HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Morrison, A. Articles by Diesmann, M. Search for Related Content PubMed Articles by Morrison, A. Articles by Diesmann, M.

Exact Subthreshold Integration with Continuous Spike Times in Discrete-Time Neural Network Simulations

abigail{at}biologie.uni-freiburg.de Computational Neurophysics, Institute of Biology III, and Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, 79104 Freiburg, Germany

Sirko Straube

sirko.straube{at}biologie.uni-freiburg.de Computational Neurophysics, Institute of Biology III, Albert-Ludwigs-University, 79104 Freiburg, Germany

Hans Ekkehard Plesser

hans.ekkehard.plesser{at}umb.no Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, N-1432 Ås, Norway

Markus Diesmann

diesmann{at}biologie.uni-freiburg.de Computational Neurophysics, Institute of Biology III, and Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, 79104 Freiburg, Germany

Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.