title: A learning rule for the emergence of stable dynamics and timing in recurrent networks.
abstract: Neural dynamics within recurrent cortical networks is an important component of neural processing. However, the learning rules that allow networks composed of hundreds or thousands of recurrently connected neurons to develop stable dynamical states are poorly understood. Here I use a neural network model to examine the emergence of stable dynamical states within recurrent networks. I describe a learning rule that can account both for the development of stable dynamics and guide networks to states that have been observed experimentally, specifically, states that instantiate a sparse code for time. Across trials, each neuron fires during a specific time window; by connecting the neurons to a hypothetical set of output units, it is possible to generate arbitrary spatial-temporal output patterns. Intertrial jitter of the spike time of a given neuron increases as a direct function of the delay at which it fires. These results establish a learning rule by which cortical networks can potentially process temporal information in a self-organizing manner, in the absence of specialized timing mechanisms.
authors.affiliation: Department of Neurobiology and Psychology, University of California, Los Angeles, 90095, USA. dbuono@ucla.edu
authors.forename: Dean V
authors.initials: DV
authors.lastname: Buonomano
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D004558:Electric Stimulation / Q000379:methods; D066298:In Vitro Techniques; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D017711:Nonlinear Dynamics; D013569:Synapses / Q000502:physiology*; D013997:Time Factors