A simulation tool for modeling the influence of anatomy on information flow using discrete integrate and fire neurons

Maya Maimon, Larry Manevitz

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)287-304
Number of pages18
JournalJournal of Combinatorial Optimization
Issue number3
StatePublished - Apr 2008

Bibliographical note

Funding Information:
Acknowledgements This work was conceived when LM was on a sabbatical visit to Edmund Rolls laboratory in Oxford, UK; and an early version of the simulator was written there. LM thanks Prof. Rolls for his hospitality. Leonardo Franco is particularly thanked for explicating the use of information theory in this context and Paul Gabbott is thanked for painstakingly describing various aspects of physiology. This work is part of the M.Sc. thesis of MM at the U. Haifa where she was supported by the Caesarea Rothschild Center and the Neurocomputation Laboratory. MM thanks them, and LM for his guidance.


  • Bias or priming hypothesis
  • Information theory
  • Large scale neural simulator
  • Temporal discrete integrate and fire

ASJC Scopus subject areas

  • Computer Science Applications
  • Discrete Mathematics and Combinatorics
  • Control and Optimization
  • Computational Theory and Mathematics
  • Applied Mathematics


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