Description: Decorrelation of neural-network activity by inhibitory feedback

This title appears in the Scientific Report : 2012

Decorrelation of neural-network activity by inhibitory feedback

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 studies demonstrate that spike correlations in recurrent ne...

Personal Name(s):	Tetzlaff, T.
	Helias, M. / Einevoll, G.T. / Diesmann, M.
Contributing Institute:	Computational and Systems Neuroscience; INM-6
Published in:	PLoS Computational Biology, 8 (2012) S. e1002596
Imprint:	San Francisco, Calif. Public Library of Science 2012
Physical Description:	e1002596
DOI:	10.1371/journal.pcbi.1002596
PubMed ID:	23133368
Document Type:	Journal Article
Research Program:	Brain-inspired multiscale computation in neuromorphic hybrid systems Theory, modelling and simulation Funktion und Dysfunktion des Nervensystems
Series Title:	PLoS Computational Biology 8
Subject (ZB):	J
Link:	Get full text OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://dx.doi.org/10.1371/journal.pcbi.1002596 in citations.
Please use the identifier: http://hdl.handle.net/2128/7579 in citations.

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 studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations 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).