Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian computation in recurrent neural circuits
Neural Computation
Two-dimensional discrete Markovian fields
IEEE Transactions on Information Theory
Shape from Shading Using Probability Functions and Belief Propagation
International Journal of Computer Vision
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There is growing evidence that neural circuits may employ statistical algorithms for inference and learning. Many such algorithms can be derived from independence diagrams (graphical models) showing causal relationships between random variables. A general algorithm for inference in graphical models is belief propagation, where nodes in a graphical model determine values for random variables by combining observed values with messages passed between neighboring nodes. We propose that small groups of synaptic connections between neurons in cortex correspond to causal dependencies in an underlying graphical model. Our results suggest a new probabilistic framework for computation in the neocortex.