Solving the problem of negative synaptic weights in cortical models.
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 be determined empirically.
Parisien
Christopher
C
Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada. chris@cs.toronto.edu
Anderson
Charles H
CH
Eliasmith
Chris
C
D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology*; D003198:Computer Simulation; D008959:Models, Neurological; D009433:Neural Inhibition / Q000502:physiology*; D016571:Neural Networks, Computer; D013569:Synapses / Q000502:physiology*