the organization of the motor and mirror systems. Based on monkey and human literature, we conclude that the mirror mechanism relies on a more extended network than previously thought, and possibly subserves basic social functions. We propose that this mechanism is also involved in preparing appropriate complementary response to observed actions, allowing two individuals to become attuned and cooperate in joint actions. PMID:26236258

Peripheral nerve recruitment curve using near-infrared stimulation

NASA Astrophysics Data System (ADS)

Dautrebande, Marie; Doguet, Pascal; Gorza, Simon-Pierre; Delbeke, Jean; Nonclercq, Antoine

2018-02-01

In the context of near-infrared neurostimulation, we report on an experimental hybrid electrode allowing for simultaneous photonic or electrical neurostimulation and for electrical recording of evoked action potentials. The electrode includes three contacts and one optrode. The optrode is an opening in the cuff through which the tip of an optical fibre is held close to the epineurium. Two contacts provide action potential recording. The remaining contact, together with a remote subcutaneous electrode, is used for electric stimulation which allows periodical assessment of the viability of the nerve during the experiment. A 1470 nm light source was used to stimulate a mouse sciatic nerve. Neural action potentials were not successfully recorded because of the electrical noise so muscular activity was used to reflect the motor fibres stimulation. A recruitment curve was obtained by stimulating with photonic pulses of same power and increasing duration and recording the evoked muscular action potentials. Motor fibres can be recruited with radiant exposures between 0.05 and 0.23 J/cm2 for pulses in the 100 to 500 μs range. Successful stimulation at short duration and at a commercial wavelength is encouraging in the prospect of miniaturisation and practical applications. Motor fibres recruitment curve is a first step in an ongoing research work. Neural action potential acquisition will be improved, with aim to shed light on the mechanism of action potential initiation under photonic stimulation.

Differentiating neural systems mediating the acquisition versus expression of goal-directed and habitual behavioral control

PubMed Central

Liljeholm, Mimi; Dunne, Simon; O'Doherty, John P.

2015-01-01

Considerable behavioral data indicates that operant actions can become habitual, as evidenced by insensitivity to changes in the action-outcome contingency and in subjective outcome values. Notably, although several studies have investigated the neural substrates of habits, none has clearly differentiated the areas of the human brain that support habit formation from those that implement habitual control. We scanned participants with fMRI as they learned and performed an operant task in which the conditional structure of the environment encouraged either goal-directed encoding of the consequences of actions, or a habit-like mapping of actions to antecedent cues. Participants were also scanned during a subsequent assessment of insensitivity to outcome devaluation. We identified dissociable roles of the cerebellum and ventral striatum, across learning and test performance, in behavioral insensitivity to outcome devaluation. We also show that the inferior parietal lobule – an area previously implicated in several aspects of goal-directed action selection, including the attribution of intent and awareness of agency – predicts sensitivity to outcome devaluation. Finally, we reveal a potential functional homology between the human subgenual cortex and rodent infralimbic cortex in the implementation of habitual control. In summary, our findings suggest a broad systems division, at the cortical and subcortical levels, between brain areas mediating the encoding and expression of action-outcome and stimulus-response associations. PMID:25892332

Action-Driven Visual Object Tracking With Deep Reinforcement Learning.

PubMed

Yun, Sangdoo; Choi, Jongwon; Yoo, Youngjoon; Yun, Kimin; Choi, Jin Young

2018-06-01

In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and background. The pretraining is done by utilizing deep reinforcement learning (RL) as well as supervised learning. The use of RL enables even partially labeled data to be successfully utilized for semisupervised learning. Through the evaluation of the object tracking benchmark data set, the proposed tracker is validated to achieve a competitive performance at three times the speed of existing deep network-based trackers. The fast version of the proposed method, which operates in real time on graphics processing unit, outperforms the state-of-the-art real-time trackers with an accuracy improvement of more than 8%.

SVM-based tree-type neural networks as a critic in adaptive critic designs for control.

PubMed

Deb, Alok Kanti; Jayadeva; Gopal, Madan; Chandra, Suresh

2007-07-01

In this paper, we use the approach of adaptive critic design (ACD) for control, specifically, the action-dependent heuristic dynamic programming (ADHDP) method. A least squares support vector machine (SVM) regressor has been used for generating the control actions, while an SVM-based tree-type neural network (NN) is used as the critic. After a failure occurs, the critic and action are retrained in tandem using the failure data. Failure data is binary classification data, where the number of failure states are very few as compared to the number of no-failure states. The difficulty of conventional multilayer feedforward NNs in learning this type of classification data has been overcome by using the SVM-based tree-type NN, which due to its feature to add neurons to learn misclassified data, has the capability to learn any binary classification data without a priori choice of the number of neurons or the structure of the network. The capability of the trained controller to handle unforeseen situations is demonstrated.

Neural bases of selective attention in action video game players.

PubMed

Bavelier, D; Achtman, R L; Mani, M; Föcker, J

2012-05-15

Over the past few years, the very act of playing action video games has been shown to enhance several different aspects of visual selective attention, yet little is known about the neural mechanisms that mediate such attentional benefits. A review of the aspects of attention enhanced in action game players suggests there are changes in the mechanisms that control attention allocation and its efficiency (Hubert-Wallander, Green, & Bavelier, 2010). The present study used brain imaging to test this hypothesis by comparing attentional network recruitment and distractor processing in action gamers versus non-gamers as attentional demands increased. Moving distractors were found to elicit lesser activation of the visual motion-sensitive area (MT/MST) in gamers as compared to non-gamers, suggestive of a better early filtering of irrelevant information in gamers. As expected, a fronto-parietal network of areas showed greater recruitment as attentional demands increased in non-gamers. In contrast, gamers barely engaged this network as attentional demands increased. This reduced activity in the fronto-parietal network that is hypothesized to control the flexible allocation of top-down attention is compatible with the proposal that action game players may allocate attentional resources more automatically, possibly allowing more efficient early filtering of irrelevant information. Copyright © 2011 Elsevier Ltd. All rights reserved.

Minimalist Social-Affective Value for Use in Joint Action: A Neural-Computational Hypothesis

PubMed Central

Lowe, Robert; Almér, Alexander; Lindblad, Gustaf; Gander, Pierre; Michael, John; Vesper, Cordula

2016-01-01

Joint Action is typically described as social interaction that requires coordination among two or more co-actors in order to achieve a common goal. In this article, we put forward a hypothesis for the existence of a neural-computational mechanism of affective valuation that may be critically exploited in Joint Action. Such a mechanism would serve to facilitate coordination between co-actors permitting a reduction of required information. Our hypothesized affective mechanism provides a value function based implementation of Associative Two-Process (ATP) theory that entails the classification of external stimuli according to outcome expectancies. This approach has been used to describe animal and human action that concerns differential outcome expectancies. Until now it has not been applied to social interaction. We describe our Affective ATP model as applied to social learning consistent with an “extended common currency” perspective in the social neuroscience literature. We contrast this to an alternative mechanism that provides an example implementation of the so-called social-specific value perspective. In brief, our Social-Affective ATP mechanism builds upon established formalisms for reinforcement learning (temporal difference learning models) nuanced to accommodate expectations (consistent with ATP theory) and extended to integrate non-social and social cues for use in Joint Action. PMID:27601989

Improved probabilistic inference as a general learning mechanism with action video games.

PubMed

Green, C Shawn; Pouget, Alexandre; Bavelier, Daphne

2010-09-14

Action video game play benefits performance in an array of sensory, perceptual, and attentional tasks that go well beyond the specifics of game play [1-9]. That a training regimen may induce improvements in so many different skills is notable because the majority of studies on training-induced learning report improvements on the trained task but limited transfer to other, even closely related, tasks ([10], but see also [11-13]). Here we ask whether improved probabilistic inference may explain such broad transfer. By using a visual perceptual decision making task [14, 15], the present study shows for the first time that action video game experience does indeed improve probabilistic inference. A neural model of this task [16] establishes how changing a single parameter, namely the strength of the connections between the neural layer providing the momentary evidence and the layer integrating the evidence over time, captures improvements in action-gamers behavior. These results were established in a visual, but also in a novel auditory, task, indicating generalization across modalities. Thus, improved probabilistic inference provides a general mechanism for why action video game playing enhances performance in a wide variety of tasks. In addition, this mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. Copyright © 2010 Elsevier Ltd. All rights reserved.

Power feasibility of implantable digital spike-sorting circuits for neural prosthetic systems.

PubMed

Zumsteg, Zachary S; Ahmed, Rizwan E; Santhanam, Gopal; Shenoy, Krishna V; Meng, Teresa H

2004-01-01

A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that several state-of-the-art spike sorting algorithms implemented in modern CMOS VLSI processes are expected to be power realistic.

What does the fruitless gene tell us about nature vs. nurture in the sex life of Drosophila?

PubMed

Yamamoto, Daisuke; Kohatsu, Soh

2017-04-03

The fruitless (fru) gene in Drosophila has been proposed to play a master regulator role in the formation of neural circuitries for male courtship behavior, which is typically considered to be an innate behavior composed of a fixed action pattern as generated by the central pattern generator. However, recent studies have shed light on experience-dependent changes and sensory-input-guided plasticity in courtship behavior. For example, enhanced male-male courtship, a fru mutant "hallmark," disappears when fru-mutant males are raised in isolation. The fact that neural fru expression is induced by neural activities in the adult invites the supposition that Fru as a chromatin regulator mediates experience-dependent epigenetic modification, which underlies the neural and behavioral plasticity.

Neural architectures for robot intelligence.

PubMed

Ritter, H; Steil, J J; Nölker, C; Röthling, F; McGuire, P

2003-01-01

We argue that direct experimental approaches to elucidate the architecture of higher brains may benefit from insights gained from exploring the possibilities and limits of artificial control architectures for robot systems. We present some of our recent work that has been motivated by that view and that is centered around the study of various aspects of hand actions since these are intimately linked with many higher cognitive abilities. As examples, we report on the development of a modular system for the recognition of continuous hand postures based on neural nets, the use of vision and tactile sensing for guiding prehensile movements of a multifingered hand, and the recognition and use of hand gestures for robot teaching. Regarding the issue of learning, we propose to view real-world learning from the perspective of data-mining and to focus more strongly on the imitation of observed actions instead of purely reinforcement-based exploration. As a concrete example of such an effort we report on the status of an ongoing project in our laboratory in which a robot equipped with an attention system with a neurally inspired architecture is taught actions by using hand gestures in conjunction with speech commands. We point out some of the lessons learnt from this system, and discuss how systems of this kind can contribute to the study of issues at the junction between natural and artificial cognitive systems.

Effect of fenspiride, a non-steroidal antiinflammatory agent, on neurogenic mucus secretion in ferret trachea in vitro.

PubMed

Khawaja, A M; Liu, Y C; Rogers, D F

1999-01-01

Neural mechanisms contribute to control of mucus secretion in the airways. Fenspiride is a non-steroidal antiinflammatory agent which has a variety of actions, including inhibition of neurogenic bronchoconstriction. The effect of fenspiride on neurally-mediated mucus secretion was investigated in vitro in electrically-stimulated ferret trachea, using(35)SO(4)as a mucus marker. Cholinergic secretory responses were isolated using adrenoceptor and tachykinin receptor antagonists. Tachykinin responses were isolated using cholinoceptor and adrenoceptor antagonists. Electrical stimulation increased cholinergic secretion by;90% and tachykininergic secretion by;40%. Fenspiride (1 microM-1 mM) tended to inhibit cholinergic secretion in a concentration-dependent manner, although only at 1 mM was inhibition (by 87%) significant. Inhibition by fenspiride of tachykininergic secretion was not concentration-dependent, and again significant inhibition (by 85%) was only at 1 mM. Inhibition was not due to loss of tissue viability, as assessed by restitution of secretory response after washout. Fenspiride also inhibited secretion induced by acetylcholine, but did not inhibit substance P-induced secretion. Histamine receptor antagonists increased basal secretion by 164%, whereas fenspiride did not affect basal secretion. We conclude that, in ferret trachea in vitro, fenspiride inhibits neurally-mediated mucus secretion, with antimuscarinic action the most plausible mechanism of action, but not necessarily the only mechanism. Copyright 1999 Academic Press.

Distributed synaptic weights in a LIF neural network and learning rules

NASA Astrophysics Data System (ADS)

Perthame, Benoît; Salort, Delphine; Wainrib, Gilles

2017-09-01

Leaky integrate-and-fire (LIF) models are mean-field limits, with a large number of neurons, used to describe neural networks. We consider inhomogeneous networks structured by a connectivity parameter (strengths of the synaptic weights) with the effect of processing the input current with different intensities. We first study the properties of the network activity depending on the distribution of synaptic weights and in particular its discrimination capacity. Then, we consider simple learning rules and determine the synaptic weight distribution it generates. We outline the role of noise as a selection principle and the capacity to memorize a learned signal.

Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neural networks and fuzzy logic methods.

PubMed

Yeşilkanat, Cafer Mert; Kobya, Yaşar; Taşkın, Halim; Çevik, Uğur

2017-09-01

The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate stations with no previous measurements and create dose rate risk maps of the study area. In order to determine the dose distribution by using artificial neural networks, two main networks and five different network structures were used; feed forward ANN; Multi-layer perceptron (MLP), Radial basis functional neural network (RBFNN), Quantile regression neural network (QRNN) and recurrent ANN; Jordan networks (JN), Elman networks (EN). In the evaluation of estimation performance obtained for the test data, all models appear to give similar results. According to the cross-validation results obtained for explaining AGDR distribution, Pearson's r coefficients were calculated as 0.94, 0.91, 0.89, 0.91, 0.91 and 0.92 and RMSE values were calculated as 34.78, 43.28, 63.92, 44.86, 46.77 and 37.92 for MLP, RBFNN, QRNN, JN, EN and FL, respectively. In addition, spatial risk maps showing distributions of AGDR of the study area were created by all models and results were compared with geological, topological and soil structure. Copyright © 2017 Elsevier Ltd. All rights reserved.

Informatic selection of a neural crest-melanocyte cDNA set for microarray analysis

PubMed Central

Loftus, S. K.; Chen, Y.; Gooden, G.; Ryan, J. F.; Birznieks, G.; Hilliard, M.; Baxevanis, A. D.; Bittner, M.; Meltzer, P.; Trent, J.; Pavan, W.

1999-01-01

With cDNA microarrays, it is now possible to compare the expression of many genes simultaneously. To maximize the likelihood of finding genes whose expression is altered under the experimental conditions, it would be advantageous to be able to select clones for tissue-appropriate cDNA sets. We have taken advantage of the extensive sequence information in the dbEST expressed sequence tag (EST) database to identify a neural crest-derived melanocyte cDNA set for microarray analysis. Analysis of characterized genes with dbEST identified one library that contained ESTs representing 21 neural crest-expressed genes (library 198). The distribution of the ESTs corresponding to these genes was biased toward being derived from library 198. This is in contrast to the EST distribution profile for a set of control genes, characterized to be more ubiquitously expressed in multiple tissues (P < 1 × 10−9). From library 198, a subset of 852 clustered ESTs were selected that have a library distribution profile similar to that of the 21 neural crest-expressed genes. Microarray analysis demonstrated the majority of the neural crest-selected 852 ESTs (Mel1 array) were differentially expressed in melanoma cell lines compared with a non-neural crest kidney epithelial cell line (P < 1 × 10−8). This was not observed with an array of 1,238 ESTs that was selected without library origin bias (P = 0.204). This study presents an approach for selecting tissue-appropriate cDNAs that can be used to examine the expression profiles of developmental processes and diseases. PMID:10430933

The Neural Foundations of Reaction and Action in Aversive Motivation.

PubMed

Campese, Vincent D; Sears, Robert M; Moscarello, Justin M; Diaz-Mataix, Lorenzo; Cain, Christopher K; LeDoux, Joseph E

2016-01-01

Much of the early research in aversive learning concerned motivation and reinforcement in avoidance conditioning and related paradigms. When the field transitioned toward the focus on Pavlovian threat conditioning in isolation, this paved the way for the clear understanding of the psychological principles and neural and molecular mechanisms responsible for this type of learning and memory that has unfolded over recent decades. Currently, avoidance conditioning is being revisited, and with what has been learned about associative aversive learning, rapid progress is being made. We review, below, the literature on the neural substrates critical for learning in instrumental active avoidance tasks and conditioned aversive motivation.

Automatic gain control of neural coupling during cooperative hand movements.

PubMed

Thomas, F A; Dietz, V; Schrafl-Altermatt, M

2018-04-13

Cooperative hand movements (e.g. opening a bottle) are controlled by a task-specific neural coupling, reflected in EMG reflex responses contralateral to the stimulation site. In this study the contralateral reflex responses in forearm extensor muscles to ipsilateral ulnar nerve stimulation was analyzed at various resistance and velocities of cooperative hand movements. The size of contralateral reflex responses was closely related to the level of forearm muscle activation required to accomplish the various cooperative hand movement tasks. This indicates an automatic gain control of neural coupling that allows a rapid matching of corrective forces exerted at both sides of an object with the goal 'two hands one action'.

Advances in neuroprosthetic learning and control.

PubMed

Carmena, Jose M

2013-01-01

Significant progress has occurred in the field of brain-machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.

Advances in Neuroprosthetic Learning and Control

PubMed Central

Carmena, Jose M.

2013-01-01

Significant progress has occurred in the field of brain–machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action. PMID:23700383

The efficacy of using human myoelectric signals to control the limbs of robots in space

NASA Technical Reports Server (NTRS)

Clark, Jane E.; Phillips, Sally J.

1988-01-01

This project was designed to investigate the usefulness of the myoelectric signal as a control in robotics applications. More specifically, the neural patterns associated with human arm and hand actions were studied to determine the efficacy of using these myoelectric signals to control the manipulator arm of a robot. The advantage of this approach to robotic control was the use of well-defined and well-practiced neural patterns already available to the system, as opposed to requiring the human operator to learn new tasks and establish new neural patterns in learning to control a joystick or mechanical coupling device.

Neural systems for preparatory control of imitation.

PubMed

Cross, Katy A; Iacoboni, Marco

2014-01-01

Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action. This work suggests that reactive control of imitation draws on at least partially specialized mechanisms. Here, we examine preparatory imitation control, where advance information allows control processes to be employed before an action is observed. Drawing on dual route models from the spatial compatibility literature, we compare control processes using biological and non-biological stimuli to determine whether preparatory imitation control recruits specialized neural systems that are similar to those observed in reactive imitation control. Results indicate that preparatory control involves anterior prefrontal, dorsolateral prefrontal, posterior parietal and early visual cortices regardless of whether automatic responses are evoked by biological (imitative) or non-biological stimuli. These results indicate both that preparatory control of imitation uses general mechanisms, and that preparatory control of imitation draws on different neural systems from reactive imitation control. Based on the regions involved, we hypothesize that preparatory control is implemented through top-down attentional biasing of visual processing.

Cognitive and neural components of the phenomenology of agency.

PubMed

Morsella, Ezequiel; Berger, Christopher C; Krieger, Stepehen C

2011-06-01

A primary aspect of the self is the sense of agency – the sense that one is causing an action. In the spirit of recent reductionistic approaches to other complex, multifaceted phenomena (e.g., working memory; cf. Johnson &Johnson, 2009), we attempt to unravel the sense of agency by investigating its most basic components, without invoking high-level conceptual or 'central executive' processes. After considering the high-level components of agency, we examine the cognitive and neural underpinnings of its low-level components, which include basic consciousness and subjective urges (e.g., the urge to breathe when holding one's breath). Regarding urges, a quantitative review revealed that certain inter-representational dynamics (conflicts between action plans, as when holding one's breath) reliably engender fundamental aspects both of the phenomenology of agency and of 'something countering the will of the self'. The neural correlates of such dynamics, for both primordial urges (e.g., air hunger) and urges elicited in laboratory interference tasks, are entertained. In addition, we discuss the implications of this unique perspective for the study of disorders involving agency.

Executive Control Over Cognition: Stronger and Earlier Rule-Based Modulation of Spatial Category Signals in Prefrontal Cortex Relative to Parietal Cortex

PubMed Central

Goodwin, Shikha J.; Blackman, Rachael K.; Sakellaridi, Sofia

2012-01-01

Human cognition is characterized by flexibility, the ability to select not only which action but which cognitive process to engage to best achieve the current behavioral objective. The ability to tailor information processing in the brain to rules, goals, or context is typically referred to as executive control, and although there is consensus that prefrontal cortex is importantly involved, at present we have an incomplete understanding of how computational flexibility is implemented at the level of prefrontal neurons and networks. To better understand the neural mechanisms of computational flexibility, we simultaneously recorded the electrical activity of groups of single neurons within prefrontal and posterior parietal cortex of monkeys performing a task that required executive control of spatial cognitive processing. In this task, monkeys applied different spatial categorization rules to reassign the same set of visual stimuli to alternative categories on a trial-by-trial basis. We found that single neurons were activated to represent spatially defined categories in a manner that was rule dependent, providing a physiological signature of a cognitive process that was implemented under executive control. We found also that neural signals coding rule-dependent categories were distributed between the parietal and prefrontal cortex—however, not equally. Rule-dependent category signals were stronger, more powerfully modulated by the rule, and earlier to emerge in prefrontal cortex relative to parietal cortex. This suggests that prefrontal cortex may initiate the switch in neural representation at a network level that is important for computational flexibility. PMID:22399773

A SOMATIC-MARKER THEORY OF ADDICTION

PubMed Central

Verdejo-García, Antonio; Bechara, Antoine

2009-01-01

Similar to patients with ventromedial prefrontal cortex (VMPC) lesions, substance abusers show altered decision-making, characterized by a tendency to choose the immediate reward, at the expense of negative future consequences. The somatic-marker model proposes that decision-making depends on neural substrates that regulate homeostasis, emotion and feeling. According to this model, there should be a link between alterations in processing emotions in substance abusers, and their impairments in decision-making. A growing evidence from neuroscientific studies indicate that core aspects of addiction may be explained in terms of abnormal emotional/homeostatic guidance of decision-making. Behavioural studies have revealed emotional processing and decision-making deficits in substance abusers. Neuroimaging studies have shown that altered decision-making in addiction is associated with abnormal functioning of a distributed neural network critical for the processing of emotional information, and the experience of “craving”, including the VMPC, the amygdala, the striatum, the anterior cingulate cortex, and the insular/somato-sensory cortices, as well as non-specific neurotransmitter systems that modulate activities of neural processes involved in decision-making. The aim of this paper is to review this growing evidence, and to examine the extent of which these studies support a somatic-marker theory of addiction. We conclude that there are at least two underlying types of dysfunctions where emotional signals (somatic-markers) turns in favor of immediate outcomes in addiction: (1) a hyperactivity in the amygdala or impulsive system, which exaggerates the rewarding impact of available incentives, and (2) hypoactivity in the prefrontal cortex or reflective system, which forecasts the long-term consequences of a given action. PMID:18722390

Using Goal- and Grip-Related Information for Understanding the Correctness of Other’s Actions: An ERP Study

PubMed Central

van Elk, Michiel; Bousardt, Roel; Bekkering, Harold; van Schie, Hein T.

2012-01-01

Detecting errors in other’s actions is of pivotal importance for joint action, competitive behavior and observational learning. Although many studies have focused on the neural mechanisms involved in detecting low-level errors, relatively little is known about error-detection in everyday situations. The present study aimed to identify the functional and neural mechanisms whereby we understand the correctness of other’s actions involving well-known objects (e.g. pouring coffee in a cup). Participants observed action sequences in which the correctness of the object grasped and the grip applied to a pair of objects were independently manipulated. Observation of object violations (e.g. grasping the empty cup instead of the coffee pot) resulted in a stronger P3-effect than observation of grip errors (e.g. grasping the coffee pot at the upper part instead of the handle), likely reflecting a reorienting response, directing attention to the relevant location. Following the P3-effect, a parietal slow wave positivity was observed that persisted for grip-errors, likely reflecting the detection of an incorrect hand-object interaction. These findings provide new insight in the functional significance of the neurophysiological markers associated with the observation of incorrect actions and suggest that the P3-effect and the subsequent parietal slow wave positivity may reflect the detection of errors at different levels in the action hierarchy. Thereby this study elucidates the cognitive processes that support the detection of action violations in the selection of objects and grips. PMID:22606261

Microprobe array with low impedance electrodes and highly flexible polyimide cables for acute neural recording.

PubMed

Kisban, S; Herwik, S; Seidl, K; Rubehn, B; Jezzini, A; Umiltà, M A; Fogassi, L; Stieglitz, T; Paul, O; Ruther, P

2007-01-01

This paper reports on a novel type of silicon-based microprobes with linear, two and three dimensional (3D) distribution of their recording sites. The microprobes comprise either single shafts, combs with multiple shafts or 3D arrays combining two combs with 9, 36 or 72 recording sites, respectively. The electrical interconnection of the probes is achieved through highly flexible polyimide ribbon cables attached using the MicroFlex Technology which allows a connection part of small lateral dimensions. For an improved handling, probes can be secured by a protecting canula. Low-impedance electrodes are achieved by the deposition of platinum black. First in vivo experiments proved the capability to record single action potentials in the motor cortex from electrodes close to the tip as well as body electrodes along the shaft.

