scholarly journals Reproducing Human Motor Adaptation in Spiking Neural Simulation and known Synaptic Learning Rules

2021 ◽  
Author(s):  
Yufei Wu ◽  
Shlomi Haar ◽  
Aldo A Faisal
Keyword(s):  
Neural Circuit ◽  
Cerebellar Nuclei ◽  
Learning Curves ◽  
Sensorimotor Adaptation ◽  
Learning Mechanisms ◽  
Neural Basis ◽  
Predictive Learning ◽  
Neuron Populations ◽  
Behavioural Experiments ◽  
High Level

Sensorimotor adaptation enables us to adjust our goal-oriented movements in response to external perturbations. These phenomena have been studied experimentally and computationally at the level of human and animals reaching movements, and have clear links to the cerebellum as evidenced by cerebellar lesions and neurodegeneration. Yet, despite our macroscopic understanding of the high-level computational mechanisms it is unclear how these are mapped and are implemented in the neural substrates of the cerebellum at a cellular-computational level. We present here a novel spiking neural circuit model of the sensorimotor system including a cerebellum which control physiological muscle models to reproduce behaviour experiments. Our cerebellar model is composed of spiking neuron populations reflecting cells in the cerebellar cortex and deep cerebellar nuclei, which generate motor correction to change behaviour in response to perturbations. The model proposes two learning mechanisms for adaptation: predictive learning and memory formation, which are implemented with synaptic updating rules. Our model is tested in a force-field sensorimotor adaptation task and successfully reproduce several phenomena arising from human adaptation, including well-known learning curves, aftereffects, savings and other multi-rate learning effects. This reveals the capability of our model to learn from perturbations and generate motor corrections while providing a bottom-up view for the neural basis of adaptation. Thus, it also shows the potential to predict how patients with specific types of cerebellar damage will perform in behavioural experiments. We explore this by in silico experiments where we selectively incapacitate selected cerebellar circuits of the model which generate and reproduce defined motor learning deficits.

scholarly journals Effects of behavioural activation on the neural circuit related to intrinsic motivation

BJPsych Open ◽  
2018 ◽  
Vol 4 (5) ◽  
pp. 317-323 ◽  
Author(s):  
Asako Mori ◽  
Yasumasa Okamoto ◽  
Go Okada ◽  
Koki Takagaki ◽  
Masahiro Takamura ◽  
...  
Keyword(s):  
Intrinsic Motivation ◽  
Neural Circuit ◽  
Intervention Group ◽  
Behavioural Activation ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Neural Basis ◽  
Efficient Treatment ◽  
The Right ◽  
Magnetic Resonance Imaging Scanning

BackgroundBehavioural activation is an efficient treatment for depression and can improve intrinsic motivation. Previous studies have revealed that the frontostriatal circuit is involved in intrinsic motivation; however, there are no data on how behavioural activation affects the frontostriatal circuit.AimsWe aimed to investigate behavioural activation-related changes in the frontostriatal circuit.MethodFifty-nine individuals with subthreshold depression were randomly assigned to either the intervention or non-intervention group. The intervention group received five weekly behavioural activation sessions. The participants underwent functional magnetic resonance imaging scanning on two separate occasions while performing a stopwatch task based on intrinsic motivation. We investigated changes in neural activity and functional connectivity after behavioural activation.ResultsAfter behavioural activation, the intervention group had increased activation and connectivity in the frontostriatal region compared with the non-intervention group. The increased activation in the right middle frontal gyrus was correlated with an improvement of subjective sensitivity to environmental rewards.ConclusionsBehavioural activation-related changes to the frontostriatal circuit advance our understanding of psychotherapy-induced improvements in the neural basis of intrinsic motivation.Declaration of interestNone.

scholarly journals A visual circuit uses complementary mechanisms to support transient and sustained pupil constriction

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
William Thomas Keenan ◽  
Alan C Rupp ◽  
Rachel A Ross ◽  
Preethi Somasundaram ◽  
Suja Hiriyanna ◽  
...  
Keyword(s):  
Neural Coding ◽  
Pupil Size ◽  
Neural Circuit ◽  
Ganglion Cells ◽  
Retinal Ganglion ◽  
Neural Basis ◽  
Behavioral Dynamics ◽  
Pupil Constriction ◽  
Sustained Responses ◽  
Stable Control

