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 Marinazzo, D. Articles by Gielen, S. C. A. M. Search for Related Content PubMed Articles by Marinazzo, D. Articles by Gielen, S. C. A. M.

Input-Driven Oscillations in Networks with Excitatory and Inhibitory Neurons with Dynamic Synapses

Daniele.Marinazzo{at}ba.infn.it Department of Biophysics, Radboud University of Nijmegen, 6525 EZ Nimegen, The Netherlands; TIRES—Center of Innovative Technologies for Signal Detection and Processing and Dipartimento Interateneo di Fisica, Università di Bari, 70125, Bari, Italy; and Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy

Hilbert J. Kappen

B.Kappen{at}science.ru.nl Department of Physics, Radboud University of Nijmegen, 6525 EZ Nijmegen, The Netherlands

Stan C. A. M. Gielen

S.Gielen{at}science.ru.nl Department of Physics, Radboud University of Nijmegen, 6525 EZ Nijmegen, The Netherlands

Previous work has shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons can reveal oscillatory activity. For example, Börgers and Kopell (2003) have shown that oscillations occur when the excitatory neurons receive a sufficiently large input. A constant drive to the excitatory neurons is sufficient for oscillatory activity. Other studies (Doiron, Chacron, Maler, Longtin, & Bastian, 2003; Doiron, Lindner, Longtin, Maler, & Bastian, 2004) have shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons reveal oscillatory activity only if the excitatory neurons receive correlated input, regardless of the amount of excitatory input. In this study, we show that these apparently contradictory results can be explained by the behavior of a single model operating in different regimes of parameter space. Moreover, we show that adding dynamic synapses in the inhibitory feedback loop provides a robust network behavior over a broad range of stimulus intensities, contrary to that of previous models. A remarkable property of the introduction of dynamic synapses is that the activity of the network reveals synchronized oscillatory components in the case of correlated input, but also reflects the temporal behavior of the input signal to the excitatory neurons. This allows the network to encode both the temporal characteristics of the input and the presence of spatial correlations in the input simultaneously.