Electro-mechanical response of a 3D nerve bundle model to mechanical loads leading to axonal injury.

PubMed

Cinelli, I; Destrade, M; Duffy, M; McHugh, P

2017-07-01

Axonal damage is one of the most common pathological features of traumatic brain injury, leading to abnormalities in signal propagation for nervous systems. We present a 3D fully coupled electro-mechanical model of a nerve bundle, made with the finite element software Abaqus 6.13-3. The model includes a real-time coupling, modulated threshold for spiking activation and independent alteration of the electrical properties for each 3-layer fibre within the bundle. Compression and tension are simulated to induce damage at the nerve membrane. Changes in strain, stress distribution and neural activity are investigated for myelinated and unmyelinated nerve fibres, by considering the cases of an intact and of a traumatized nerve membrane. Results show greater changes in transmitting action potential in the myelinated fibre.

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters.

PubMed

Wang, Dandan; Zong, Qun; Tian, Bailing; Shao, Shikai; Zhang, Xiuyun; Zhao, Xinyi

2018-02-01

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks

NASA Astrophysics Data System (ADS)

Zhelavskaya, I. S.; Shprits, Y.; Spasojevic, M.

2017-12-01

We present a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of October 1, 2012 - July 1, 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2 ≤ L ≤ 6 and all local times. We validate and test the model by measuring its performance on independent datasets withheld from the training set and by comparing the model predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in-situ observations by using machine learning techniques.

Neurodevelopmental effects of insulin-like growth factor signaling

PubMed Central

O’Kusky, John; Ye, Ping

2012-01-01

Insulin-like growth factor (IGF) signaling greatly impacts the development and growth of the central nervous system (CNS). IGF-I and IGF-II, two ligands of the IGF system, exert a wide variety of actions both during development and in adulthood, promoting the survival and proliferation of neural cells. The IGFs also influence the growth and maturation of neural cells, augmenting dendritic growth and spine formation, axon outgrowth, synaptogenesis, and myelination. Specific IGF actions, however, likely depend on cell type, developmental stage, and local microenvironmental milieu within the brain. Emerging research also indicates that alterations in IGF signaling likely contribute to the pathogenesis of some neurological disorders. This review summarizes experimental studies and shed light on the critical roles of IGF signaling, as well as its mechanisms, during CNS development. PMID:22710100

Machine learning action parameters in lattice quantum chromodynamics

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

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information contentmore » and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.« less

Machine learning action parameters in lattice quantum chromodynamics

DOE PAGES

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

2018-05-16

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information contentmore » and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.« less

The Neuropeptide Tac2 Controls a Distributed Brain State Induced by Chronic Social Isolation Stress.

PubMed

Zelikowsky, Moriel; Hui, May; Karigo, Tomomi; Choe, Andrea; Yang, Bin; Blanco, Mario R; Beadle, Keith; Gradinaru, Viviana; Deverman, Benjamin E; Anderson, David J

2018-05-17

Chronic social isolation causes severe psychological effects in humans, but their neural bases remain poorly understood. 2 weeks (but not 24 hr) of social isolation stress (SIS) caused multiple behavioral changes in mice and induced brain-wide upregulation of the neuropeptide tachykinin 2 (Tac2)/neurokinin B (NkB). Systemic administration of an Nk3R antagonist prevented virtually all of the behavioral effects of chronic SIS. Conversely, enhancing NkB expression and release phenocopied SIS in group-housed mice, promoting aggression and converting stimulus-locked defensive behaviors to persistent responses. Multiplexed analysis of Tac2/NkB function in multiple brain areas revealed dissociable, region-specific requirements for both the peptide and its receptor in different SIS-induced behavioral changes. Thus, Tac2 coordinates a pleiotropic brain state caused by SIS via a distributed mode of action. These data reveal the profound effects of prolonged social isolation on brain chemistry and function and suggest potential new therapeutic applications for Nk3R antagonists. Copyright © 2018 Elsevier Inc. All rights reserved.

Eye Gaze Metrics Reflect a Shared Motor Representation for Action Observation and Movement Imagery

ERIC Educational Resources Information Center

McCormick, Sheree A.; Causer, Joe; Holmes, Paul S.

2012-01-01

Action observation (AO) and movement imagery (MI) have been reported to share similar neural networks. This study investigated the congruency between AO and MI using the eye gaze metrics, dwell time and fixation number. A simple reach-grasp-place arm movement was observed and, in a second condition, imagined where the movement was presented from…

By the sound of it. An ERP investigation of human action sound processing in 7-month-old infants

PubMed Central

Geangu, Elena; Quadrelli, Ermanno; Lewis, James W.; Macchi Cassia, Viola; Turati, Chiara

2015-01-01

Recent evidence suggests that human adults perceive human action sounds as a distinct category from human vocalizations, environmental, and mechanical sounds, activating different neural networks (Engel et al., 2009; Lewis et al., 2011). Yet, little is known about the development of such specialization. Using event-related potentials (ERP), this study investigated neural correlates of 7-month-olds’ processing of human action (HA) sounds in comparison to human vocalizations (HV), environmental (ENV), and mechanical (MEC) sounds. Relative to the other categories, HA sounds led to increased positive amplitudes between 470 and 570 ms post-stimulus onset at left anterior temporal locations, while HV led to increased negative amplitudes at the more posterior temporal locations in both hemispheres. Collectively, human produced sounds (HA + HV) led to significantly different response profiles compared to non-living sound sources (ENV + MEC) at parietal and frontal locations in both hemispheres. Overall, by 7 months of age human action sounds are being differentially processed in the brain, consistent with a dichotomy for processing living versus non-living things. This provides novel evidence regarding the typical categorical processing of socially relevant sounds. PMID:25732377

Analysis of Neural Stem Cells from Human Cortical Brain Structures In Vitro.

PubMed

Aleksandrova, M A; Poltavtseva, R A; Marei, M V; Sukhikh, G T

2016-05-01

Comparative immunohistochemical analysis of the neocortex from human fetuses showed that neural stem and progenitor cells are present in the brain throughout the gestation period, at least from week 8 through 26. At the same time, neural stem cells from the first and second trimester fetuses differed by the distribution, morphology, growth, and quantity. Immunocytochemical analysis of neural stem cells derived from fetuses at different gestation terms and cultured under different conditions showed their differentiation capacity. Detailed analysis of neural stem cell populations derived from fetuses on gestation weeks 8-9, 18-20, and 26 expressing Lex/SSEA1 was performed.

Contact-force distribution optimization and control for quadruped robots using both gradient and adaptive neural networks.

PubMed

Li, Zhijun; Ge, Shuzhi Sam; Liu, Sibang

2014-08-01

This paper investigates optimal feet forces' distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamics of quadruped robots and derive a reduced-order dynamical model of motion/force. Consider an external wrench on quadruped robots; the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to deal with the optimized objective function formulated as to minimize this quadratic objective function subjected to linear equality and inequality constraints. For the obtained optimized tip-point force and the motion of legs, we propose the hybrid motion/force control based on an adaptive neural network to compensate for the perturbations in the environment and approximate feedforward force and impedance of the leg joints. The proposed control can confront the uncertainties including approximation error and external perturbation. The verification of the proposed control is conducted using a simulation.

Neural Integration in Body Perception.

PubMed

Ramsey, Richard

2018-06-19

The perception of other people is instrumental in guiding social interactions. For example, the appearance of the human body cues a wide range of inferences regarding sex, age, health, and personality, as well as emotional state and intentions, which influence social behavior. To date, most neuroscience research on body perception has aimed to characterize the functional contribution of segregated patches of cortex in the ventral visual stream. In light of the growing prominence of network architectures in neuroscience, the current article reviews neuroimaging studies that measure functional integration between different brain regions during body perception. The review demonstrates that body perception is not restricted to processing in the ventral visual stream but instead reflects a functional alliance between the ventral visual stream and extended neural systems associated with action perception, executive functions, and theory of mind. Overall, these findings demonstrate how body percepts are constructed through interactions in distributed brain networks and underscore that functional segregation and integration should be considered together when formulating neurocognitive theories of body perception. Insight from such an updated model of body perception generalizes to inform the organizational structure of social perception and cognition more generally and also informs disorders of body image, such as anorexia nervosa, which may rely on atypical integration of body-related information.

Perceiving nonverbal behavior: neural correlates of processing movement fluency and contingency in dyadic interactions.

PubMed

Georgescu, Alexandra L; Kuzmanovic, Bojana; Santos, Natacha S; Tepest, Ralf; Bente, Gary; Tittgemeyer, Marc; Vogeley, Kai

2014-04-01

Despite the fact that nonverbal dyadic social interactions are abundant in the environment, the neural mechanisms underlying their processing are not yet fully understood. Research in the field of social neuroscience has suggested that two neural networks appear to be involved in social understanding: (1) the action observation network (AON) and (2) the social neural network (SNN). The aim of this study was to determine the differential contributions of the AON and the SNN to the processing of nonverbal behavior as observed in dyadic social interactions. To this end, we used short computer animation sequences displaying dyadic social interactions between two virtual characters and systematically manipulated two key features of movement activity, which are known to influence the perception of meaning in nonverbal stimuli: (1) movement fluency and (2) contingency of movement patterns. A group of 21 male participants rated the "naturalness" of the observed scenes on a four-point scale while undergoing fMRI. Behavioral results showed that both fluency and contingency significantly influenced the "naturalness" experience of the presented animations. Neurally, the AON was preferentially engaged when processing contingent movement patterns, but did not discriminate between different degrees of movement fluency. In contrast, regions of the SNN were engaged more strongly when observing dyads with disturbed movement fluency. In conclusion, while the AON is involved in the general processing of contingent social actions, irrespective of their kinematic properties, the SNN is preferentially recruited when atypical kinematic properties prompt inferences about the agents' intentions. Copyright © 2013 Wiley Periodicals, Inc.

Disruption of Boundary Encoding During Sensorimotor Sequence Learning: An MEG Study.

PubMed

Michail, Georgios; Nikulin, Vadim V; Curio, Gabriel; Maess, Burkhard; Herrojo Ruiz, María

2018-01-01

Music performance relies on the ability to learn and execute actions and their associated sounds. The process of learning these auditory-motor contingencies depends on the proper encoding of the serial order of the actions and sounds. Among the different serial positions of a behavioral sequence, the first and last (boundary) elements are particularly relevant. Animal and patient studies have demonstrated a specific neural representation for boundary elements in prefrontal cortical regions and in the basal ganglia, highlighting the relevance of their proper encoding. The neural mechanisms underlying the encoding of sequence boundaries in the general human population remain, however, largely unknown. In this study, we examined how alterations of auditory feedback, introduced at different ordinal positions (boundary or within-sequence element), affect the neural and behavioral responses during sensorimotor sequence learning. Analysing the neuromagnetic signals from 20 participants while they performed short piano sequences under the occasional effect of altered feedback (AF), we found that at around 150-200 ms post-keystroke, the neural activities in the dorsolateral prefrontal cortex (DLPFC) and supplementary motor area (SMA) were dissociated for boundary and within-sequence elements. Furthermore, the behavioral data demonstrated that feedback alterations on boundaries led to greater performance costs, such as more errors in the subsequent keystrokes. These findings jointly support the idea that the proper encoding of boundaries is critical in acquiring sensorimotor sequences. They also provide evidence for the involvement of a distinct neural circuitry in humans including prefrontal and higher-order motor areas during the encoding of the different classes of serial order.

Neural Mechanisms of Emotion.

ERIC Educational Resources Information Center

Derryberry, Douglas; Tucker, Don M.

1992-01-01

Views neural mechanisms of emotion from an evolutionary perspective, seeing them distributed across the brainstem, limbic, paralimbic, and neocortical regions. Discusses descending and ascending connections among these levels in relation to three types of emotional processes: peripheral effects on patterned bodily responses, central effects on…

Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.

PubMed

Carpenter, Gail A.

1997-11-01

A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.

[Relationship between reference values of fibrinogen and geographical factors based on neural network analysis].

