HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Parisien, C. Articles by Eliasmith, C. PubMed Articles by Parisien, C. Articles by Eliasmith, C.

Solving the Problem of Negative Synaptic Weights in Cortical Models

chris{at}cs.toronto.edu Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada

Charles H. Anderson

cha{at}wustl.edu Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO 63110, U.S.A.

Chris Eliasmith

celiasmith{at}uwaterloo.ca Centre for Theoretical Neuroscience, Departments of Philosophy and Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

In cortical neural networks, connections from a given neuron are either inhibitory or excitatory but not both. This constraint is often ignored by theoreticians who build models of these systems. There is currently no general solution to the problem of converting such unrealistic network models into biologically plausible models that respect this constraint. We demonstrate a constructive transformation of models that solves this problem for both feedforward and dynamic recurrent networks. The resulting models give a close approximation to the original network functions and temporal dynamics of the system, and they are biologically plausible. More precisely, we identify a general form for the solution to this problem. As a result, we also describe how the precise solution for a given cortical network can bedetermined empirically.