Extracting Oscillations: Neuronal Coincidence Detection with Noisy Periodic Spike Input (1998)
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BibTeX
@MISC{Kempter98extractingoscillations:,
author = {Richard Kempter and Wulfram Gerstner and J. Leo Van Hemmen and H. Wagner},
title = {Extracting Oscillations: Neuronal Coincidence Detection with Noisy Periodic Spike Input},
year = {1998}
}
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Abstract
How does a neuron vary its mean output firing rate if the input changes from random to coherent activity? What are the critical parameters of the neuronal dynamics and input statistics? To answer these questions, we investigate the coincidence detection properties of an integrate-and-fire neuron. We derive an expression indicating how coincidence detection depends on neuronal parameters. Specifically, (i) we show how coincidence detection depends on the shape of the postsynaptic response function, the number of synapses, and the input statistics, and (ii) we demonstrate that there is an optimal threshold. Our considerations can be used to predict from neuronal parameters whether and to what extent a neuron can act as a coincidence detector and thus can convert a temporal code into a rate code. Physik-Department der TU Munchen (T35), D-85747 Garching bei Munchen, Germany y Swiss Federal Institute of Technology, Center of Neuromimetic Systems, EPFL-DI, CH-1015 Lausanne, Switz...