PubMed

Li, Meng-Jiao; Ge, Miao; Wang, Cong-Xia; Cen, Min-Yi; Jiang, Ji-Lin; He, Jin-Wei; Lin, Qian-Yi; Liu, Xin

2016-08-20

To analyze the relationship between the reference values of fibrinogen (FIB) in healthy Chinese adults and geographical factors to provide scientific evidences for establishing the uniform standard. The reference values of FIB of 10701 Chinese healthy adults from 103 cities were collected to investigate their relationship with 18 geographical factors including spatial index, terrain index, climate index, and soil index. Geographical factors that significantly correlated with the reference values were selected for constructing the BP neural network model. The spatial distribution map of the reference value of FIB of healthy Chinese adults was fitted by disjunctive kriging interpolation. We used the 5-layer neural network and selected 2000 times of training covering 11 hidden layers to build the simulation rule for simulating the relationship between FIB and geographical environmental factors using the MATLAB software. s The reference value of FIB in healthy Chinese adults was significantly correlated with the latitude, sunshine duration, annual average temperature, annual average relative humidity, annual precipitation, annual range of air temperature, average annual soil gravel content, and soil cation exchange capacity (silt). The artificial neural networks were created to analyze the simulation of the selected indicators of geographical factors. The spatial distribution map of the reference values of FIB in healthy Chinese adults showed a distribution pattern that FIB levels were higher in the South and lower in the North, and higher in the East and lower in the West. When the geographical factors of a certain area are known, the reference values of FIB in healthy Chinese adults can be obtained by establishing the neural network mode or plotting the spatial distribution map.

Proceedings of the Organization of 1990 Meeting of International Neural Network Society Jointed with IEEE Held in Washington, DC on January 15 - 19, 1990. Volume 2. Applications Track.

DTIC Science & Technology

1990-11-30

Simonotto Universita’ di Genova Learning from Natural Selection in an Artificial Environment ...................................................... 1...11-92 Ethem Alpaydin Swiss Federal Institute of Technology Framework for Distributed Artificial Neural System Simulation...11-129 David Y. Fong Lockheed Missiles and Space Co. and Christopher Tocci Raytheon Co. Simulation of Artificial Neural

Correlation Filter Synthesis Using Neural Networks.

DTIC Science & Technology

1993-12-01

trained neural networks may be understood as "smart" data interpolators, the stored filter and the filter synthesis approaches have much in common: in...the former new filters are found by searching a data bank consisting of the filters themselves; in the latter filters are formed from a distributed... data bank that contains neural network interaction strengths or weights. 1.2 Key Results and Outputs Excellent computer simulation results were

Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

PubMed

Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

2017-03-01

Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

Mapping face categorization in the human ventral occipitotemporal cortex with direct neural intracranial recordings.

PubMed

Rossion, Bruno; Jacques, Corentin; Jonas, Jacques

2018-02-26

The neural basis of face categorization has been widely investigated with functional magnetic resonance imaging (fMRI), identifying a set of face-selective local regions in the ventral occipitotemporal cortex (VOTC). However, indirect recording of neural activity with fMRI is associated with large fluctuations of signal across regions, often underestimating face-selective responses in the anterior VOTC. While direct recording of neural activity with subdural grids of electrodes (electrocorticography, ECoG) or depth electrodes (stereotactic electroencephalography, SEEG) offers a unique opportunity to fill this gap in knowledge, these studies rather reveal widely distributed face-selective responses. Moreover, intracranial recordings are complicated by interindividual variability in neuroanatomy, ambiguity in definition, and quantification of responses of interest, as well as limited access to sulci with ECoG. Here, we propose to combine SEEG in large samples of individuals with fast periodic visual stimulation to objectively define, quantify, and characterize face categorization across the whole VOTC. This approach reconciles the wide distribution of neural face categorization responses with their (right) hemispheric and regional specialization, and reveals several face-selective regions in anterior VOTC sulci. We outline the challenges of this research program to understand the neural basis of face categorization and high-level visual recognition in general. © 2018 New York Academy of Sciences.

Evolutionary Artificial Neural Network Weight Tuning to Optimize Decision Making for an Abstract Game

DTIC Science & Technology

2010-03-01

separate LoA heuristic. If any of the examined heuristics produced competitive player , then the final measurement was a success . Barring that, a...if offline training actually results in a successful player . Whereas offline learning plays many games and then trains as many networks as desired...a competitive Lines of Action player , shedding light on the difficulty of developing a neural network to model such a large and complex solution

Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap S.

1992-01-01

Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.

Determining the neural substrates of goal-directed learning in the human brain.

PubMed

Valentin, Vivian V; Dickinson, Anthony; O'Doherty, John P

2007-04-11

Instrumental conditioning is considered to involve at least two distinct learning systems: a goal-directed system that learns associations between responses and the incentive value of outcomes, and a habit system that learns associations between stimuli and responses without any link to the outcome that that response engendered. Lesion studies in rodents suggest that these two distinct components of instrumental conditioning may be mediated by anatomically distinct neural systems. The aim of the present study was to determine the neural substrates of the goal-directed component of instrumental learning in humans. Nineteen human subjects were scanned with functional magnetic resonance imaging while they learned to choose instrumental actions that were associated with the subsequent delivery of different food rewards (tomato juice, chocolate milk, and orange juice). After training, one of these foods was devalued by feeding the subject to satiety on that food. The subjects were then scanned again, while being re-exposed to the instrumental choice procedure (in extinction). We hypothesized that regions of the brain involved in goal-directed learning would show changes in their activity as a function of outcome devaluation. Our results indicate that neural activity in one brain region in particular, the orbitofrontal cortex, showed a strong modulation in its activity during selection of a devalued compared with a nondevalued action. These results suggest an important contribution of orbitofrontal cortex in guiding goal-directed instrumental choices in humans.

Synaptic plasticity, neural circuits, and the emerging role of altered short-term information processing in schizophrenia

PubMed Central

Crabtree, Gregg W.; Gogos, Joseph A.

2014-01-01

Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adaptive behaviors. As current theories of neuropsychiatric disease suggest that distinct dysfunctions in neural circuit performance may critically underlie the unique symptoms of these diseases, pathological alterations in synaptic plasticity mechanisms may be fundamental to the disease process. Here we consider mechanisms of both short-term and long-term plasticity of synaptic transmission and their possible roles in information processing by neural microcircuits in both health and disease. As paradigms of neuropsychiatric diseases with strongly implicated risk genes, we discuss the findings in schizophrenia and autism and consider the alterations in synaptic plasticity and network function observed in both human studies and genetic mouse models of these diseases. Together these studies have begun to point toward a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders (ASDs) may be due to a combination of both short-term and long-term synaptic plasticity alterations. PMID:25505409

Investigating habits: strategies, technologies and models

PubMed Central

Smith, Kyle S.; Graybiel, Ann M.

2014-01-01

Understanding habits at a biological level requires a combination of behavioral observations and measures of ongoing neural activity. Theoretical frameworks as well as definitions of habitual behaviors emerging from classic behavioral research have been enriched by new approaches taking account of the identification of brain regions and circuits related to habitual behavior. Together, this combination of experimental and theoretical work has provided key insights into how brain circuits underlying action-learning and action-selection are organized, and how a balance between behavioral flexibility and fixity is achieved. New methods to monitor and manipulate neural activity in real time are allowing us to have a first look “under the hood” of a habit as it is formed and expressed. Here we discuss ideas emerging from such approaches. We pay special attention to the unexpected findings that have arisen from our own experiments suggesting that habitual behaviors likely require the simultaneous activity of multiple distinct components, or operators, seen as responsible for the contrasting dynamics of neural activity in both cortico-limbic and sensorimotor circuits recorded concurrently during different stages of habit learning. The neural dynamics identified thus far do not fully meet expectations derived from traditional models of the structure of habits, and the behavioral measures of habits that we have made also are not fully aligned with these models. We explore these new clues as opportunities to refine an understanding of habits. PMID:24574988

Attack of the nervous system by Clostridium perfringens Epsilon toxin: from disease to mode of action on neural cells.

PubMed

Wioland, Laetitia; Dupont, Jean-Luc; Bossu, Jean-Louis; Popoff, Michel R; Poulain, Bernard

2013-12-01

Epsilon toxin (ET), produced by Clostridium perfringens types B and D, ranks among the four most potent poisonous substances known so far. ET-intoxication is responsible for enterotoxaemia in animals, mainly sheep and goats. This disease comprises several manifestations indicating the attack of the nervous system. This review aims to summarize the effects of ET on central nervous system. ET binds to endothelial cells of brain capillary vessels before passing through the blood-brain barrier. Therefore, it induces perivascular oedema and accumulates into brain. ET binding to different brain structures and to different component in the brain indicates regional susceptibility to the toxin. Histological examination has revealed nerve tissue and cellular lesions, which may be directly or indirectly caused by ET. The naturally occurring disease caused by ET-intoxication can be reproduced experimentally in rodents. In mice and rats, ET recognizes receptor at the surface of different neural cell types, including certain neurons (e.g. the granule cells in cerebellum) as well as oligodendrocytes, which are the glial cells responsible for the axons myelination. Moreover, ET induces release of glutamate and other transmitters, leading to firing of neural network. The precise mode of action of ET on neural cells remains to be determined. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

[Folic acid: Primary prevention of neural tube defects. Literature Review].

PubMed

Llamas Centeno, M J; Miguélez Lago, C

2016-03-01

Neural tube defects (NTD) are the most common congenital malformations of the nervous system, they have a multifactorial etiology, are caused by exposure to chemical, physical or biological toxic agents, factors deficiency, diabetes, obesity, hyperthermia, genetic alterations and unknown causes. Some of these factors are associated with malnutrition by interfering with the folic acid metabolic pathway, the vitamin responsible for neural tube closure. Its deficit produce anomalies that can cause abortions, stillbirths or newborn serious injuries that cause disability, impaired quality of life and require expensive treatments to try to alleviate in some way the alterations produced in the embryo. Folic acid deficiency is considered the ultimate cause of the production of neural tube defects, it is clear the reduction in the incidence of Espina Bifida after administration of folic acid before conception, this leads us to want to further study the action of folic acid and its application in the primary prevention of neural tube defects. More than 40 countries have made the fortification of flour with folate, achieving encouraging data of decrease in the prevalence of neural tube defects. This paper attempts to make a literature review, which clarify the current situation and future of the prevention of neural tube defects.

Genetic dissection of neural circuits underlying sexually dimorphic social behaviours

PubMed Central

Bayless, Daniel W.; Shah, Nirao M.

2016-01-01

The unique hormonal, genetic and epigenetic environments of males and females during development and adulthood shape the neural circuitry of the brain. These differences in neural circuitry result in sex-typical displays of social behaviours such as mating and aggression. Like other neural circuits, those underlying sex-typical social behaviours weave through complex brain regions that control a variety of diverse behaviours. For this reason, the functional dissection of neural circuits underlying sex-typical social behaviours has proved to be difficult. However, molecularly discrete neuronal subpopulations can be identified in the heterogeneous brain regions that control sex-typical social behaviours. In addition, the actions of oestrogens and androgens produce sex differences in gene expression within these brain regions, thereby highlighting the neuronal subpopulations most likely to control sexually dimorphic social behaviours. These conditions permit the implementation of innovative genetic approaches that, in mammals, are most highly advanced in the laboratory mouse. Such approaches have greatly advanced our understanding of the functional significance of sexually dimorphic neural circuits in the brain. In this review, we discuss the neural circuitry of sex-typical social behaviours in mice while highlighting the genetic technical innovations that have advanced the field. PMID:26833830

Differentiation in the effects of the angiotensin II receptor blocker class on autonomic function.

PubMed

Esler, Murray

2002-06-01

Measurement of regional sympathetic activity with nerve recording and noradrenaline spillover isotope dilution techniques demonstrates activation of the sympathetic nerves of the heart, kidneys and skeletal muscle vasculature in younger patients with essential hypertension. Sympathetic overactivity in the renal sympathetic outflow is a prominent pathophysiological feature in obesity-related hypertensives of any age. This increase in sympathetic activity is thought to both initiate and sustain the blood pressure elevation, and, in addition, contributes to adverse cardiovascular events. Sympathetic overactivity seems to particularly influence systolic pressure, by increasing the rate of left ventricular ejection, by reducing arterial compliance through increasing neural arterial tone, and via arteriolar vasoconstriction, by promoting rebound of the reflected arterial wave from the periphery. Inhibition of the renin-angiotensin system in certain circumstances appears to be able to reduce sympathetic nervous activity. Claims have been made for such an action at virtually every site in the sympathetic neuraxis. In reality, renin-angiotensin actions on the sympathetic nervous system are probably much more circumscribed than this, with the case perhaps being strongest for a presynaptic action of angiotensin on sympathetic nerves, to augment noradrenaline release. The ability of angiotensin receptor blockers to antagonize neural presynaptic angiotensin AT1 receptors appears to differ markedly between the individual agents in this drug class. In experimental models, such as the pithed rat, neural presynaptic actions are particularly evident with eprosartan. In a blinded study of crossover design, the effects of eprosartan and losartan on sympathetic nerve firing, measured by microneurography, and whole body noradrenaline spillover to plasma is currently being measured in patients with essential hypertension. A reduction in noradrenaline spillover disproportionate to any possible fall in nerve firing would document the presence of presynaptic antagonism of noradrenaline release.

