title: A reanalysis of "Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons".
abstract: Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF) neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.
authors.affiliation: Max Planck Institute for Dynamics and Self-Organization (MPI-DS), Bernstein Center for Computational Neuroscience Göttingen, Faculty of Physics,, University of Göttingen, Göttingen, Germany.|Collaborative Research Center 889, University of Göttingen, Göttingen, 37099, Germany.
authors.forename: Rainer
authors.initials: R
authors.lastname: Engelken
authors.affiliation: Institut für Mathematik, Technische Universität Berlin and Bernstein Center for Computational Neuroscience, Berlin, Germany.
authors.forename: Farzad
authors.initials: F
authors.lastname: Farkhooi
authors.affiliation: Cerebral Dynamics, Learning and Memory Research Group, Center for Neurophysics, Physiology and Pathology, CNRS UMR8119, Université Paris Descartes, Paris, France.
authors.forename: David
authors.initials: D
authors.lastname: Hansel
authors.affiliation: Cerebral Dynamics, Learning and Memory Research Group, Center for Neurophysics, Physiology and Pathology, CNRS UMR8119, Université Paris Descartes, Paris, France.
authors.forename: Carl
authors.initials: C
authors.lastname: van Vreeswijk
authors.affiliation: Max Planck Institute for Dynamics and Self-Organization (MPI-DS), Bernstein Center for Computational Neuroscience Göttingen, Faculty of Physics,, University of Göttingen, Göttingen, Germany.|Collaborative Research Center 889, University of Göttingen, Göttingen, 37099, Germany.
authors.forename: Fred
authors.initials: F
authors.lastname: Wolf
keywords: balanced state; mean field theory; network dynamics; rate chaos