Rapid and stable control of pupil size in response to light is critical for vision, but the neural coding mechanisms remain unclear. Here, we investigated the neural basis of pupil control by monitoring pupil size across time while manipulating each photoreceptor input or neurotransmitter output of intrinsically photosensitive retinal ganglion cells (ipRGCs), a critical relay in the control of pupil size. We show that transient and sustained pupil responses are mediated by distinct photoreceptors and neurotransmitters. Transient responses utilize input from rod photoreceptors and output by the classical neurotransmitter glutamate, but adapt within minutes. In contrast, sustained responses are dominated by non-conventional signaling mechanisms: melanopsin phototransduction in ipRGCs and output by the neuropeptide PACAP, which provide stable pupil maintenance across the day. These results highlight a temporal switch in the coding mechanisms of a neural circuit to support proper behavioral dynamics.

scholarly journals Rational metareasoning and the plasticity of cognitive control

2017 ◽  
Author(s):  
Falk Lieder ◽  
Amitai Shenhav ◽  
Sebastian Musslick ◽  
Tom Griffiths
Keyword(s):  
Cognitive Control ◽  
Self Control ◽  
Neural Pathways ◽  
Interference Control ◽  
Learning Mechanisms ◽  
Control Failure ◽  
Stimulus Features ◽  
High Level ◽  
Computational Resources ◽  
And Behavior

The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.

scholarly journals Complex dynamics of choice during operant extinction and a “simple” associative learning account

2020 ◽  
Author(s):  
José R. Donoso ◽  
Julian Packheiser ◽  
Roland Pusch ◽  
Zhiyin Lederer ◽  
Thomas Walther ◽  
...  
Keyword(s):  
Computational Model ◽  
Associative Learning ◽  
Operant Conditioning ◽  
Complex Dynamics ◽  
Rule Learning ◽  
Learning Curves ◽  
Learning Mechanisms ◽  
Learning Session ◽  
Extinction Learning ◽  
Operant Extinction

AbstractExtinction learning, the process of ceasing an acquired behavior in response to altered reinforcement contingencies, is essential for survival in a changing environment. So far, research has mostly neglected the learning dynamics and variability of behavior during extinction learning and instead focused on a few response types that were studied by population averages. Here, we take a different approach by analyzing the trial-by-trial dynamics of operant extinction learning in both pigeons and a computational model. The task involved discriminant operant conditioning in context A, extinction in context B, and a return to context A to test the context-dependent return of the conditioned response (ABA renewal). By studying single learning curves across animals under repeated sessions of this paradigm, we uncovered a rich variability of behavior during extinction learning: (1) Pigeons prefer the unrewarded alternative choice in one-third of the sessions, predominantly during the very first extinction session an animal encountered. (2) In later sessions, abrupt transitions of behavior at the onset of context B emerge, and (3) the renewal effect decays as sessions progress. While these results could be interpreted in terms of rule learning mechanisms, we show that they can be parsimoniously accounted for by a computational model based only on associative learning between stimuli and actions. Our work thus demonstrates the critical importance of studying the trial-by-trial dynamics of learning in individual sessions, and the unexpected power of “simple” associative learning processes.Significance StatementOperant conditioning is essential for the discovery of purposeful actions, but once a stimulus-response association is acquired, the ability to extinguish it in response to altered reward contingencies is equally important. These processes also play a fundamental role in the development and treatment of pathological behaviors such as drug addiction, overeating and gambling. Here we show that extinction learning is not limited to the cessation of a previously reinforced response, but also drives the emergence of complex and variable choices that change from learning session to learning session. At first sight, these behavioral changes appear to reflect abstract rule learning, but we show in a computational model that they can emerge from “simple” associative learning.

scholarly journals Human frontoparietal cortex represents behaviorally relevant target status based on abstract object features

2019 ◽  
Vol 121 (4) ◽  
pp. 1410-1427 ◽  
Author(s):  
Margaret Henderson ◽  
John T. Serences
Keyword(s):  
Frontal Cortex ◽  
Functional Mri ◽  
Irrelevant Dimension ◽  
Target Recognition ◽  
Human Subjects ◽  
Object Identity ◽  
Neural Basis ◽  
Related Information ◽  
Parking Lot ◽  
High Level