Estimation of neural energy in microelectrode signals

NASA Astrophysics Data System (ADS)

Gaumond, R. P.; Clement, R.; Silva, R.; Sander, D.

2004-09-01

We considered the problem of determining the neural contribution to the signal recorded by an intracortical electrode. We developed a linear least-squares approach to determine the energy fraction of a signal attributable to an arbitrary number of autocorrelation-defined signals buried in noise. Application of the method requires estimation of autocorrelation functions Rap(tgr) characterizing the action potential (AP) waveforms and Rn(tgr) characterizing background noise. This method was applied to the analysis of chronically implanted microelectrode signals from motor cortex of rat. We found that neural (AP) energy consisted of a large-signal component which grows linearly with the number of threshold-detected neural events and a small-signal component unrelated to the count of threshold-detected AP signals. The addition of pseudorandom noise to electrode signals demonstrated the algorithm's effectiveness for a wide range of noise-to-signal energy ratios (0.08 to 39). We suggest, therefore, that the method could be of use in providing a measure of neural response in situations where clearly identified spike waveforms cannot be isolated, or in providing an additional 'background' measure of microelectrode neural activity to supplement the traditional AP spike count.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

PubMed

Erfanian Saeedi, Nafise; Blamey, Peter J; Burkitt, Anthony N; Grayden, David B

2016-04-01

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks

PubMed Central

Erfanian Saeedi, Nafise; Blamey, Peter J.; Burkitt, Anthony N.; Grayden, David B.

2016-01-01

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy. PMID:27049657

Neural crest stem cell multipotency requires Foxd3 to maintain neural potential and repress mesenchymal fates.

PubMed

Mundell, Nathan A; Labosky, Patricia A

2011-02-01

Neural crest (NC) progenitors generate a wide array of cell types, yet molecules controlling NC multipotency and self-renewal and factors mediating cell-intrinsic distinctions between multipotent versus fate-restricted progenitors are poorly understood. Our earlier work demonstrated that Foxd3 is required for maintenance of NC progenitors in the embryo. Here, we show that Foxd3 mediates a fate restriction choice for multipotent NC progenitors with loss of Foxd3 biasing NC toward a mesenchymal fate. Neural derivatives of NC were lost in Foxd3 mutant mouse embryos, whereas abnormally fated NC-derived vascular smooth muscle cells were ectopically located in the aorta. Cranial NC defects were associated with precocious differentiation towards osteoblast and chondrocyte cell fates, and individual mutant NC from different anteroposterior regions underwent fate changes, losing neural and increasing myofibroblast potential. Our results demonstrate that neural potential can be separated from NC multipotency by the action of a single gene, and establish novel parallels between NC and other progenitor populations that depend on this functionally conserved stem cell protein to regulate self-renewal and multipotency.

Evolvable Neural Software System

NASA Technical Reports Server (NTRS)

Curtis, Steven A.

2009-01-01

The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

OpenSim: A Flexible Distributed Neural Network Simulator with Automatic Interactive Graphics.

PubMed

Jarosch, Andreas; Leber, Jean Francois

1997-06-01

An object-oriented simulator called OpenSim is presented that achieves a high degree of flexibility by relying on a small set of building blocks. The state variables and algorithms put in this framework can easily be accessed through a command shell. This allows one to distribute a large-scale simulation over several workstations and to generate the interactive graphics automatically. OpenSim opens new possibilities for cooperation among Neural Network researchers. Copyright 1997 Elsevier Science Ltd.

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

PubMed Central

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

2015-01-01

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

Neural networks distinguish between taste qualities based on receptor cell population responses.

PubMed

Varkevisser, B; Peterson, D; Ogura, T; Kinnamon, S C

2001-06-01

Response features of taste receptor cell action potentials were examined using an artificial neural network to determine whether they contain information about taste quality. Using the loose patch technique to record from hamster taste buds in vivo we recorded population responses of single fungiform papillae to NaCl (100 mM), sucrose (200 mM) and the synthetic sweetener NC-00274-01 (NC-01) (200 microM). Features of each response describing both burst and inter-burst characteristics were then presented to an artificial neural network for pairwise classification of taste stimuli. Responses to NaCl could be distinguished from those to both NC-01 and sucrose with accuracies of up to 86%. In contrast, pairwise comparisons between sucrose and NC-01 were not successful, scoring at chance (50%). Also, comparisons between two different concentrations of NaCl, 0.01 and 0.005 M, scored at chance. Pairwise comparisons using only those features that relate to the inter-burst behavior of the response (i.e. bursting rate) did not hinder the performance of the neural network as both sweeteners versus NaCl received scores of 75--85%. Comparisons using features corresponding to each individual burst scored poorly, receiving scores only slightly above chance. We then compared the sweeteners with varying concentrations of NaCl (0.1, 0.01, 0.005 and 0.001 M) using only those features corresponding to bursting rate within a 1 s time window. The neural network was capable of distinguishing between NaCl and NC-01 at all concentrations tested; while comparisons between NaCl and sucrose received high scores at all concentrations except 0.001 M. These results show that two different taste qualities can be distinguished from each other based solely on the bursting rates of action potentials in single taste buds and that this distinction is independent of stimulation intensity down to 0.001 M NaCl. These data suggest that action potentials in taste receptor cells may play a role in taste quality coding.

Online location of a break in water distribution systems

NASA Astrophysics Data System (ADS)

Liang, Jianwen; Xiao, Di; Zhao, Xinhua; Zhang, Hongwei

2003-08-01

Breaks often occur to urban water distribution systems under severely cold weather, or due to corrosion of pipes, deformation of ground, etc., and the breaks cannot easily be located, especially immediately after the events. This paper develops a methodology to locate a break in a water distribution system by monitoring water pressure online at some nodes in the water distribution system. For the purpose of online monitoring, supervisory control and data acquisition (SCADA) technology can well be used. A neural network-based inverse analysis method is constructed for locating the break based on the variation of water pressure. The neural network is trained by using analytically simulated data from the water distribution system, and validated by using a set of data that have never been used in the training. It is found that the methodology provides a quick, effective, and practical way in which a break in a water distribution system can be located.

Inference in the brain: Statistics flowing in redundant population codes

PubMed Central

Pitkow, Xaq; Angelaki, Dora E

2017-01-01

It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors. PMID:28595050

Neural differences between intrinsic reasons for doing versus extrinsic reasons for doing: an fMRI study

PubMed Central

Lee, Woogul; Reeve, Johnmarshall; Xue, Yiqun; Xiong, Jinhu

2012-01-01

The contemporary neural understanding of motivation is based almost exclusively on the neural mechanisms of incentive motivation. Recognizing this as a limitation, we used event-related functional magnetic resonance imaging (fMRI) to pursue the viability of expanding the neural understanding of motivation by initiating a pioneering study of intrinsic motivation by scanning participants’ neural activity when they decided to act for intrinsic reasons versus when they decided to act for extrinsic reasons. As expected, intrinsic reasons for acting more recruited insular cortex activity while extrinsic reasons for acting more recruited posterior cingulate cortex (PCC) activity. The results demonstrate that engagement decisions based on intrinsic motivation are more determined by weighing the presence of spontaneous self-satisfactions such as interest and enjoyment while engagement decisions based on extrinsic motivation are more determined by weighing socially-acquired stored values as to whether the environmental incentive is attractive enough to warrant action. PMID:23565014

Direct detection of a single evoked action potential with MRS in Lumbricus terrestris.

PubMed

Poplawsky, Alexander J; Dingledine, Raymond; Hu, Xiaoping P

2012-01-01

Functional MRI (fMRI) measures neural activity indirectly by detecting the signal change associated with the hemodynamic response following brain activation. In order to alleviate the temporal and spatial specificity problems associated with fMRI, a number of attempts have been made to detect neural magnetic fields (NMFs) with MRI directly, but have thus far provided conflicting results. In this study, we used MR to detect axonal NMFs in the median giant fiber of the earthworm, Lumbricus terrestris, by examining the free induction decay (FID) with a sampling interval of 0.32 ms. The earthworm nerve cords were isolated from the vasculature and stimulated at the threshold of action potential generation. FIDs were acquired shortly after the stimulation, and simultaneous field potential recordings identified the presence or absence of single evoked action potentials. FIDs acquired when the stimulus did not evoke an action potential were summed as background. The phase of the background-subtracted FID exhibited a systematic change, with a peak phase difference of (-1.2 ± 0.3) × 10(-5) radians occurring at a time corresponding to the timing of the action potential. In addition, we calculated the possible changes in the FID magnitude and phase caused by a simulated action potential using a volume conductor model. The measured phase difference matched the theoretical prediction well in both amplitude and temporal characteristics. This study provides the first evidence for the direct detection of a magnetic field from an evoked action potential using MR. Copyright © 2011 John Wiley & Sons, Ltd.

The neural career of sensory-motor metaphors.

PubMed

Desai, Rutvik H; Binder, Jeffrey R; Conant, Lisa L; Mano, Quintino R; Seidenberg, Mark S

2011-09-01

The role of sensory-motor systems in conceptual understanding has been controversial. It has been proposed that many abstract concepts are understood metaphorically through concrete sensory-motor domains such as actions. Using fMRI, we compared neural responses with literal action (Lit; The daughter grasped the flowers), metaphoric action (Met; The public grasped the idea), and abstract (Abs; The public understood the idea) sentences of varying familiarity. Both Lit and Met sentences activated the left anterior inferior parietal lobule, an area involved in action planning, with Met sentences also activating a homologous area in the right hemisphere, relative to Abs sentences. Both Met and Abs sentences activated the left superior temporal regions associated with abstract language. Importantly, activation in primary motor and biological motion perception regions was inversely correlated with Lit and Met familiarity. These results support the view that the understanding of metaphoric action retains a link to sensory-motor systems involved in action performance. However, the involvement of sensory-motor systems in metaphor understanding changes through a gradual abstraction process whereby relatively detailed simulations are used for understanding unfamiliar metaphors, and these simulations become less detailed and involve only secondary motor regions as familiarity increases. Consistent with these data, we propose that anterior inferior parietal lobule serves as an interface between sensory-motor and conceptual systems and plays an important role in both domains. The similarity of abstract and metaphoric sentences in the activation of left superior temporal regions suggests that action metaphor understanding is not completely based on sensory-motor simulations but relies also on abstract lexical-semantic codes.

The Direct Detection of a Single Evoked Action Potential with Magnetic Resonance Spectroscopy in Lumbricus Terrestris

PubMed Central

Poplawsky, Alexander J.; Dingledine, Raymond

2011-01-01

Functional MRI (fMRI) indirectly measures neural activity by detecting the signal change associated with the hemodynamic response following brain activation. In order to alleviate the temporal and spatial specificity problems associated with fMRI, a number of attempts have been made to detect neural magnetic fields (NMFs) with MRI directly, but have thus far provided conflicting results. In the present study, we used magnetic resonance to detect axonal NMFs in the median giant fiber of the earthworm, Lumbricus terrestris, by examining the free-induction decay (FID) with a sampling interval of 0.32 ms. The earthworm nerve cords were isolated from the vasculature and stimulated at the threshold of action potential generation. FIDs were acquired shortly after the stimulation and simultaneous field potential recordings identified the presence or absence of single evoked action potentials. FIDs acquired when the stimulus did not evoke an action potential were summed as background. The phase of the background-subtracted FID exhibited a systematic change, with a peak phase difference of [-1.2 ± 0.3] ×10-5 radians occurring at a time corresponding to the timing of the action potential. In addition, we calculated the possible changes in the FID magnitude and phase due to a simulated action potential using a volume conductor model. The measured phase difference matched the theoretical prediction well in both amplitude and temporal characteristics. This study provides the first evidence for the direct detection of a magnetic field from an evoked action potential using magnetic resonance. PMID:21728204

Distributed neural control of a hexapod walking vehicle

NASA Technical Reports Server (NTRS)

Beer, R. D.; Sterling, L. S.; Quinn, R. D.; Chiel, H. J.; Ritzmann, R.

1989-01-01

There has been a long standing interest in the design of controllers for multilegged vehicles. The approach is to apply distributed control to this problem, rather than using parallel computing of a centralized algorithm. Researchers describe a distributed neural network controller for hexapod locomotion which is based on the neural control of locomotion in insects. The model considers the simplified kinematics with two degrees of freedom per leg, but the model includes the static stability constraint. Through simulation, it is demonstrated that this controller can generate a continuous range of statically stable gaits at different speeds by varying a single control parameter. In addition, the controller is extremely robust, and can continue the function even after several of its elements have been disabled. Researchers are building a small hexapod robot whose locomotion will be controlled by this network. Researchers intend to extend their model to the dynamic control of legs with more than two degrees of freedom by using data on the control of multisegmented insect legs. Another immediate application of this neural control approach is also exhibited in biology: the escape reflex. Advanced robots are being equipped with tactile sensing and machine vision so that the sensory inputs to the robot controller are vast and complex. Neural networks are ideal for a lower level safety reflex controller because of their extremely fast response time. The combination of robotics, computer modeling, and neurobiology has been remarkably fruitful, and is likely to lead to deeper insights into the problems of real time sensorimotor control.

Modular neuron-based body estimation: maintaining consistency over different limbs, modalities, and frames of reference

PubMed Central

Ehrenfeld, Stephan; Herbort, Oliver; Butz, Martin V.

2013-01-01

This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control. PMID:24191151

Autoplan: A self-processing network model for an extended blocks world planning environment

NASA Technical Reports Server (NTRS)

Dautrechy, C. Lynne; Reggia, James A.; Mcfadden, Frank

1990-01-01

Self-processing network models (neural/connectionist models, marker passing/message passing networks, etc.) are currently undergoing intense investigation for a variety of information processing applications. These models are potentially very powerful in that they support a large amount of explicit parallel processing, and they cleanly integrate high level and low level information processing. However they are currently limited by a lack of understanding of how to apply them effectively in many application areas. The formulation of self-processing network methods for dynamic, reactive planning is studied. The long-term goal is to formulate robust, computationally effective information processing methods for the distributed control of semiautonomous exploration systems, e.g., the Mars Rover. The current research effort is focusing on hierarchical plan generation, execution and revision through local operations in an extended blocks world environment. This scenario involves many challenging features that would be encountered in a real planning and control environment: multiple simultaneous goals, parallel as well as sequential action execution, action sequencing determined not only by goals and their interactions but also by limited resources (e.g., three tasks, two acting agents), need to interpret unanticipated events and react appropriately through replanning, etc.

A 64-channel ultra-low power system-on-chip for local field and action potentials recording

NASA Astrophysics Data System (ADS)

Rodríguez-Pérez, Alberto; Delgado-Restituto, Manuel; Darie, Angela; Soto-Sánchez, Cristina; Fernández-Jover, Eduardo; Rodríguez-Vázquez, Ángel

2015-06-01

This paper reports an integrated 64-channel neural recording sensor. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration mechanism which configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by an embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330μW.

What is the Essence of Hypnosis?

PubMed

Dell, Paul F

2017-01-01

The author explores the nature of hypnosis, which he characterizes as a motivated mode of neural functioning that enables most humans to alter, to varying degrees, their experience of body, self, actions, and world. The essence of hypnosis is not to be found in hetero-hypnosis; instead, it lies in the spontaneous self-activation of that mode of neural functioning. The hypnosis field has substantially lost sight of spontaneous self-activation, because the word hypnosis is usually used to mean hetero-hypnosis. Self-activation of this mode of neural functioning is the necessary sine qua non of hypnotic psychopathology. Moreover, self-activation of trance is the characteristic hypnotic behavior of a distinct subset of highly hypnotizable individuals.

Fault detection in mechanical systems with friction phenomena: an online neural approximation approach.

PubMed

Papadimitropoulos, Adam; Rovithakis, George A; Parisini, Thomas

2007-07-01

In this paper, the problem of fault detection in mechanical systems performing linear motion, under the action of friction phenomena is addressed. The friction effects are modeled through the dynamic LuGre model. The proposed architecture is built upon an online neural network (NN) approximator, which requires only system's position and velocity. The friction internal state is not assumed to be available for measurement. The neural fault detection methodology is analyzed with respect to its robustness and sensitivity properties. Rigorous fault detectability conditions and upper bounds for the detection time are also derived. Extensive simulation results showing the effectiveness of the proposed methodology are provided, including a real case study on an industrial actuator.

Focal versus distributed temporal cortex activity for speech sound category assignment

PubMed Central

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

2018-01-01

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

Performance monitoring in the medial frontal cortex and related neural networks: From monitoring self actions to understanding others' actions.

PubMed

Ninomiya, Taihei; Noritake, Atsushi; Ullsperger, Markus; Isoda, Masaki

2018-04-27

Action is a key channel for interacting with the outer world. As such, the ability to monitor actions and their consequences - regardless as to whether they are self-generated or other-generated - is of crucial importance for adaptive behavior. The medial frontal cortex (MFC) has long been studied as a critical node for performance monitoring in nonsocial contexts. Accumulating evidence suggests that the MFC is involved in a wide range of functions necessary for one's own performance monitoring, including error detection, and monitoring and resolving response conflicts. Recent studies, however, have also pointed to the importance of the MFC in performance monitoring under social conditions, ranging from monitoring and understanding others' actions to reading others' mental states, such as their beliefs and intentions (i.e., mentalizing). Here we review the functional roles of the MFC and related neural networks in performance monitoring in both nonsocial and social contexts, with an emphasis on the emerging field of a social systems neuroscience approach using macaque monkeys as a model system. Future work should determine the way in which the MFC exerts its monitoring function via interactions with other brain regions, such as the superior temporal sulcus in the mentalizing system and the ventral premotor cortex in the mirror system. Copyright © 2018. Published by Elsevier B.V.

Consequences of converting graded to action potentials upon neural information coding and energy efficiency.

PubMed

Sengupta, Biswa; Laughlin, Simon Barry; Niven, Jeremy Edward

2014-01-01

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na(+) and K(+) channels, with generator potential and graded potential models lacking voltage-gated Na(+) channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na(+) channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a 'footprint' in the generator potential that obscures incoming signals. These three processes reduce information rates by ∼50% in generator potentials, to ∼3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation.

Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency

PubMed Central

Sengupta, Biswa; Laughlin, Simon Barry; Niven, Jeremy Edward

2014-01-01

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ∼50% in generator potentials, to ∼3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation. PMID:24465197

Understanding the internal states of others by listening to action verbs.

PubMed

Di Cesare, G; Fasano, F; Errante, A; Marchi, M; Rizzolatti, G

2016-08-01

The internal state of others can be understood observing their actions or listening to their voice. While the neural bases of action style (vitality forms) have been investigated, there is no information on how we recognize others' internal state by listening to their speech. Here, using fMRI technique, we investigated the neural correlates of auditory vitality forms while participants listened to action verbs in three different conditions: human voice pronouncing the verbs in a rude and gentle way, robot voice pronouncing the same verbs without vitality forms, and a scrambled version of the same verbs pronounced by human voice. In agreement with previous studies on vitality forms encoding, we found specific activation of the central part of insula during listening to human voice conveying specific vitality forms. In addition, when listening both to human and robot voices there was an activation of the posterior part of the left inferior frontal gyrus and of the parieto-premotor circuit typically described to be activated during observation and execution of arm actions. Finally, the superior temporal gyrus was activated bilaterally in all three conditions. We conclude that, the central part of insula is a key region for vitality forms processing allowing the understanding of the vitality forms regardless of the modality by which they are conveyed. Copyright © 2016. Published by Elsevier Ltd.

Defunct brain stem cardiovascular regulation underlies cardiovascular collapse associated with methamphetamine intoxication.

PubMed

Li, Faith C H; Yen, J C; Chan, Samuel H H; Chang, Alice Y W

2012-02-07

Intoxication from the psychostimulant methamphetamine (METH) because of cardiovascular collapse is a common cause of death within the abuse population. For obvious reasons, the heart has been taken as the primary target for this METH-induced toxicity. The demonstration that failure of brain stem cardiovascular regulation, rather than the heart, holds the key to cardiovascular collapse induced by the pesticide mevinphos implicates another potential underlying mechanism. The present study evaluated the hypothesis that METH effects acute cardiovascular depression by dampening the functional integrity of baroreflex via an action on brain stem nuclei that are associated with this homeostatic mechanism. The distribution of METH in brain and heart on intravenous administration in male Sprague-Dawley rats, and the resultant changes in arterial pressure (AP), heart rate (HR) and indices for baroreflex-mediated sympathetic vasomotor tone and cardiac responses were evaluated, alongside survival rate and time. Intravenous administration of METH (12 or 24 mg/kg) resulted in a time-dependent and dose-dependent distribution of the psychostimulant in brain and heart. The distribution of METH to neural substrates associated with brain stem cardiovascular regulation was significantly larger than brain targets for its neurological and psychological effects; the concentration of METH in cardiac tissues was the lowest among all tissues studied. In animals that succumbed to METH, the baroreflex-mediated sympathetic vasomotor tone and cardiac response were defunct, concomitant with cessation of AP and HR. On the other hand, although depressed, those two indices in animals that survived were maintained, alongside sustainable AP and HR. Linear regression analysis further revealed that the degree of dampening of brain stem cardiovascular regulation was positively and significantly correlated with the concentration of METH in key neural substrate involved in this homeostatic mechanism. We conclude that on intravenous administration, METH exhibits a preferential distribution to brain stem nuclei that are associated with cardiovascular regulation. We further found that the concentration of METH in those brain stem sites dictates the extent that baroreflex-mediated sympathetic vasomotor tone and cardiac responses are compromised, which in turn determines survival or fatality because of cardiovascular collapse.

Defunct brain stem cardiovascular regulation underlies cardiovascular collapse associated with methamphetamine intoxication

PubMed Central

2012-01-01

Background Intoxication from the psychostimulant methamphetamine (METH) because of cardiovascular collapse is a common cause of death within the abuse population. For obvious reasons, the heart has been taken as the primary target for this METH-induced toxicity. The demonstration that failure of brain stem cardiovascular regulation, rather than the heart, holds the key to cardiovascular collapse induced by the pesticide mevinphos implicates another potential underlying mechanism. The present study evaluated the hypothesis that METH effects acute cardiovascular depression by dampening the functional integrity of baroreflex via an action on brain stem nuclei that are associated with this homeostatic mechanism. Methods The distribution of METH in brain and heart on intravenous administration in male Sprague-Dawley rats, and the resultant changes in arterial pressure (AP), heart rate (HR) and indices for baroreflex-mediated sympathetic vasomotor tone and cardiac responses were evaluated, alongside survival rate and time. Results Intravenous administration of METH (12 or 24 mg/kg) resulted in a time-dependent and dose-dependent distribution of the psychostimulant in brain and heart. The distribution of METH to neural substrates associated with brain stem cardiovascular regulation was significantly larger than brain targets for its neurological and psychological effects; the concentration of METH in cardiac tissues was the lowest among all tissues studied. In animals that succumbed to METH, the baroreflex-mediated sympathetic vasomotor tone and cardiac response were defunct, concomitant with cessation of AP and HR. On the other hand, although depressed, those two indices in animals that survived were maintained, alongside sustainable AP and HR. Linear regression analysis further revealed that the degree of dampening of brain stem cardiovascular regulation was positively and significantly correlated with the concentration of METH in key neural substrate involved in this homeostatic mechanism. Conclusions We conclude that on intravenous administration, METH exhibits a preferential distribution to brain stem nuclei that are associated with cardiovascular regulation. We further found that the concentration of METH in those brain stem sites dictates the extent that baroreflex-mediated sympathetic vasomotor tone and cardiac responses are compromised, which in turn determines survival or fatality because of cardiovascular collapse. PMID:22313577

The evolution of phenotypic correlations and ‘developmental memory’

PubMed Central

Watson, Richard A.; Wagner, Günter P.; Pavlicev, Mihaela; Weinreich, Daniel M.; Mills, Rob

2014-01-01

Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent non-linear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can ‘store’ and ‘recall’ multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and ‘generalise’ (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviours follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well-understood in the context of neural networks. This helps to explain how development facilitates the evolution of high-fitness phenotypes and how this ability changes over evolutionary time. PMID:24351058

Predicting protein complex geometries with a neural network.

PubMed

Chae, Myong-Ho; Krull, Florian; Lorenzen, Stephan; Knapp, Ernst-Walter

2010-03-01

A major challenge of the protein docking problem is to define scoring functions that can distinguish near-native protein complex geometries from a large number of non-native geometries (decoys) generated with noncomplexed protein structures (unbound docking). In this study, we have constructed a neural network that employs the information from atom-pair distance distributions of a large number of decoys to predict protein complex geometries. We found that docking prediction can be significantly improved using two different types of polar hydrogen atoms. To train the neural network, 2000 near-native decoys of even distance distribution were used for each of the 185 considered protein complexes. The neural network normalizes the information from different protein complexes using an additional protein complex identity input neuron for each complex. The parameters of the neural network were determined such that they mimic a scoring funnel in the neighborhood of the native complex structure. The neural network approach avoids the reference state problem, which occurs in deriving knowledge-based energy functions for scoring. We show that a distance-dependent atom pair potential performs much better than a simple atom-pair contact potential. We have compared the performance of our scoring function with other empirical and knowledge-based scoring functions such as ZDOCK 3.0, ZRANK, ITScore-PP, EMPIRE, and RosettaDock. In spite of the simplicity of the method and its functional form, our neural network-based scoring function achieves a reasonable performance in rigid-body unbound docking of proteins. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

With you or against you: social orientation dependent learning signals guide actions made for others.

