Plasticity, learning, and complexity in spiking networks.
Complexity is widespread in neuronal spike trains and propagation of spike activity, in that variations in measurements of neural activity are irregular, heterogeneous, non-stationary, transient, and scale-free. There are numerous possible reasons for this complexity, and numerous possible consequences for neural and behavioral function. The present review is focused on relationships among neural plasticity, learning, and complex spike dynamics in animal nervous systems, including those of humans. The literature on complex spike dynamics and mechanisms of synaptic plasticity are reviewed for the purpose of considering the roles that each might play for the other. That is, the roles of complex spike dynamics in learning and regulatory functions are considered, as well as the roles of learning and regulatory functions in generating complex spike dynamics. Experimental and computational studies from a range of disciplines and perspectives are discussed, and it is concluded that cognitive science and neuroscience have much to gain from investigating the adaptive aspects of complex spike dynamics for neural and cognitive function.
Department of Cognitive and Information Sciences, University of California, Merced, CA, USA. ckello@ucmerced.edu
Christopher T
CT
Kello
Jeffrey
J
Rodny
Anne S
AS
Warlaumont
David C
DC
Noelle
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*