A network of spiking neurons that can represent interval timing: mean field analysis.
Despite the vital importance of our ability to accurately process and encode temporal information, the underlying neural mechanisms are largely unknown. We have previously described a theoretical framework that explains how temporal representations, similar to those reported in the visual cortex, can form in locally recurrent cortical networks as a function of reward modulated synaptic plasticity. This framework allows networks of both linear and spiking neurons to learn the temporal interval between a stimulus and paired reward signal presented during training. Here we use a mean field approach to analyze the dynamics of non-linear stochastic spiking neurons in a network trained to encode specific time intervals. This analysis explains how recurrent excitatory feedback allows a network structure to encode temporal representations.
Gavornik
Jeffrey P
JP
Department of Neurobiology and Anatomy, The University of Texas Medical School at Houston, 6431 Fannin St., Houston, TX 77030, USA.
Shouval
Harel Z
HZ
D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001703:Biophysics; D004558:Electric Stimulation; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009431:Neural Conduction / Q000502:physiology; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D013997:Time Factors