Nonlinear dynamic modeling of spike train transformations for hippocampal-cortical prostheses.
One of the fundamental principles of cortical brain regions, including the hippocampus, is that information is represented in the ensemble firing of populations of neurons, i.e., spatio-temporal patterns of electrophysiological activity. The hippocampus has long been known to be responsible for the formation of declarative, or fact-based, memories. Damage to the hippocampus disrupts the propagation of spatio-temporal patterns of activity through hippocampal internal circuitry, resulting in a severe anterograde amnesia. Developing a neural prosthesis for the damaged hippocampus requires restoring this multiple-input, multiple-output transformation of spatio-temporal patterns of activity. Because the mechanisms underlying synaptic transmission and generation of electrical activity in neurons are inherently nonlinear, any such prosthesis must be based on a nonlinear multiple-input, multiple-output model. In this paper, we have formulated the transformational process of multi-site propagation of spike activity between two subregions of the hippocampus (CA3 and CA1) as the identification of a multiple-input, multiple-output (MIMO) system, and proposed that it can be decomposed into a series of multiple-input, single-output (MISO) systems. Each MISO system is modeled as a physiologically plausible structure that consists of 1) linear/nonlinear feedforward Volterra kernels modeling synaptic transmission and dendritic integration, 2) a linear feedback Volterra kernel modeling spike-triggered after-potentials, 3) a threshold for spike generation, 4) a summation process for somatic integration, and 5) a noise term representing intrinsic neuronal noise and the contributions of unobserved inputs. Input and output spike trains were recorded from hippocampal CA3 and CA1 regions of rats performing a spatial delayed-nonmatch-to-sample memory task that requires normal hippocampal function. Kernels were expanded with Laguerre basis functions and estimated using a maximum-likelihood method. Complexity of the feedforward kernel was progressively increased to capture higher-order system nonlinear dynamics. Results showed higher prediction accuracies as kernel complexity increased. Self-kernels describe the nonlinearities within each input. Cross-kernels capture the nonlinear interaction between inputs. Second- and third-order nonlinear models were found to successfully predict the CA1 output spike distribution based on CA3 input spike trains. First-order, linear models were shown to be insufficient.
Song
Dong
D
Department of Biomedical Engineering, Program in Neuroscience, Center for Neural Engineering, University of Southern California, Los Angeles, CA 90089, USA. dsong@usc.edu
Chan
Rosa H M
RH
Marmarelis
Vasilis Z
VZ
Hampson
Robert E
RE
Deadwyler
Sam A
SA
Berger
Theodore W
TW
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology*; D003072:Cognition Disorders / Q000503:physiopathology / Q000534:rehabilitation; D003198:Computer Simulation; D046690:Deep Brain Stimulation / Q000295:instrumentation / Q000379:methods*; D004558:Electric Stimulation / Q000295:instrumentation / Q000379:methods*; D006624:Hippocampus / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D011474:Prosthesis Design