Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking Networks
A major function of central nervous systems is to discriminate different categories or types of sensory input. Neuronal networks accomplish such tasks by learning different sensory maps at several stages of neural hierarchy, such that different neurons fire selectively to reflect different internal...
Main Author: | |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2017-04-01
|
Series: | Frontiers in Computational Neuroscience |
Subjects: | |
Online Access: | http://journal.frontiersin.org/article/10.3389/fncom.2017.00020/full |
id |
doaj-ac64f505f0ae4ca8a58e5bbcc0cbb7ab |
---|---|
record_format |
Article |
spelling |
doaj-ac64f505f0ae4ca8a58e5bbcc0cbb7ab2020-11-24T22:39:15ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882017-04-011110.3389/fncom.2017.00020242287Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking NetworksYanqing ChenA major function of central nervous systems is to discriminate different categories or types of sensory input. Neuronal networks accomplish such tasks by learning different sensory maps at several stages of neural hierarchy, such that different neurons fire selectively to reflect different internal or external patterns and states. The exact mechanisms of such map formation processes in the brain are not completely understood. Here we study the mechanism by which a simple recurrent/reentrant neuronal network accomplish group selection and discrimination to different inputs in order to generate sensory maps. We describe the conditions and mechanism of transition from a rhythmic epileptic state (in which all neurons fire synchronized and indiscriminately to any input) to a winner-take-all state in which only a subset of neurons fire for a specific input. We prove an analytic condition under which a stable bump solution and a winner-take-all state can emerge from the local recurrent excitation-inhibition interactions in a three-layer spiking network with distinct excitatory and inhibitory populations, and demonstrate the importance of surround inhibitory connection topology on the stability of dynamic patterns in spiking neural network.http://journal.frontiersin.org/article/10.3389/fncom.2017.00020/fullneuronal spiking networkphase transitionlearning and memoryWinner-take-all (WTA)neural computationRobotics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yanqing Chen |
spellingShingle |
Yanqing Chen Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking Networks Frontiers in Computational Neuroscience neuronal spiking network phase transition learning and memory Winner-take-all (WTA) neural computation Robotics |
author_facet |
Yanqing Chen |
author_sort |
Yanqing Chen |
title |
Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking Networks |
title_short |
Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking Networks |
title_full |
Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking Networks |
title_fullStr |
Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking Networks |
title_full_unstemmed |
Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking Networks |
title_sort |
mechanisms of winner-take-all and group selection in neuronal spiking networks |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Computational Neuroscience |
issn |
1662-5188 |
publishDate |
2017-04-01 |
description |
A major function of central nervous systems is to discriminate different categories or types of sensory input. Neuronal networks accomplish such tasks by learning different sensory maps at several stages of neural hierarchy, such that different neurons fire selectively to reflect different internal or external patterns and states. The exact mechanisms of such map formation processes in the brain are not completely understood. Here we study the mechanism by which a simple recurrent/reentrant neuronal network accomplish group selection and discrimination to different inputs in order to generate sensory maps. We describe the conditions and mechanism of transition from a rhythmic epileptic state (in which all neurons fire synchronized and indiscriminately to any input) to a winner-take-all state in which only a subset of neurons fire for a specific input. We prove an analytic condition under which a stable bump solution and a winner-take-all state can emerge from the local recurrent excitation-inhibition interactions in a three-layer spiking network with distinct excitatory and inhibitory populations, and demonstrate the importance of surround inhibitory connection topology on the stability of dynamic patterns in spiking neural network. |
topic |
neuronal spiking network phase transition learning and memory Winner-take-all (WTA) neural computation Robotics |
url |
http://journal.frontiersin.org/article/10.3389/fncom.2017.00020/full |
work_keys_str_mv |
AT yanqingchen mechanismsofwinnertakeallandgroupselectioninneuronalspikingnetworks |
_version_ |
1725709974146908160 |