TY - GEN
T1 - A neural implementation of the Kalman filter
AU - Wilson, Robert C.
AU - Finkel, Leif H.
PY - 2009
Y1 - 2009
N2 - Recent experimental evidence suggests that the brain is capable of approximating Bayesian inference in the face of noisy input stimuli. Despite this progress, the neural underpinnings of this computation are still poorly understood. In this paper we focus on the Bayesian filtering of stochastic time series and introduce a novel neural network, derived from a line attractor architecture, whose dynamics map directly onto those of the Kalman filter in the limit of small prediction error. When the prediction error is large we show that the network responds robustly to changepoints in a way that is qualitatively compatible with the optimal Bayesian model. The model suggests ways in which probability distributions are encoded in the brain and makes a number of testable experimental predictions.
AB - Recent experimental evidence suggests that the brain is capable of approximating Bayesian inference in the face of noisy input stimuli. Despite this progress, the neural underpinnings of this computation are still poorly understood. In this paper we focus on the Bayesian filtering of stochastic time series and introduce a novel neural network, derived from a line attractor architecture, whose dynamics map directly onto those of the Kalman filter in the limit of small prediction error. When the prediction error is large we show that the network responds robustly to changepoints in a way that is qualitatively compatible with the optimal Bayesian model. The model suggests ways in which probability distributions are encoded in the brain and makes a number of testable experimental predictions.
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M3 - Conference contribution
AN - SCOPUS:84863393459
SN - 9781615679119
T3 - Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
SP - 2062
EP - 2070
BT - Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
T2 - 23rd Annual Conference on Neural Information Processing Systems, NIPS 2009
Y2 - 7 December 2009 through 10 December 2009
ER -