A simulation tool for modeling the influence of anatomy on information flow using discrete integrate and fire neurons |
| |
Authors: | Maya Maimon Larry Manevitz |
| |
Institution: | (1) Department of Computer Science, University of Haifa, Haifa, Israel |
| |
Abstract: | There are theories on brain functionality that can only be tested in very large models. In this work, a simulation model appropriate
for working with large number of neurons was developed, and Information Theory measuring tools were designed to monitor the
flow of information in such large networks. The model’s simulator can handle up to one million neurons in its current implementation
by using a discretized version of the Lapicque integrate and fire neuron instead of interacting differential equations. A modular
structure facilitates the setting of parameters of the neurons, networks, time and most importantly, architectural changes.
Applications of this research are demonstrated by testing architectures in terms of mutual information. We present some preliminary
architectural results showing that adding a virtual analogue to white matter called “jumps” to a simple representation of
cortex results in: (1) an increase in the rate of mutual information flow, corresponding to the “bias” or “priming” hypothesis;
thereby giving a possible explanation of the high speed response to stimuli in complex networks. (2) An increase in the stability
of response of the network; i.e. a system with “jumps” is a more reliable machine. This also has an effect on the potential
speed of response. |
| |
Keywords: | Large scale neural simulator Temporal discrete integrate and fire Information theory Bias or priming hypothesis |
本文献已被 SpringerLink 等数据库收录! |
|