Emergence of Persistent Activity States in a Two-Population Neural Field Model for Smooth $\alpha$ -Type External Input

Zeeshan Afzal,Yongsheng Rao,Naima Amin,Yousaf Bhatti

doi:10.1109/access.2019.2914427

Zeeshan Afzal, Yongsheng Rao + Show 2 more

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https://doi.org/10.1109/access.2019.2914427

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Abstract

Emergence of Persistent Activity States in a Two-Population Neural Field Model for Smooth $\alpha$ -Type External Input

Highlights

The main objective of this study is to investigate the effect of spatio-temporal external input on bump solutions with more smooth and more commonly observed temporal part
This is the main section of this study where we investigate the effects of different types of spatio-temporal external inputs (23) the emergence of self-sustained activity states
A possible way underlying this type of activation of subpopulations of neurons in the network is the switching between the attractor states, which is found in the neural firing rate models by means of the external input [1], [19], [27], [29]

Summary

Introduction

The brain has a basic ability to transiently hold stimulus related information so-called working memory It has been observed experimentally, that persistent evoking of groups of neurons in prefrontal cortex were identified as a neural correlate underlying this short-term memory [5], [11], [12], [22], [23].In last decade studies have been done where various models have come across on how cortical networks can possibly generate and sustain the selective activation of group ofThe associate editor coordinating the review of this manuscript and approving it for publication was Aniruddha Datta.neurons (subpopulations), e.g. attractor states in the network, persistent activation of thalmo-cortical and cortiocortical loops [12], [13]. In the cell assembly the activity is persistent through strong recurrent excitatory connections [21]. Another idea is that activity circulates in form loops (called synfire chains ) [22], which consists of feed-forward connected subgroups with no direct feedback links between succeeding groups of neurons.

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Conclusion