Neural network model of nonlinear Bayesian learning*

Animals learn sensorimotor tasks by performing actions, getting sensory feedback, and incorporating the feedback into their knowledge of the world to guide future actions. Previous work from our group revealed that this process, in the context of pitch compensation in songbirds learning to sing, can be modeled by a Bayesian filter with non-Gaussian probability distributions. How such computations can be implemented using neural networks remains unknown. Inspired by line attractor neural network models, we develop a neural network with stochastic dynamics, which stores information about optimal actions in the positions of activity bumps, and represents the growing uncertainty about the actions in the diffusion of these bumps with time. The model explains salient features of experiments, such as a dynamically changing variability of actions, a bimodal distribution of actions, jumps and continuous responses to external perturbation, and a common disregard for sensory feedback that contradicts prior expectations.