Searching for items that are useful given current goals, or “target” recognition, requires observers to flexibly attend to certain object properties at the expense of others. This could involve focusing on the identity of an object while ignoring identity-preserving transformations such as changes in viewpoint or focusing on its current viewpoint while ignoring its identity. To effectively filter out variation due to the irrelevant dimension, performing either type of task is likely to require high-level, abstract search templates. Past work has found target recognition signals in areas of ventral visual cortex and in subregions of parietal and frontal cortex. However, target status in these tasks is typically associated with the identity of an object, rather than identity-orthogonal properties such as object viewpoint. In this study, we used a task that required subjects to identify novel object stimuli as targets according to either identity or viewpoint, each of which was not predictable from low-level properties such as shape. We performed functional MRI in human subjects of both sexes and measured the strength of target-match signals in areas of visual, parietal, and frontal cortex. Our multivariate analyses suggest that the multiple-demand (MD) network, including subregions of parietal and frontal cortex, encodes information about an object’s status as a target in the relevant dimension only, across changes in the irrelevant dimension. Furthermore, there was more target-related information in MD regions on correct compared with incorrect trials, suggesting a strong link between MD target signals and behavior. NEW & NOTEWORTHY Real-world target detection tasks, such as searching for a car in a crowded parking lot, require both flexibility and abstraction. We investigated the neural basis of these abilities using a task that required invariant representations of either object identity or viewpoint. Multivariate decoding analyses of our whole brain functional MRI data reveal that invariant target representations are most pronounced in frontal and parietal regions, and the strength of these representations is associated with behavioral performance.

scholarly journals The observant mind: self-awareness of attentional status

2010 ◽  
Vol 277 (1699) ◽  
pp. 3421-3426 ◽  
Author(s):  
Noriko Yamagishi ◽  
Stephen J. Anderson ◽  
Mitsuo Kawato
Keyword(s):  
Threshold Model ◽  
Discrimination Performance ◽  
Attentional Focus ◽  
Orientation Discrimination ◽  
Self Awareness ◽  
Neural Basis ◽  
Attentional Processes ◽  
Delay Times ◽  
Linear Rise ◽  
High Level

Visual perception is dependent not only on low-level sensory input but also on high-level cognitive factors such as attention. In this paper, we sought to determine whether attentional processes can be internally monitored for the purpose of enhancing behavioural performance. To do so, we developed a novel paradigm involving an orientation discrimination task in which observers had the freedom to delay target presentation—by any amount required—until they judged their attentional focus to be complete. Our results show that discrimination performance is significantly improved when individuals self-monitor their level of visual attention and respond only when they perceive it to be maximal. Although target delay times varied widely from trial-to-trial (range 860 ms–12.84 s), we show that their distribution is Gaussian when plotted on a reciprocal latency scale. We further show that the neural basis of the delay times for judging attentional status is well explained by a linear rise-to-threshold model. We conclude that attentional mechanisms can be self-monitored for the purpose of enhancing human decision-making processes, and that the neural basis of such processes can be understood in terms of a simple, yet broadly applicable, linear rise-to-threshold model.

scholarly journals Neural Correlates of Group Bias During Natural Viewing

2018 ◽  
Vol 29 (8) ◽  
pp. 3380-3389
Author(s):  
Timothy J Andrews ◽  
Ryan K Smith ◽  
Richard L Hoggart ◽  
Philip I N Ulrich ◽  
Andre D Gouws
Keyword(s):  
Time Course ◽  
Social Groups ◽  
Sensory Information ◽  
Brain Regions ◽  
Group Differences ◽  
Neural Basis ◽  
Brain Responses ◽  
The World ◽  
High Level ◽  
The Brain

Abstract Individuals from different social groups interpret the world in different ways. This study explores the neural basis of these group differences using a paradigm that simulates natural viewing conditions. Our aim was to determine if group differences could be found in sensory regions involved in the perception of the world or were evident in higher-level regions that are important for the interpretation of sensory information. We measured brain responses from 2 groups of football supporters, while they watched a video of matches between their teams. The time-course of response was then compared between individuals supporting the same (within-group) or the different (between-group) team. We found high intersubject correlations in low-level and high-level regions of the visual brain. However, these regions of the brain did not show any group differences. Regions that showed higher correlations for individuals from the same group were found in a network of frontal and subcortical brain regions. The interplay between these regions suggests a range of cognitive processes from motor control to social cognition and reward are important in the establishment of social groups. These results suggest that group differences are primarily reflected in regions involved in the evaluation and interpretation of the sensory input.

ADVANCED IMAGE PROCESSING CELLULAR NEURAL NETWORKS

2007 ◽  
Vol 17 (04) ◽  
pp. 1109-1150 ◽  
Author(s):  
MAKOTO ITOH ◽  
LEON O. CHUA
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Networks ◽  
Visual Illusions ◽  
Cognitive Engineering ◽  
Neural Basis ◽  
Brain Functions ◽  
Texture Generation ◽  
High Level ◽  
Neural Field Models

Many useful and well-known image processing templates for cellular neural networks (CNN's) can be derived from neural field models, thereby providing a neural basis for the CNN paradigm. The potential ability of multitasking image processing is investigated by using these templates. Many visual illusions are simulated via CNN image processing. The ability of the CNN to mimic such high-level brain functions suggests possible applications of the CNN in cognitive engineering. Furthermore, two kinds of painting-like image processings, namely, texture generation and illustration style transformation are investigated.

scholarly journals The experience of time: neural mechanisms and the interplay of emotion, cognition and embodiment

2009 ◽  
Vol 364 (1525) ◽  
pp. 1809-1813 ◽  
Author(s):  
Marc Wittmann ◽  
Virginie van Wassenhove
Keyword(s):  
Functional Neuroimaging ◽  
Neural Basis ◽  
Pharmacological Interventions ◽  
Time Intervals ◽  
Experience Of Time ◽  
Cognitive Neurosciences ◽  
Sense Of Time ◽  
Introductory Paper ◽  
Behavioural Experiments ◽  
The Brain

Time research has been a neglected topic in the cognitive neurosciences of the last decades: how do humans perceive time? How and where in the brain is time processed? This introductory paper provides an overview of the empirical and theoretical papers on the psychological and neural basis of time perception collected in this theme issue. Contributors from the fields of cognitive psychology, psychiatry, neurology and neuroanatomy tackle this complex question with a variety of techniques ranging from psychophysical and behavioural experiments to pharmacological interventions and functional neuroimaging. Several (and some new) models of how and where in the brain time is processed are presented in this unique collection of recent research that covers experienced time intervals from milliseconds to minutes. We hope this volume to be conducive in developing a better understanding of the sense of time as part of complex set of brain–body factors that include cognitive, emotional and body states.

The Interrelations between Verbal Working Memory and Visual Selection of Emotional Faces

2010 ◽  
Vol 22 (6) ◽  
pp. 1189-1200 ◽  
Author(s):  
Alessandro Grecucci ◽  
David Soto ◽  
Raffaella Ida Rumiati ◽  
Glyn W. Humphreys ◽  
Pia Rotshtein
Keyword(s):  
Working Memory ◽  
Verbal Working Memory ◽  
Search Display ◽  
Visual Selection ◽  
Emotional Word ◽  
Neural Basis ◽  
Selection Processes ◽  
Orbital Gyrus ◽  
High Level ◽  
Selection Of

Working memory (WM) and visual selection processes interact in a reciprocal fashion based on overlapping representations abstracted from the physical characteristics of stimuli. Here, we assessed the neural basis of this interaction using facial expressions that conveyed emotion information. Participants memorized an emotional word for a later recognition test and then searched for a face of a particular gender presented in a display with two faces that differed in gender and expression. The relation between the emotional word and the expressions of the target and distractor faces was varied. RTs for the memory test were faster when the target face matched the emotional word held in WM (on valid trials) relative to when the emotional word matched the expression of the distractor (on invalid trials). There was also enhanced activation on valid compared with invalid trials in the lateral orbital gyrus, superior frontal polar (BA 10), lateral occipital sulcus, and pulvinar. Re-presentation of the WM stimulus in the search display led to an earlier onset of activity in the superior and inferior frontal gyri and the anterior hippocampus irrespective of the search validity of the re-presented stimulus. The data indicate that the middle temporal and prefrontal cortices are sensitive to the reappearance of stimuli that are held in WM, whereas a fronto-thalamic occipital network is sensitive to the behavioral significance of the match between WM and targets for selection. We conclude that these networks are modulated by high-level matches between the contents of WM, behavioral goals, and current sensory input.

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