Neural decoding of movements: From linear to nonlinear trajectory models.
In: Ishikawa, M and Doya, K and Miyamoto, H and Yamakawa, T, (eds.)
NEURAL INFORMATION PROCESSING, PART I.
(pp. 586 - 595).
To date, the neural decoding of time-evolving physical state for example, the path of a foraging rat or arm movements - has been largely carried out using linear trajectory models, primarily due to their computational efficiency. The possibility of better capturing the statistics of the movements using nonlinear trajectory models, thereby yielding more accurate decoded trajectories, is enticing. However, nonlinear decoding usually carries a higher computational cost, which is an important consideration in real-time settings. In this paper, we present techniques for nonlinear decoding employing modal Gaussian approximations, expectatation propagation, and Gaussian quadrature. We compare their decoding accuracy versus computation time tradeoffs based on high-dimensional simulated neural spike counts.
|Title:||Neural decoding of movements: From linear to nonlinear trajectory models|
|Event:||14th International Conference on Neural Information Processing (ICONIP 2007)|
|Dates:||2007-11-13 - 2007-11-16|
|Keywords:||nonlinear dynamical models, nonlinear state estimation, neural decoding, neural prosthetics, expectation-propagation, Gaussian quadrature, GOAL-DIRECTED MOVEMENTS, HIPPOCAMPAL PLACE CELLS, CORTICAL CONTROL, KALMAN FILTER, ALGORITHMS, SIGNALS, ARM, RECONSTRUCTION, PROSTHETICS, PREDICTION|
|UCL classification:||UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit
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