Extracting Oscillations: Neuronal Coincidence Detection with Noisy Periodic Spike Input

Authors:
Richard Kempter;Wulfram Gerstner;J. Leo Van Hemmen;Hermann Wagner
Affiliations:
Physik-Department der TU München, D-85747 Garching bei München, Germany;Swiss Federal Institute of Technology, Center of Neuromimetic Systems, EPFL-DI, CH-1015 Lausanne, Switzerland;Physik-Department der TU Mu¨nchen, D-85747 Garching bei Mu¨nchen, Germany;RWTH Aachen, Institut fu¨r Biologie II, D-52074 Aachen, Germany
Venue:
Neural Computation
Year:
1998

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Abstract

How does a neuron vary its mean output firing rate if the input changes from random to oscillatory coherent but noisy 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, we show how coincidence detection depends on the shape of the postsynaptic response function, the number of synapses, and the input statistics, and 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.