Speaker
Markus Diesman
(Research Center Juelich)
Description
Correlations in spike-train ensembles can seriously impair the
encoding of information by their spatio-temporal structure.
An inevitable source of correlation in finite neural networks is
common presynaptic input to pairs of neurons. Recent
theoretical and experimental studies demonstrate that spike
correlations in recurrent neural networks are considerably
smaller than expected based on the amount of shared
presynaptic input. By means of a linear network model and
simulations of networks of leaky integrate-and-fire neurons,
we show that shared-input correlations are efficiently
suppressed by inhibitory feedback. To elucidate the effect of
feedback, we compare the responses of the intact recurrent
network and systems where the statistics of the feedback
channel is perturbed. The suppression of spike-train
correlations and population-rate fluctuations by inhibitory
feedback can be observed both in purely inhibitory and in
excitatory-inhibitory networks. The effect is fully understood
by a linear theory and becomes already apparent at the
macroscopic level of population averaged activity. At the
microscopic level, shared-input correlations are suppressed by
spike-train correlations: In purely inhibitory networks, they
are canceled by negative spike-train correlations. In
excitatory-inhibitory networks, spike-train correlations are
typically positive. Here, the suppression of input correlations
is not a result of the mere existence of correlations between
excitatory (E) and inhibitory (I) neurons, but a consequence
of a particular structure of correlations among the three
possible pairings (EE, EI, II).
