Bayesian auxiliary particle filters for estimating neural tuning parameters.
A common challenge in neural engineering is to track the dynamic parameters of neural tuning functions. This work introduces the application of Bayesian auxiliary particle filters for this purpose. Based on Monte-Carlo filtering, Bayesian auxiliary particle filters use adaptive methods to model the prior densities of the state parameters being tracked. The observations used are the neural firing times, modeled here as a Poisson process, and the biological driving signal. The Bayesian auxiliary particle filter was evaluated by simultaneously tracking the three parameters of a hippocampal place cell and compared to a stochastic state point process filter. It is shown that Bayesian auxiliary particle filters are substantially more accurate and robust than alternative methods of state parameter estimation. The effects of time-averaging on parameter estimation are also evaluated.
Mountney
John
J
Department of Electrical & Computer Engineering, Temple University, Philadelphia, PA 19122, USA. jmm@temple.edu
Sobel
Marc
M
Obeid
Iyad
I
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted