title: Nonlinear modeling of neural population dynamics for hippocampal prostheses.
abstract: Developing a neural prosthesis for the damaged hippocampus requires restoring the transformation of population neural activities performed by the hippocampal circuitry. 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 the input-output transformation of spike trains. In this approach, a MIMO model comprises a series of physiologically-plausible multiple-input, single-output (MISO) neuron models that consist of five components each: (1) feedforward Volterra kernels transforming the input spike trains into the synaptic potential, (2) a feedback kernel transforming the output spikes into the spike-triggered after-potential, (3) a noise term capturing the system uncertainty, (4) an adder generating the pre-threshold potential, and (5) a threshold function generating output spikes. It is shown that this model is equivalent to a generalized linear model with a probit link function. To reduce model complexity and avoid overfitting, statistical model selection and cross-validation methods are employed to choose the significant inputs and interactions between inputs. The model is applied successfully to the hippocampal CA3-CA1 population dynamics. Such a model can serve as a computational basis for the development of hippocampal prostheses.
authors.affiliation: Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA. dsong@usc.edu
authors.forename: Dong
authors.initials: D
authors.lastname: Song
authors.forename: Rosa H M
authors.initials: RH
authors.lastname: Chan
authors.forename: Vasilis Z
authors.initials: VZ
authors.lastname: Marmarelis
authors.forename: Robert E
authors.initials: RE
authors.lastname: Hampson
authors.forename: Sam A
authors.initials: SA
authors.lastname: Deadwyler
authors.forename: Theodore W
authors.initials: TW
authors.lastname: Berger
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D056547:CA1 Region, Hippocampal / Q000502:physiology*; D056654:CA3 Region, Hippocampal / Q000502:physiology*; D005071:Evoked Potentials; D016014:Linear Models; D008297:Male; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009483:Neuropsychological Tests; D017711:Nonlinear Dynamics; D019736:Prostheses and Implants; D051381:Rats; D020318:Rats, Long-Evans; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D035501:Uncertainty