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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