CaltechAUTHORS
  A Caltech Library Service

The impact of spike timing variability on the signal-encoding performance of neural spiking models

Manwani, Amit and Steinmetz, Peter N. and Koch, Christof (2002) The impact of spike timing variability on the signal-encoding performance of neural spiking models. Neural Computation, 14 (2). pp. 347-367. ISSN 0899-7667. http://resolver.caltech.edu/CaltechAUTHORS:MANnc02

[img]
Preview
PDF
See Usage Policy.

325Kb

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:MANnc02

Abstract

It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance.


Item Type:Article
Additional Information:© 2002 The MIT Press. Received June 20, 2000; accepted May 2, 2001. This work was funded by the NSF Center for Neuromorphic Systems Engineering at Caltech, the NIMH, and the Sloan-Swartz Center for Theoretical Neuroscience. We thank Idan Segev, Michael London, and Elad Schneidman for their invaluable suggestions.
Subject Keywords:TIME-VARYING SIGNALS; STOCHASTIC RESONANCE; NEURONAL MEMBRANES; NOISE; TRAINS; RELIABILITY; THRESHOLD; KINETICS; SYSTEMS
Record Number:CaltechAUTHORS:MANnc02
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:MANnc02
Alternative URL:http://neco.mitpress.org/cgi/content/abstract/14/2/347
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:7306
Collection:CaltechAUTHORS
Deposited By: Lindsay Cleary
Deposited On:29 Jan 2007
Last Modified:26 Dec 2012 09:30

Repository Staff Only: item control page