PubMed

Christopoulos, George I; King-Casas, Brooks

2015-01-01

In social environments, it is crucial that decision-makers take account of the impact of their actions not only for oneself, but also on other social agents. Previous work has identified neural signals in the striatum encoding value-based prediction errors for outcomes to oneself; also, recent work suggests that neural activity in prefrontal cortex may similarly encode value-based prediction errors related to outcomes to others. However, prior work also indicates that social valuations are not isomorphic, with social value orientations of decision-makers ranging on a cooperative to competitive continuum; this variation has not been examined within social learning environments. Here, we combine a computational model of learning with functional neuroimaging to examine how individual differences in orientation impact neural mechanisms underlying 'other-value' learning. Across four experimental conditions, reinforcement learning signals for other-value were identified in medial prefrontal cortex, and were distinct from self-value learning signals identified in striatum. Critically, the magnitude and direction of the other-value learning signal depended strongly on an individual's cooperative or competitive orientation toward others. These data indicate that social decisions are guided by a social orientation-dependent learning system that is computationally similar but anatomically distinct from self-value learning. The sensitivity of the medial prefrontal learning signal to social preferences suggests a mechanism linking such preferences to biases in social actions and highlights the importance of incorporating heterogeneous social predispositions in neurocomputational models of social behavior. Published by Elsevier Inc.

With you or against you: Social orientation dependent learning signals guide actions made for others

PubMed Central

Christopoulos, George I.; King-Casas, Brooks

2014-01-01

In social environments, it is crucial that decision-makers take account of the impact of their actions not only for oneself, but also on other social agents. Previous work has identified neural signals in the striatum encoding value-based prediction errors for outcomes to oneself; also, recent work suggests neural activity in prefrontal cortex may similarly encode value-based prediction errors related to outcomes to others. However, prior work also indicates that social valuations are not isomorphic, with social value orientations of decision-makers ranging on a cooperative to competitive continuum; this variation has not been examined within social learning environments. Here, we combine a computational model of learning with functional neuroimaging to examine how individual differences in orientation impact neural mechanisms underlying ‘other-value’ learning. Across four experimental conditions, reinforcement learning signals for other-value were identified in medial prefrontal cortex, and were distinct from self-value learning signals identified in striatum. Critically, the magnitude and direction of the other-value learning signal depended strongly on an individual’s cooperative or competitive orientation towards others. These data indicate that social decisions are guided by a social orientation-dependent learning system that is computationally similar but anatomically distinct from self-value learning. The sensitivity of the medial prefrontal learning signal to social preferences suggests a mechanism linking such preferences to biases in social actions and highlights the importance of incorporating heterogeneous social predispositions in neurocomputational models of social behavior. PMID:25224998

Using Arrays of Microelectrodes Implanted in Residual Peripheral Nerves to Provide Dexterous Control of, and Modulated Sensory Feedback from, a Hand Prosthesis

DTIC Science & Technology

2017-10-01

potentials or multi-action potential activity from residual peripheral nerve while patient intends movements of amputated hand/arm Subtask 3.1: Mapping of...neural activity (Months 4 – 36) • Patients will be asked to intend a number of individual finger and multiple finger flexion, extension, adduction...intended movements. We will map the different intended movements onto the neural activity recorded on the electrodes of the micro-electrode array

ANNS An X Window Based Version of the AFIT Neural Network Simulator

DTIC Science & Technology

1993-06-01

programer or user can view the dy- namic behavior of an algorithm and its changes of learning state while the neural network paradigms or algorithms...an object as "something you can do things to. An object has state, behavior , and identity, the structure and behavior of similar objects are defined in...their common class. The terms instance and object are interchangeable" [5:516]. The behavior of an object is "characterized by the actions that it

Contextual Processing of Abstract Concepts Reveals Neural Representations of Non-Linguistic Semantic Content

PubMed Central

Wilson-Mendenhall, Christine D.; Simmons, W. Kyle; Martin, Alex; Barsalou, Lawrence W.

2014-01-01

Concepts develop for many aspects of experience, including abstract internal states and abstract social activities that do not refer to concrete entities in the world. The current study assessed the hypothesis that, like concrete concepts, distributed neural patterns of relevant, non-linguistic semantic content represent the meanings of abstract concepts. In a novel neuroimaging paradigm, participants processed two abstract concepts (convince, arithmetic) and two concrete concepts (rolling, red) deeply and repeatedly during a concept-scene matching task that grounded each concept in typical contexts. Using a catch trial design, neural activity associated with each concept word was separated from neural activity associated with subsequent visual scenes to assess activations underlying the detailed semantics of each concept. We predicted that brain regions underlying mentalizing and social cognition (e.g., medial prefrontal cortex, superior temporal sulcus) would become active to represent semantic content central to convince, whereas brain regions underlying numerical cognition (e.g., bilateral intraparietal sulcus) would become active to represent semantic content central to arithmetic. The results supported these predictions, suggesting that the meanings of abstract concepts arise from distributed neural systems that represent concept-specific content. PMID:23363408

How to include the variability of TMS responses in simulations: a speech mapping case study

NASA Astrophysics Data System (ADS)

De Geeter, N.; Lioumis, P.; Laakso, A.; Crevecoeur, G.; Dupré, L.

2016-11-01

When delivered over a specific cortical site, TMS can temporarily disrupt the ongoing process in that area. This allows mapping of speech-related areas for preoperative evaluation purposes. We numerically explore the observed variability of TMS responses during a speech mapping experiment performed with a neuronavigation system. We selected four cases with very small perturbations in coil position and orientation. In one case (E) a naming error occurred, while in the other cases (NEA, B, C) the subject appointed the images as smoothly as without TMS. A realistic anisotropic head model was constructed of the subject from T1-weighted and diffusion-weighted MRI. The induced electric field distributions were computed, associated to the coil parameters retrieved from the neuronavigation system. Finally, the membrane potentials along relevant white matter fibre tracts, extracted from DTI-based tractography, were computed using a compartmental cable equation. While only minor differences could be noticed between the induced electric field distributions of the four cases, computing the corresponding membrane potentials revealed different subsets of tracts were activated. A single tract was activated for all coil positions. Another tract was only triggered for case E. NEA induced action potentials in 13 tracts, while NEB stimulated 11 tracts and NEC one. The calculated results are certainly sensitive to the coil specifications, demonstrating the observed variability in this study. However, even though a tract connecting Broca’s with Wernicke’s area is only triggered for the error case, further research is needed on other study cases and on refining the neural model with synapses and network connections. Case- and subject-specific modelling that includes both electromagnetic fields and neuronal activity enables demonstration of the variability in TMS experiments and can capture the interaction with complex neural networks.

Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches.

PubMed

Zounemat-Kermani, Mohammad; Ramezani-Charmahineh, Abdollah; Adamowski, Jan; Kisi, Ozgur

2018-06-13

Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R 2 , and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.

Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays.

PubMed

Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

2017-01-01

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

Synchrotron Radiation X-Ray Microfluorescence Reveals Polarized Distribution of Atomic Elements during Differentiation of Pluripotent Stem Cells

PubMed Central

Paulsen, Bruna S.; Rehen, Stevens K.

2011-01-01

The mechanisms underlying pluripotency and differentiation in embryonic and reprogrammed stem cells are unclear. In this work, we characterized the pluripotent state towards neural differentiated state through analysis of trace elements distribution using the Synchrotron Radiation X-ray Fluorescence Spectroscopy. Naive and neural-stimulated embryoid bodies (EB) derived from embryonic and induced pluripotent stem (ES and iPS) cells were irradiated with a spatial resolution of 20 µm to make elemental maps and qualitative chemical analyses. Results show that these embryo-like aggregates exhibit self-organization at the atomic level. Metallic elements content rises and consistent elemental polarization pattern of P and S in both mouse and human pluripotent stem cells were observed, indicating that neural differentiation and elemental polarization are strongly correlated. PMID:22195032

Direct imaging of neural currents using ultra-low field magnetic resonance techniques

DOEpatents

Volegov, Petr L [Los Alamos, NM; Matlashov, Andrei N [Los Alamos, NM; Mosher, John C [Los Alamos, NM; Espy, Michelle A [Los Alamos, NM; Kraus, Jr., Robert H.

2009-08-11

Using resonant interactions to directly and tomographically image neural activity in the human brain using magnetic resonance imaging (MRI) techniques at ultra-low field (ULF), the present inventors have established an approach that is sensitive to magnetic field distributions local to the spin population in cortex at the Larmor frequency of the measurement field. Because the Larmor frequency can be readily manipulated (through varying B.sub.m), one can also envision using ULF-DNI to image the frequency distribution of the local fields in cortex. Such information, taken together with simultaneous acquisition of MEG and ULF-NMR signals, enables non-invasive exploration of the correlation between local fields induced by neural activity in cortex and more `distant` measures of brain activity such as MEG and EEG.

Path integrals and large deviations in stochastic hybrid systems.

PubMed

Bressloff, Paul C; Newby, Jay M

2014-04-01

We construct a path-integral representation of solutions to a stochastic hybrid system, consisting of one or more continuous variables evolving according to a piecewise-deterministic dynamics. The differential equations for the continuous variables are coupled to a set of discrete variables that satisfy a continuous-time Markov process, which means that the differential equations are only valid between jumps in the discrete variables. Examples of stochastic hybrid systems arise in biophysical models of stochastic ion channels, motor-driven intracellular transport, gene networks, and stochastic neural networks. We use the path-integral representation to derive a large deviation action principle for a stochastic hybrid system. Minimizing the associated action functional with respect to the set of all trajectories emanating from a metastable state (assuming that such a minimization scheme exists) then determines the most probable paths of escape. Moreover, evaluating the action functional along a most probable path generates the so-called quasipotential used in the calculation of mean first passage times. We illustrate the theory by considering the optimal paths of escape from a metastable state in a bistable neural network.

Effects of action observation therapy and mirror therapy after stroke on rehabilitation outcomes and neural mechanisms by MEG: study protocol for a randomized controlled trial.

PubMed

Shih, Tsai-Yu; Wu, Ching-Yi; Lin, Keh-Chung; Cheng, Chia-Hsiung; Hsieh, Yu-Wei; Chen, Chia-Ling; Lai, Chih-Jou; Chen, Chih-Chi

2017-10-04

Loss of upper-extremity motor function is one of the most debilitating deficits following stroke. Two promising treatment approaches, action observation therapy (AOT) and mirror therapy (MT), aim to enhance motor learning and promote neural reorganization in patients through different afferent inputs and patterns of visual feedback. Both approaches involve different patterns of motor observation, imitation, and execution but share some similar neural bases of the mirror neuron system. AOT and MT used in stroke rehabilitation may confer differential benefits and neural activities that remain to be determined. This clinical trial aims to investigate and compare treatment effects and neural activity changes of AOT and MT with those of the control intervention in patients with subacute stroke. An estimated total of 90 patients with subacute stroke will be recruited for this study. All participants will be randomly assigned to receive AOT, MT, or control intervention for a 3-week training period (15 sessions). Outcome measurements will be taken at baseline, immediately after treatment, and at the 3-month follow-up. For the magnetoencephalography (MEG) study, we anticipate that we will recruit 12 to 15 patients per group. The primary outcome will be the Fugl-Meyer Assessment score. Secondary outcomes will include the modified Rankin Scale, the Box and Block Test, the ABILHAND questionnaire, the Questionnaire Upon Mental Imagery, the Functional Independence Measure, activity monitors, the Stroke Impact Scale version 3.0, and MEG signals. This clinical trial will provide scientific evidence of treatment effects on motor, functional outcomes, and neural activity mechanisms after AOT and MT in patients with subacute stroke. Further application and use of AOT and MT may include telerehabilitation or home-based rehabilitation through web-based or video teaching. ClinicalTrials.gov, ID: NCT02871700 . Registered on 1 August 2016.

