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BMC Neuroscience

Open Access

Scaling of spike-timing based neuron model for mammalian olfaction with network size

  • Bolun Chen1,
  • Jan R Engelbrecht1 and
  • Renato Mirollo2
BMC Neuroscience201415(Suppl 1):P90

https://doi.org/10.1186/1471-2202-15-S1-P90

Published: 21 July 2014

We investigate extensions to the model put forward by Brody and Hopfield [1] for spike-timing based pattern recognition applied to mammalian olfaction. Their model implements a pattern recognition algorithm realized in the dynamics of a network of coupled IF neurons subject to a sine-wave rhythm. Subsets of these neurons can synchronize through the principle of one-to-one mode locking. Their network represents 3 layers of neural activity, the first two of which are inspired by the connectivity of glomeruli and mitral cells in mammalian olfactory circuits and the gamma-rhythm activity observed in the olfactory bulb. Specifically in this model a pattern of glomerular activity representing a given odor causes a particular subset of model mitral cells to synchronize and this synchronous activity can drive a "grandmother" model cortical cell through threshold triggering a recognition event. In this study we quantify the performance of their original model and compare it to our extensions of the model such as using a network-generated rhythm rather than a sine-wave, introducing inhibitive feedback and generalizing to p-q mode locking strategies. We compute the scaling with respect to the number of mitral neurons of a measure of the number of odor patterns the model can recognize. Quite remarkably we find this performance can increase very fast with increasing network size -- consistent with exponential scaling.

Authors’ Affiliations

(1)
Physics Department, Boston College
(2)
Mathematics Department, Boston College

References

  1. Brody CD, Hopfield JJ: Simple Networks for Spike-Timing-Based Computation, with Application to Olfactory Processing. Neuron. 2003, 37: 843-852. 10.1016/S0896-6273(03)00120-X.View ArticlePubMedGoogle Scholar

Copyright

© Chen et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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