Receptive Field Inference with Localized Priors
Figure 1
Neural encoding model and empirical Bayes receptive field inference.
(A) Linear Gaussian encoding model: the stimulus is projected on the receptive field
and Gaussian noise is added to produce the neural response
. (B) Graphical model for a hierarchical Bayesian receptive field model. The hyperparameters
specify a prior over the receptive field
, which together with stimulus
determines the conditional probability of neural response
. Circles indicate variables, arrows indicate conditional dependence, and the square denotes a pair of variables (stimulus
and response
) that are observed many times. (C) Empirical Bayes involves a two-stage inference procedure: first, maximize the evidence
for
(left), which can be computed by integrating out
from the generative model in (B); second, maximize the posterior over
given the data and estimated hyperparameters
(right). See text for details.