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Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses

Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuron...

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Detalles Bibliográficos
Autores principales: Rigotti, Mattia, Rubin, Daniel Ben Dayan, Wang, Xiao-Jing, Fusi, Stefano
Formato: Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2967380/
https://www.ncbi.nlm.nih.gov/pubmed/21048899
http://dx.doi.org/10.3389/fncom.2010.00024
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author Rigotti, Mattia
Rubin, Daniel Ben Dayan
Wang, Xiao-Jing
Fusi, Stefano
author_facet Rigotti, Mattia
Rubin, Daniel Ben Dayan
Wang, Xiao-Jing
Fusi, Stefano
author_sort Rigotti, Mattia
collection PubMed
description Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context-dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding). A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.
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spelling pubmed-29673802010-11-03 Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses Rigotti, Mattia Rubin, Daniel Ben Dayan Wang, Xiao-Jing Fusi, Stefano Front Comput Neurosci Neuroscience Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context-dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding). A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation. Frontiers Research Foundation 2010-10-04 /pmc/articles/PMC2967380/ /pubmed/21048899 http://dx.doi.org/10.3389/fncom.2010.00024 Text en Copyright © 2010 Rigotti, Rubin, Wang and Fusi. http://www.frontiersin.org/licenseagreement This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
spellingShingle Neuroscience
Rigotti, Mattia
Rubin, Daniel Ben Dayan
Wang, Xiao-Jing
Fusi, Stefano
Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses
title Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses
title_full Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses
title_fullStr Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses
title_full_unstemmed Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses
title_short Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses
title_sort internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2967380/
https://www.ncbi.nlm.nih.gov/pubmed/21048899
http://dx.doi.org/10.3389/fncom.2010.00024
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