title: Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems.
abstract: Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences of chaos for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggesting that small changes in stimuli will be obscured by intrinsically generated variability. On the other hand, recent work shows that the type of chaos that occurs in spiking networks can have a surprisingly low-dimensional structure, suggesting that there may be room for fine stimulus features to be precisely resolved. Here we show that strongly chaotic networks produce patterned spikes that reliably encode time-dependent stimuli: using a decoder sensitive to spike times on timescales of 10's of ms, one can easily distinguish responses to very similar inputs. Moreover, recurrence serves to distribute signals throughout chaotic networks so that small groups of cells can encode substantial information about signals arriving elsewhere. A conclusion is that the presence of strong chaos in recurrent networks need not exclude precise encoding of temporal stimuli via spike patterns.
authors.affiliation: University of Washington Institute for Neuroengineering, University of Washington, Seattle, Washington, United States of America.|Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Lajoie
authors.affiliation: School of Mathematics, University of Arizona, Tucson, Arizona, United States of America.
authors.forename: Kevin K
authors.identifier: 0000-0002-3694-544X
authors.initials: KK
authors.lastname: Lin
authors.affiliation: School of Psychology, University of Ottawa, Ottawa, Ontario, Canada.
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Thivierge
authors.affiliation: University of Washington Institute for Neuroengineering, University of Washington, Seattle, Washington, United States of America.|Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.|Department of Physiology and Biophysics, University of Washington, Seattle, Washington, United States of America.
authors.forename: Eric
authors.initials: E
authors.lastname: Shea-Brown
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D019295:Computational Biology; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics