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Activity Number: 171
Type: Contributed
Date/Time: Monday, August 3, 2009 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #304805
Title: Generalized Volterra Model of Neural Population Dynamics for Hippocampal Prostheses
Author(s): Dong Song*+ and Theodore W. Berger
Companies: University of Southern California and University of Southern California
Address: HNB 403, Los Angeles, CA, 90089,
Keywords: spike ; hippocampus ; generalized linear model ; Volterra kernel ; model ; EMG
Abstract:

Developing a hippocampal prosthesis requires restoring the transformation of population neural activities. To bypass a damaged region, output spike trains need to be predicted from the input spike trains and then reinstated through stimulation. We formulate a multiple-input, multiple-output (MIMO) nonlinear dynamic model for such task. A MIMO model is comprised of a series of multiple-input, single-output neuron models that each consists of feedforward Volterra kernels, a feedback Volterra kernel, a Gaussian noise, an adder, and a threshold. This model is equivalent to a generalized linear model with a probit link function and thus can be termed as generalized Volterra model (GVM). The GVM is applied successfully to the hippocampal CA3-CA1 population dynamics and can serve as the computational basis of the hippocampal prostheses.

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