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This article is part of the supplement: Twentieth Annual Computational Neuroscience Meeting: CNS*2011

Open Access Poster presentation

Emergence of direction- and orientation-selectivity and other complex structures from stochastic neuronal networks evolving under STDP

Nana Arizumi1*, Todd Coleman1 and Lee DeVille23

Author Affiliations

1 Computer science, University of Illinois, Urbana-Champaign, IL 61801, USA

2 Electrical Engineering, University of Illinois, Urbana-Champaign, IL 61801, USA

3 Mathematics, University of Illinois, Urbana-Champaign, IL 61801, USA

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BMC Neuroscience 2011, 12(Suppl 1):P68  doi:10.1186/1471-2202-12-S1-P68

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/12/S1/P68


Published:18 July 2011

© 2011 Arizumi1 et al; 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 consider neuronal network models with plasticity and randomness and show that complicated global structures can evolve even in the presence of simple local update rules. Our computational model generates several interesting features; e.g. orientation- and direction-selectivity when the inputs are arranged in a manner analogous to a visual field. Our model is a discrete-time Markov chain which contains multiple excitatory and inhibitory input neurons, and has as outputs stochastic leaky integrate-and-fire neurons; the system evolves through the plasticity of the synapses, updated according to a spike-timing dependent plasticity (STDP) rule.

We observe that the network is capable of rich properties (e.g. bifurcation, various forms of stability, etc) that depend on the statistics of the stimulus and the coupling parameters in the network. Since we are using a mechanism that can be easily modeled mathematically, we believe that this approach provides a well-positioned balance between neuro-biological relevance and theoretical tractability.