Testing dynamical models of vision

"Most models of vision focus either on the spatial or temporal aspects of visual processing and neglect the other component. A variety of studies have shown, however, that spatial and temporal processing cannot easily be separated. The shine-through effect has proven to be a sensitive tool to study spatio-temporal processing. Two very different dynamical models, the 3D-LAMINART and the WCTM model, have explained the key aspects of the shine-through effect..."

 

 

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Testing dynamical models of vision

via ScienceDirect Publication: Vision Research on 12/3/10

Publication year: 2010
Source: Vision Research, In Press, Accepted Manuscript, Available online 3 December 2010
Johannes, Rüter , Greg, Francis , Patricia, Frehe , Michael H., Herzog
Most models of vision focus either on the spatial or temporal aspects of visual processing and neglect the other component. A variety of studies have shown, however, that spatial and temporal processing cannot easily be separated. The shine-through effect has proven to be a sensitive tool to study spatio-temporal processing. Two very different dynamical models, the 3D-LAMINART and the WCTM model, have explained the key aspects of the shine-through effect. Based on computer simulations, Francis (2009) proposed a set of predictions based on stimulus variants of the shine-through effect that are crucial for both models. Here, we tested these predictions...

 

 

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Cue combination on the circle and the sphere

These authors describe how optimal cue combination would work for non-linear quantities.

 

 

Feed: Journal of Vision current issue
Posted on: dinsdag 21 september 2010 10:44
Author: Murray, R. F., Morgenstern, Y.
Subject: Cue combination on the circle and the sphere

 

Bayesian cue combination models have been used to examine how human observers combine information from several cues to form estimates of linear quantities like depth. Here we develop an analogous theory for circular quantities like planar direction. The circular theory is broadly similar to the linear theory but differs in significant ways. First, in the circular theory the combined estimate is a nonlinear function of the individual cue estimates. Second, in the circular theory the mean of the combined estimate is affected not only by the means of individual cues and the weights assigned to individual cues but also by the variability of individual cues. Third, in the circular theory the combined estimate can be less certain than the individual estimates, if the individual estimates disagree with one another. Fourth, the circular theory does not have some of the closed-form expressions available in the linear theory, so data analysis requires numerical methods. We describe a vector sum model that gives a heuristic approximation to the circular theory's behavior. We also show how the theory can be extended to deal with spherical quantities like direction in three-dimensional space.

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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