L5-BinocularRivalry

Bayesian inference can be burdened by prior

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Unformatted text preview: and chemical architectures. Big questions remain regarding sensible chemical inference, the right choice of neural architecture, how to implement complex models and energy costs. Bayesian inference can be burdened by prior information in an un-modeled non-stationary world. NEXT: Control. Laplace transforms to study ODE systems. Nick Jones Sampling and the Brain: Inference, Control and Driving Bibliography The paper below is the basis for this lecture. There are a few bugs but it’s both interesting and well written. There are a couple of implementation disconnects. In their paper: a) the term απji in the i Hamiltonian should be −απn b) they actually used a smaller lattice than they state (a 5 by 5 image - that helps explain Fig. 2a) c) they suppose the input image has values +1 or −1 and they used w in a discretized fashion. There are also some differences between the form we discuss and implement mostly for reasons of simplicity. Multistability and Perceptual Inference - Samuel J. Gershman Edward Vul Joshua B. Tenenbaum - Neural Computation 2012 Society of America A, 20(7):14341448, 2003. Nick Jones Sampling and the Brain: Inference, Control and Driving...
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This document was uploaded on 03/01/2014 for the course EE 208 at Imperial College.

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