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L5-BinocularRivalry - Sampling and the Brain Inference...

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Sampling and the Brain: Inference Control and Driving Nick Jones [email protected]
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So far What we have covered so far. Inference with chemicals and point process models for chemistry (and neurons). The problem of normalization. Sampling (Rejection, Importance, Metropolis, Gibbs). Encoding uncertain information using probabilistic population coding. Nick Jones Sampling and the Brain: Inference, Control and Driving
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Today: Binocular rivalry and Gibbs sampling We will now illustrate the preceding ideas with a study and implementation of the following paper: Multistability and Perceptual Inference - Samuel J. Gershman Edward Vul Joshua B. Tenenbaum - Neural Computation 2012 You can read about the basics of binocular rivalry here: http://www.scholarpedia.org/article/Binocular_rivalry 1 MCMC in the form of Gibbs sampling 2 (in passing an example of a Markov Random Field) 3 An alternative view to Probabilistic Population Coding In this example we are not attempting to encode entire distributions (this is what PPC does). Instead we are merely sampling from our posterior as in MCMC. Nick Jones Sampling and the Brain: Inference, Control and Driving
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Plan + Rivalry We’ll cover: 1 Binocular rivalry 2 The model 3 Implementation tips If we are presented with different images to our left eye x L and to our right eye x R then what image s do we perceive? Nick Jones Sampling and the Brain: Inference, Control and Driving
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Images and Perceptions If we are presented with different images to our left eye x L and to our right eye x R then what image s do we perceive? In practice you appear to see the image from one eye dominating for a while and then flipping to the other eye. Each image has N binary pixels and x i ∈ { 0 , 1 } where i ∈ { L , R } . We suppose that our perception of the n th pixel of the image is a linear combination s n = w n x L n - (1 - w n ) x R n . For simplicity we’ll treat w as a binary vector. Perceiving the image as only that from the left eye would be w = 1 .
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