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Stimulus-dependent suppression of chaos in recurrent neural networks Kanaka Rajan*

Summary: Stimulus-dependent suppression of chaos in recurrent neural networks
Kanaka Rajan*
Lewis-Sigler Institute for Integrative Genomics, Icahn 262, Princeton University, Princeton, New Jersey 08544, USA
L. F. Abbott
Department of Neuroscience and Department of Physiology and Cellular Biophysics, College of Physicians and Surgeons,
Columbia University, New York, New York 10032-2695, USA
Haim Sompolinsky
Racah Institute of Physics, Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel
Received 31 July 2009; revised manuscript received 22 April 2010; published 7 July 2010
Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural
circuits and responses driven by external stimuli. Using mean-field analysis, we ask how a neural network that
intrinsically generates chaotic patterns of activity can remain sensitive to extrinsic input. We find that inputs
not only drive network responses, but they also actively suppress ongoing activity, ultimately leading to a
phase transition in which chaos is completely eliminated. The critical input intensity at the phase transition is
a nonmonotonic function of stimulus frequency, revealing a "resonant" frequency at which the input is most
effective at suppressing chaos even though the power spectrum of the spontaneous activity peaks at zero and
falls exponentially. A prediction of our analysis is that the variance of neural responses should be most strongly
suppressed at frequencies matching the range over which many sensory systems operate.
DOI: 10.1103/PhysRevE.82.011903 PACS number s : 87.19.lj, 64.60.aq, 84.35. i, 87.19.ll
Circuits of the central nervous system exhibit temporally


Source: Abbott, Laurence - Center for Neurobiology and Behavior & Department of Physiology and Cellular Biophysics, Columbia University
Sompolinsky, Haim - Racah Institute of Physics, Hebrew University of Jerusalem


Collections: Biology and Medicine; Physics