Tracking temporal evolution of nonlinear dynamics in hippocampus using time-varying volterra kernels.
Hippocampus and other parts of the cortex are not stationary, but change as a function of time and experience. The goal of this study is to apply adaptive modeling techniques to the tracking of multiple-input, multiple-output (MIMO) nonlinear dynamics underlying spike train transformations across brain subregions, e.g. CA3 and CA1 of the hippocampus. A stochastic state point process adaptive filter will be used to track the temporal evolutions of both feedforward and feedback kernels in the natural flow of multiple behavioral events.
Chan
Rosa H M
RH
Department of Biomedical Engineering, University of Southern California, Los Angeles, 90089, USA. homchan@usc.edu
Song
Dong
D
Berger
Theodore W
TW
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D003198:Computer Simulation; D006624:Hippocampus / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D009435:Synaptic Transmission / Q000502:physiology*