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This article is part of the supplement: Twenty First Annual Computational Neuroscience Meeting: CNS*2012

Open Access Poster presentation

Determining information flow through a network of simulated neurons

Cathal J Cooney* and Eoin Lynch

Author Affiliations

Mathematical Neuroscience Lab, School of Maths, Trinity College Dublin, Ireland

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BMC Neuroscience 2012, 13(Suppl 1):P92  doi:10.1186/1471-2202-13-S1-P92

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/13/S1/P92


Published:16 July 2012

© 2012 Cooney and Lynch; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

We feel that by applying Network Theory to neuroscience that we can determine how information can pass through a network of neurons. In vivo data would only provide a partial network, so we could not examine the information flow properly. Therefore, we decided to simulate a network of neurons, so that we could have control over the input, and so that we could see how each neuron reacts with its neighbors.

We simulate our network of neurons using the Adaptive Exponential Integrate-and-Fire (aEIF) model [1]. We let the network of neurons have the same characteristics as we would expect from a network of neurons in the brain.

We determine, from the output data, which neurons have a strong influence on when other neurons spike using Incremental Mutual Information (IMI) [2]. We model the network mathematically with the strength of links determined by the peak IMI to get a directed network. We form the bibliographic coupling network and cluster it effectively by using Newman's eigenvalue algorithm for maximizing modularity [3]. By comparing these clusters back to the directed network, we get a map of information flow through the network of neurons.

We feel that this could be a useful method for analyzing datasets of simultaneous neurons as such datasets get larger with advances in recording equipment.

References

  1. Brette R, Gerstner W: Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity.

    J Neurophysiol 2005, 94:3637-3642. PubMed Abstract | Publisher Full Text OpenURL

  2. Singh A, Lesica NA: Incremental Mutual Information: A New Method for Characterizing the Strength and Dynamics of Connections in Neuronal Circuits.

    PloS Comput Biol 2010, 6(12):e1001035.

    doi:10.1371/journal.pcbi.1001035

    PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  3. Newman MEJ: Modularity and community structure in networks.

    PNAS 2006, 103(23):8577-8582.

    doi: 10.1073/pnas.0601602103

    PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL