Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

May 2011, Vol. 23, No. 5, Pages 1261-1305
(doi: 10.1162/NECO_a_00116)
© 2011 Massachusetts Institute of Technology
Mechanisms That Modulate the Transfer of Spiking Correlations
Article PDF (1.08 MB)
Abstract

Correlations between neuronal spike trains affect network dynamics and population coding. Overlapping afferent populations and correlations between presynaptic spike trains introduce correlations between the inputs to downstream cells. To understand network activity and population coding, it is therefore important to understand how these input correlations are transferred to output correlations. Recent studies have addressed this question in the limit of many inputs with infinitesimal postsynaptic response amplitudes, where the total input can be approximated by gaussian noise. In contrast, we address the problem of correlation transfer by representing input spike trains as point processes, with each input spike eliciting a finite postsynaptic response. This approach allows us to naturally model synaptic noise and recurrent coupling and to treat excitatory and inhibitory inputs separately. We derive several new results that provide intuitive insights into the fundamental mechanisms that modulate the transfer of spiking correlations.