Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 354 - Experimental Design and Reliability
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract #318001
Title: A Consistent Estimator of Nontrivial Stationary Solutions of Dynamic Neural Fields
Author(s): Eddy Kwessi*
Companies: Trinity University
Keywords: Dynamic neural field; nontrivial; stationary; estimator; consistent

Dynamics of Neural Fields are tools used in neurosciences to understand the activities generated by large ensembles of neurons. They are also used in networks analysis and neuroinformatics in particular to model a continuum of neural networks. They are mathematical models that describe the average behavior of these congregations of neurons, which are often in large amounts, even in small cortexes of the brain. Therefore, change of average activity (potential, connectivity, firing rate, etc) are described using systems of partial different equations. In their continuous or discrete forms, these systems have a rich array of properties, among which the existence of nontrivial stationary solutions. In this paper, we propose an estimator for nontrivial solutions of dynamical neural fields with a single layer. The estimator is shown to be consistent and a computational algorithm is proposed to help carry out implementation. An illustrations of this consistency is given based on different inputs functions, different kernels, and different pulse emission rate functions.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program