Learning to Estimate Dynamical State with Probabilistic Population Codes.

ABSTRACT: Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We sho

SUBMITTER: Makin JG 

PROVIDER: S-EPMC4634970 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature