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Single Neuron Computation: From Dynamical System to Feature Detector

shhong{at}u.washington.edu Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, U.S.A.

Blaise Agüera y Arcas

blaisea{at}microsoft.com Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544

Adrienne L. Fairhall

fairhall{at}u.washington.edu Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, U.S.A.

White noise methods are a powerful tool for characterizing the computation performed by neural systems. These methods allow one to identify the feature or features that a neural system extracts from a complex input and to determine how these features are combined to drive the system's spiking response. These methods have also been applied to characterize the input-output relations of single neurons driven by synaptic inputs, simulated by direct current injection. To interpret the results of white noise analysis of single neurons, we would like to understand how the obtained feature space of a single neuron maps onto the biophysical properties of the membrane, in particular, the dynamics of ion channels. Here, through analysis of a simple dynamical model neuron, we draw explicit connections between the output of a white noise analysis and the underlying dynamical system. We find that under certain assumptions, the form of the relevant features is well defined by the parameters of the dynamical system. Further, we show that under some conditions, the feature space is spanned by the spike-triggered average and its successive order time derivatives.

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				B. N. Lundstrom, S. Hong, M. H. Higgs, and A. L. Fairhall Two Computational Regimes of a Single-Compartment Neuron Separated by a Planar Boundary in Conductance Space Neural Comput., May 1, 2008; 20(5): 1239 - 1260. [Abstract] [Full Text] [PDF]