Journal of Combinatorial Optimization

, Volume 15, Issue 3, pp 287–304 | Cite as

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

  • Maya Maimon
  • Larry ManevitzEmail author


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.


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


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  1. Abbott LF (1999) Lapicque’s introduction of the integrate-and-fire model neuron (1907). Brain Res Bull 50:303–304 CrossRefGoogle Scholar
  2. Bezzi M, Diamond ME, Treves A (2002) Redundancy and synergy arising from pairwise correlations in neuronal ensembles. J Comp Neurosci 12:165–174 CrossRefGoogle Scholar
  3. Braitenberg V, Schüz A (1998) Cortex: statistics and geometry of neuronal connectivity. Springer, Berlin (Revised edition of Anatomy of the cortex, statistics and geometry, 1991) Google Scholar
  4. Eytan D, Marom S (2006) The network spike: a basic mode of synchronization within and between neuronal assemblies. PhD thesis, Faculty of Medicine, Technion Google Scholar
  5. Fellman DJ, Van Essen DC (2001) Distributed hierarchical processing in the primate visual cortex. Cereb Cortex 1:1–47 CrossRefGoogle Scholar
  6. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500–544 Google Scholar
  7. Lapicque L (1907) Recherches quantitatives sur l’excitation lectrique des nerfs traite comme une polarisation. Physiol Pathol Gen 9:620–635 Google Scholar
  8. Marom S, Shahaf G (2002) Development, learning and memory in large random networks of cortical neurons:lesson beyond anatomy. Q Rev Biophys 35:63–87 CrossRefGoogle Scholar
  9. Numerical recipes in Fortran 90 (1996) The art of scientific parallel computing, scientific computing. Cambridge University Press, Cambridge Google Scholar
  10. Panzeri S, Schultz SR, Treves A, Rolls ET (1999) Correlations and encoding of information in the nervous system. Proc R Soc Lond Ser B: Biol Sci 266:1001–1012 CrossRefGoogle Scholar
  11. Panzeri S, Rolls ET, Battaglia F, Lavis R (2001) Speed of feed forward and recurrent processing in multilayer networks of integrate-and-fire neurons. Network 12(4):423–440 Google Scholar
  12. Rolls ET, Deco G (2002) Computational neuroscience of vision. Oxford University Press, Oxford Google Scholar
  13. Rolls ET, Franco L, Aggelpoulos NC, Reece S (2003) An information theoretic approach to the contributions of the firing rates and the correlations between the firing of neurons. Neurophysiol 89:2810–2822 CrossRefGoogle Scholar
  14. Schüz A (1998) Neuroanatomy in a computational perspective. In: Arbib MA (ed) Handbook of brain theory and neural networks. MIT Press, Cambridge Google Scholar
  15. The genesis simulator (1994–2006)
  16. Trappenberg TP (2002) Fundamental of Computational Neuroscience. Oxford University Press, Oxford Google Scholar
  17. Treves A, Rolls ET, Simmen M (1997) Time for retrieval in recurrent associative memories. Physica D 107:392–400 Google Scholar
  18. Wolfram S (2002) A new kind of science. Wolfram Media, Inc Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of HaifaHaifaIsrael

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