Optical alignment procedure utilizing neural networks combined with Shack-Hartmann wavefront sensor

NASA Astrophysics Data System (ADS)

Adil, Fatime Zehra; Konukseven, Erhan İlhan; Balkan, Tuna; Adil, Ömer Faruk

2017-05-01

In the design of pilot helmets with night vision capability, to not limit or block the sight of the pilot, a transparent visor is used. The reflected image from the coated part of the visor must coincide with the physical human sight image seen through the nonreflecting regions of the visor. This makes the alignment of the visor halves critical. In essence, this is an alignment problem of two optical parts that are assembled together during the manufacturing process. Shack-Hartmann wavefront sensor is commonly used for the determination of the misalignments through wavefront measurements, which are quantified in terms of the Zernike polynomials. Although the Zernike polynomials provide very useful feedback about the misalignments, the corrective actions are basically ad hoc. This stems from the fact that there exists no easy inverse relation between the misalignment measurements and the physical causes of the misalignments. This study aims to construct this inverse relation by making use of the expressive power of the neural networks in such complex relations. For this purpose, a neural network is designed and trained in MATLAB® regarding which types of misalignments result in which wavefront measurements, quantitatively given by Zernike polynomials. This way, manual and iterative alignment processes relying on trial and error will be replaced by the trained guesses of a neural network, so the alignment process is reduced to applying the counter actions based on the misalignment causes. Such a training requires data containing misalignment and measurement sets in fine detail, which is hard to obtain manually on a physical setup. For that reason, the optical setup is completely modeled in Zemax® software, and Zernike polynomials are generated for misalignments applied in small steps. The performance of the neural network is experimented and found promising in the actual physical setup.

Rapid Improvement in Visual Selective Attention Related to Action Video Gaming Experience.

PubMed

Qiu, Nan; Ma, Weiyi; Fan, Xin; Zhang, Youjin; Li, Yi; Yan, Yuening; Zhou, Zhongliang; Li, Fali; Gong, Diankun; Yao, Dezhong

2018-01-01

A central issue in cognitive science is understanding how learning induces cognitive and neural plasticity, which helps illuminate the biological basis of learning. Research in the past few decades showed that action video gaming (AVG) offered new, important perspectives on learning-related cognitive and neural plasticity. However, it is still unclear whether cognitive and neural plasticity is observable after a brief AVG session. Using behavioral and electrophysiological measures, this study examined the plasticity of visual selective attention (VSA) associated with a 1 h AVG session. Both AVG experts and non-experts participated in this study. Their VSA was assessed prior to and after the AVG session. Within-group comparisons on the participants' performance before and after the AVG session showed improvements in response time in both groups and modulations of electrophysiological measures in the non-experts. Furthermore, between-group comparisons showed that the experts had superior VSA, relative to the non-experts, prior to the AVG session. These findings suggested an association between the plasticity of VSA and AVG. Most importantly, this study showed that the plasticity of VSA was observable after even a 1 h AVG session.

Altered cingulo-striatal function underlies reward drive deficits in schizophrenia.

PubMed

Park, Il Ho; Chun, Ji Won; Park, Hae-Jeong; Koo, Min-Seong; Park, Sunyoung; Kim, Seok-Hyeong; Kim, Jae-Jin

2015-02-01

Amotivation in schizophrenia is assumed to involve dysfunctional dopaminergic signaling of reward prediction or anticipation. It is unclear, however, whether the translation of neural representation of reward value to behavioral drive is affected in schizophrenia. In order to examine how abnormal neural processing of response valuation and initiation affects incentive motivation in schizophrenia, we conducted functional MRI using a deterministic reinforcement learning task with variable intervals of contingency reversals in 20 clinically stable patients with schizophrenia and 20 healthy controls. Behaviorally, the advantage of positive over negative reinforcer in reinforcement-related responsiveness was not observed in patients. Patients showed altered response valuation and initiation-related striatal activity and deficient rostro-ventral anterior cingulate cortex activation during reward approach initiation. Among these neural abnormalities, rostro-ventral anterior cingulate cortex activation was correlated with positive reinforcement-related responsiveness in controls and social anhedonia and social amotivation subdomain scores in patients. Our findings indicate that the central role of the anterior cingulate cortex is in translating action value into driving force of action, and underscore the role of the cingulo-striatal network in amotivation in schizophrenia. Copyright © 2014 Elsevier B.V. All rights reserved.

Prefrontal neural correlates of memory for sequences.

PubMed

Averbeck, Bruno B; Lee, Daeyeol

2007-02-28

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

Games people play-toward an enactive view of cooperation in social neuroscience.

PubMed

Engemann, Denis A; Bzdok, Danilo; Eickhoff, Simon B; Vogeley, Kai; Schilbach, Leonhard

2012-01-01

The field of social neuroscience has made considerable progress in unraveling the neural correlates of human cooperation by making use of brain imaging methods. Within this field, neuroeconomic research has drawn on paradigms from experimental economics, such as the Prisoner's Dilemma (PD) and the Trust Game. These paradigms capture the topic of conflict in cooperation, while focusing strongly on outcome-related decision processes. Cooperation, however, does not equate with that perspective, but relies on additional psychological processes and events, including shared intentions and mutually coordinated joint action. These additional facets of cooperation have been successfully addressed by research in developmental psychology, cognitive science, and social philosophy. Corresponding neuroimaging data, however, is still sparse. Therefore, in this paper, we present a juxtaposition of these mutually related but mostly independent trends in cooperation research. We propose that the neuroscientific study of cooperation could benefit from paradigms and concepts employed in developmental psychology and social philosophy. Bringing both to a neuroimaging environment might allow studying the neural correlates of cooperation by using formal models of decision-making as well as capturing the neural responses that underlie joint action scenarios, thus, promising to advance our understanding of the nature of human cooperation.

Games people play—toward an enactive view of cooperation in social neuroscience

PubMed Central

Engemann, Denis A.; Bzdok, Danilo; Eickhoff, Simon B.; Vogeley, Kai; Schilbach, Leonhard

2012-01-01

The field of social neuroscience has made considerable progress in unraveling the neural correlates of human cooperation by making use of brain imaging methods. Within this field, neuroeconomic research has drawn on paradigms from experimental economics, such as the Prisoner's Dilemma (PD) and the Trust Game. These paradigms capture the topic of conflict in cooperation, while focusing strongly on outcome-related decision processes. Cooperation, however, does not equate with that perspective, but relies on additional psychological processes and events, including shared intentions and mutually coordinated joint action. These additional facets of cooperation have been successfully addressed by research in developmental psychology, cognitive science, and social philosophy. Corresponding neuroimaging data, however, is still sparse. Therefore, in this paper, we present a juxtaposition of these mutually related but mostly independent trends in cooperation research. We propose that the neuroscientific study of cooperation could benefit from paradigms and concepts employed in developmental psychology and social philosophy. Bringing both to a neuroimaging environment might allow studying the neural correlates of cooperation by using formal models of decision-making as well as capturing the neural responses that underlie joint action scenarios, thus, promising to advance our understanding of the nature of human cooperation. PMID:22675293

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

PubMed

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

2012-01-01

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

Rapid Improvement in Visual Selective Attention Related to Action Video Gaming Experience

PubMed Central

Qiu, Nan; Ma, Weiyi; Fan, Xin; Zhang, Youjin; Li, Yi; Yan, Yuening; Zhou, Zhongliang; Li, Fali; Gong, Diankun; Yao, Dezhong

2018-01-01

A central issue in cognitive science is understanding how learning induces cognitive and neural plasticity, which helps illuminate the biological basis of learning. Research in the past few decades showed that action video gaming (AVG) offered new, important perspectives on learning-related cognitive and neural plasticity. However, it is still unclear whether cognitive and neural plasticity is observable after a brief AVG session. Using behavioral and electrophysiological measures, this study examined the plasticity of visual selective attention (VSA) associated with a 1 h AVG session. Both AVG experts and non-experts participated in this study. Their VSA was assessed prior to and after the AVG session. Within-group comparisons on the participants' performance before and after the AVG session showed improvements in response time in both groups and modulations of electrophysiological measures in the non-experts. Furthermore, between-group comparisons showed that the experts had superior VSA, relative to the non-experts, prior to the AVG session. These findings suggested an association between the plasticity of VSA and AVG. Most importantly, this study showed that the plasticity of VSA was observable after even a 1 h AVG session. PMID:29487514

Online monitoring of seismic damage in water distribution systems

NASA Astrophysics Data System (ADS)

Liang, Jianwen; Xiao, Di; Zhao, Xinhua; Zhang, Hongwei

2004-07-01

It is shown that water distribution systems can be damaged by earthquakes, and the seismic damages cannot easily be located, especially immediately after the events. Earthquake experiences show that accurate and quick location of seismic damage is critical to emergency response of water distribution systems. This paper develops a methodology to locate seismic damage -- multiple breaks in a water distribution system by monitoring water pressure online at limited positions in the water distribution system. For the purpose of online monitoring, supervisory control and data acquisition (SCADA) technology can well be used. A neural network-based inverse analysis method is constructed for locating the seismic damage based on the variation of water pressure. The neural network is trained by using analytically simulated data from the water distribution system, and validated by using a set of data that have never been used in the training. It is found that the methodology provides an effective and practical way in which seismic damage in a water distribution system can be accurately and quickly located.

Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP based neural systems.

PubMed

Parrado-Hernández, Emilio; Gómez-Sánchez, Eduardo; Dimitriadis, Yannis A

2003-09-01

An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out, from both qualitative and quantitative points of view. The study involves two original winner-take-all (WTA) architectures, Fuzzy ARTMAP and FasArt, and their distributed versions, dARTMAP and dFasArt. A qualitative analysis of the distributed learning properties of dARTMAP is made, focusing on the new elements introduced to endow Fuzzy ARTMAP with distributed learning. In addition, a quantitative study on a selected set of classification problems points out that problems have to present certain features in their output classes in order to noticeably reduce the number of recruited categories and achieve an acceptable classification accuracy. As part of this analysis, distributed learning was successfully adapted to a member of the Fuzzy ARTMAP family, FasArt, and similar procedures can be used to extend distributed learning capabilities to other Fuzzy ARTMAP based systems.

A Neural Basis of Facial Action Recognition in Humans

PubMed Central

Srinivasan, Ramprakash; Golomb, Julie D.

2016-01-01

By combining different facial muscle actions, called action units, humans can produce an extraordinarily large number of facial expressions. Computational models and studies in cognitive science and social psychology have long hypothesized that the brain needs to visually interpret these action units to understand other people's actions and intentions. Surprisingly, no studies have identified the neural basis of the visual recognition of these action units. Here, using functional magnetic resonance imaging and an innovative machine learning analysis approach, we identify a consistent and differential coding of action units in the brain. Crucially, in a brain region thought to be responsible for the processing of changeable aspects of the face, multivoxel pattern analysis could decode the presence of specific action units in an image. This coding was found to be consistent across people, facilitating the estimation of the perceived action units on participants not used to train the multivoxel decoder. Furthermore, this coding of action units was identified when participants attended to the emotion category of the facial expression, suggesting an interaction between the visual analysis of action units and emotion categorization as predicted by the computational models mentioned above. These results provide the first evidence for a representation of action units in the brain and suggest a mechanism for the analysis of large numbers of facial actions and a loss of this capacity in psychopathologies. SIGNIFICANCE STATEMENT Computational models and studies in cognitive and social psychology propound that visual recognition of facial expressions requires an intermediate step to identify visible facial changes caused by the movement of specific facial muscles. Because facial expressions are indeed created by moving one's facial muscles, it is logical to assume that our visual system solves this inverse problem. Here, using an innovative machine learning method and neuroimaging data, we identify for the first time a brain region responsible for the recognition of actions associated with specific facial muscles. Furthermore, this representation is preserved across subjects. Our machine learning analysis does not require mapping the data to a standard brain and may serve as an alternative to hyperalignment. PMID:27098688

Controlling selective stimulations below a spinal cord hemisection using brain recordings with a neural interface system approach

NASA Astrophysics Data System (ADS)

Panetsos, Fivos; Sanchez-Jimenez, Abel; Torets, Carlos; Largo, Carla; Micera, Silvestro

2011-08-01

In this work we address the use of realtime cortical recordings for the generation of coherent, reliable and robust motor activity in spinal-lesioned animals through selective intraspinal microstimulation (ISMS). The spinal cord of adult rats was hemisectioned and groups of multielectrodes were implanted in both the central nervous system (CNS) and the spinal cord below the lesion level to establish a neural system interface (NSI). To test the reliability of this new NSI connection, highly repeatable neural responses recorded from the CNS were used as a pattern generator of an open-loop control strategy for selective ISMS of the spinal motoneurons. Our experimental procedure avoided the spontaneous non-controlled and non-repeatable neural activity that could have generated spurious ISMS and the consequent undesired muscle contractions. Combinations of complex CNS patterns generated precisely coordinated, reliable and robust motor actions.