By Rajesh P. N. Rao, Bruno A. Olshausen, Michael S. Lewicki

ISBN-10: 0262182246

ISBN-13: 9780262182249

ISBN-10: 0585437122

ISBN-13: 9780585437125

Neurophysiological, neuroanatomical, and mind imaging reports have helped to make clear how the mind transforms uncooked sensory details right into a shape that's invaluable for goal-directed habit. A basic query that's seldom addressed by way of those reviews, besides the fact that, is why the mind makes use of the categories of representations it does and what evolutionary virtue, if any, those representations confer. it truly is tricky to deal with such questions at once through animal experiments. A promising substitute is to take advantage of probabilistic rules reminiscent of greatest probability and Bayesian inference to derive versions of mind functionality. This ebook surveys a few of the present probabilistic ways to modeling and knowing mind functionality. even if lots of the examples specialize in imaginative and prescient, some of the types and methods are appropriate to different modalities besides. The ebook offers top-down computational versions in addition to bottom-up neurally encouraged types of mind functionality. the themes coated contain Bayesian and information-theoretic types of conception, probabilistic theories of neural coding and spike timing, computational types of lateral and cortico-cortical suggestions connections, and the advance of receptive box houses from normal indications.

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**Extra info for Probabilistic models of the brain: Perception and neural function**

**Sample text**

When we have no reason to expect observers to have a particular bias, we give the corresponding parameter a uniform prior distribution. In contrast, where biases are expected, the prior is modeled as a non-uniform distribution where the modes of the distribution match the biases. g. Gaussian). For instance, among all possible light source directions in the frontal plane, only one direction is preferred. Moreover, we showed how these biases can be estimated experimentally [11, 12]. So far, this is pretty much the Bayesian methodology.

5 shows the posterior distribution when the likelihood function was computed as in Fig. 3 and the prior probability was chosen as in Fig. 4 . Note that the peak of the posterior distribution is located between the peaks of the likelihood function (Fig. 3) and the prior distribution (Fig. 4). Bayesian Modelling of Visual Perception 21 In effect, the two sources of information we have are being pooled. The likelihood function would lead us to pick values of near 135 degrees as our maximum likelihood guess, the prior would lead us to favor a guess nearer 90 degrees, reﬂecting knowledge of the Demon’s preference.

Question for natural visual tasks. Statistical inference theory, and in particular the speciﬁc form we describe below as pattern inference theory, plays a role in the analysis of both kinds of perceptual behavior–but is particularly relevant for developing quantitative predictive models of visual function. In this chapter, we describe a framework within which to develop and test predictive quantitative theories of human visual behavior as pertains to both mechanism and function. What are we asking for in a quantitative theory?

### Probabilistic models of the brain: Perception and neural function by Rajesh P. N. Rao, Bruno A. Olshausen, Michael S. Lewicki

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