Learning Priors for Bayesian Computations in the Nervous System

 

Feed: PLoS ONE Alerts: Neuroscience
Author: Max Berniker et al.
Subject: Learning Priors for Bayesian Computations in the Nervous System

 

Our nervous system continuously combines new information from our senses with information it has acquired throughout life. Numerous studies have found that human subjects manage this by integrating their observations with their previous experience (priors) in a way that is close to the statistical optimum. However, little is known about the way the nervous system acquires or learns priors. Here we present results from experiments where the underlying distribution of target locations in an estimation task was switched, manipulating the prior subjects should use. Our experimental design allowed us to measure a subject's evolving prior while they learned. We confirm that through extensive practice subjects learn the correct prior for the task. We found that subjects can rapidly learn the mean of a new prior while the variance is learned more slowly and with a variable learning rate. In addition, we found that a Bayesian inference model could predict the time course of the observed learning while offering an intuitive explanation for the findings. The evidence suggests the nervous system continuously updates its priors to enable efficient behavior.

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Suggested by Maarten

Causal inference in perception

http://www.cell.com/trends/cognitive-sciences/abstract/S1364-6613%2810%2900147-6

 

 

Optimal population coding by noisy spiking neurons

Feed: Proceedings of the National Academy of Sciences current issue
Posted on: dinsdag 10 augustus 2010 18:30
Author: Tkacik, G., Prentice, J. S., Balasubramanian, V., Schneidman, E.
Subject: Optimal population coding by noisy spiking neurons [Physics]

 

In retina and in cortical slice the collective response of spiking neural populations is well described by "maximum-entropy" models in which only pairs of neurons interact. We asked, how should such interactions be organized to maximize the amount of information represented in population responses? To this end, we extended the linear-nonlinear-Poisson model of single neural response to include pairwise interactions, yielding a stimulus-dependent, pairwise maximum-entropy model. We found that as we varied the noise level in single neurons and the distribution of network inputs, the optimal pairwise interactions smoothly interpolated to achieve network functions that are usually regarded as discrete—stimulus decorrelation, error correction, and independent encoding. These functions reflected a trade-off between efficient consumption of finite neural bandwidth and the use of redundancy to mitigate noise. Spontaneous activity in the optimal network reflected stimulus-induced activity patterns, and single-neuron response variability overestimated network noise. Our analysis suggests that rather than having a single coding principle hardwired in their architecture, networks in the brain should adapt their function to changing noise and stimulus correlations.

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Suggested by Maarten