Analog Computation at a Critical Point: A Novel Function for Neuronal Oscillations?

Abstract

\Ve show that a simple spin system bia.sed at its critical point can encode spatial characteristics of external signals, sHch as the dimensions of "objects" in the visual field. in the temporal correlation functions of individual spins. Qualit.ative arguments suggest that regularly firing neurons should be described by a planar spin of unit lengt.h. and such XY models exhibit critical dynamics over a broad range of parameters. \Ve show how to extract these spins from spike trains and then mea'3ure t.he interaction Hamilt.onian using simulations of small dusters of cells. Static correlations among spike trains obtained from simulations of large arrays of cells are in agreement with the predictions from these Hamiltonians, and dynamic correlat.ions display the predicted encoding of spatial information. \Ve suggest that this novel representation of object dinwnsions in temporal correlations may be relevant t.o recent experiment.s on oscillatory neural firing in the visual cortex.

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Cite this paper

@inproceedings{Kruglyak1990AnalogCA, title={Analog Computation at a Critical Point: A Novel Function for Neuronal Oscillations?}, author={Leonid Kruglyak}, year={1990} }