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Intensity Coding in Two-Dimensional Excitable Neural Networks

Mauro Copelli·Osame Kinouchi
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Abstract

In the light of recent experimental findings that gap junctions are essential for low level intensity detection in the sensory periphery, the Greenberg-Hastings cellular automaton is employed to model the response of a two-dimensional sensory network to external stimuli. We show that excitable elements (sensory neurons) that have a small dynamical range are shown to give rise to a collective large dynamical range. Therefore the network transfer (gain) function (which is Hill or Stevens law-like) is an emergent property generated from a pool of small dynamical range cells, providing a basis for a "neural psychophysics". The growth of the dynamical range with the system size is approximately logarithmic, suggesting a functional role for electrical coupling. For a fixed number of neurons, the dynamical range displays a maximum as a function of the refractory period, which suggests experimental tests for the model. A biological application to ephaptic interactions in olfactory nerve fascicles is proposed.

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Mauro Copelli, Osame Kinouchi. 2004-09-28. Intensity Coding in Two-Dimensional Excitable Neural Networks. https://doi.org/10.1016/j.physa.2004.10.043

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