Title :
Non-Linear Neural Spike Train Decoding via Polynomial Kernel Regression
Author :
Cassidy, A. ; Etienne-Cummings, Ralph
Author_Institution :
Johns Hopkins Univ., Baltimore
Abstract :
Neural spike train decoding is an important task for understanding how the biological nervous system performs computation and communication. Interest in this field has grown due to recent advances in neural prosthetics, as well as the need to explore non-traditional computational architectures. Current methods deal poorly with or ignore altogether the non-linearities inherent in neural computation. In this paper, we explore polynomial kernel regression as a method for for dealing with neural non-linearities. Two experiments, based on sensory perception and motor control, demonstrate the ability of the approach to decode the neural spike code using synthetic data.
Keywords :
decoding; mechanoception; neurophysiology; nonlinear systems; prosthetics; regression analysis; biological nervous system; motor control; neural computation; neural prosthetics; nonlinear neural spike train decoding; polynomial kernel regression; sensory perception; Biological information theory; Biology computing; Central nervous system; Kernel; Maximum likelihood decoding; Motor drives; Nervous system; Polynomials; Prosthetics; Retina; Animals; Central Nervous System; Humans; Models, Biological; Nerve Net; Neurons; Prostheses and Implants;
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
Print_ISBN :
978-1-4244-0787-3
DOI :
10.1109/IEMBS.2007.4353238