[Report] A Large-Scale Model of the Functioning Brain

The brain has been solved, finally! What took them so long? :)

 

 

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[Report] A Large-Scale Model of the Functioning Brain

per Science: Current Issue autorius Chris Eliasmith 12.11.29

Two-and-a-half million model neurons recognize images, learn via reinforcement, and display fluid intelligence.

Authors: Chris Eliasmith, Terrence C. Stewart, Xuan Choo, Trevor Bekolay, Travis DeWolf, Charlie Tang, Daniel Rasmussen

 

 

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Compressive neural representation of sparse, high-dimensional probabilities....

 

 

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Compressive neural representation of sparse, high-dimensional probabilities. (arXiv:1206.1800v1 [q-bio.NC])

via q-bio.NC updates on arXiv.org by <a href="http://arxiv.org/find/q-bio/1/au:+Pitkow_X/0/1/0/all/0/1">Xaq Pitkow</a> on 6/10/12

This paper shows how sparse, high-dimensional probability distributions could be represented by neurons with exponential compression. The representation is a novel application of compressive sensing to sparse probability distributions rather than to the usual sparse signals. The compressive measurements correspond to expected values of nonlinear functions of the probabilistically distributed variables. When these expected values are estimated by sampling, the quality of the compressed representation is limited only by the quality of sampling. Since the compression preserves the geometric structure of the space of sparse probability distributions, probabilistic computation can be performed in the compressed domain. Interestingly, functions satisfying the requirements of compressive sensing can be implemented as simple perceptrons. If we use perceptrons as a simple model of feedforward computation by neurons, these results show that the mean activity of a relatively small number of neurons can accurately represent a high-dimensional joint distribution implicitly, even without accounting for any noise correlations. This comprises a novel hypothesis for how neurons could encode probabilities in the brain.

 

 

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for the next M-exhibition?

http://coolsciencenews.blogspot.be/2012/06/3d-mapping-will-allow-us-to-look-inside.html
--
Suggested reading by Ervin

Masking of Figure-Ground Texture and Single Targets by Surround Inhibition: ...

 

 

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Masking of Figure-Ground Texture and Single Targets by Surround Inhibition: A Computational Spiking Model

via PLoS ONE Alerts: Neuroscience by Hans Supèr et al. on 2/29/12

by Hans Supèr, August Romeo

A visual stimulus can be made invisible, i.e. masked, by the presentation of a second stimulus. In the sensory cortex, neural responses to a masked stimulus are suppressed, yet how this suppression comes about is still debated. Inhibitory models explain masking by asserting that the mask exerts an inhibitory influence on the responses of a neuron evoked by the target. However, other models argue that the masking interferes with recurrent or reentrant processing. Using computer modeling, we show that surround inhibition evoked by ON and OFF responses to the mask suppresses the responses to a briefly presented stimulus in forward and backward masking paradigms. Our model results resemble several previously described psychophysical and neurophysiological findings in perceptual masking experiments and are in line with earlier theoretical descriptions of masking. We suggest that precise spatiotemporal influence of surround inhibition is relevant for visual detection.

 

 

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Are computational models of any use to psychiatry?

 

 

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Are computational models of any use to psychiatry?

via ScienceDirect Publication: Neural Networks on 3/10/11

Publication year: 2011
Source: Neural Networks, In Press, Accepted Manuscript, Available online 10 March 2011
Quentin J.M., Huys , Michael, Moutoussis , Jonathan, Williams
Mathematically rigorous descriptions of key hypotheses and theories are becoming more common in neuroscience and are beginning to be applied to psychiatry. In this article two fictional characters, Dr. Strong and Mr. Micawber, debate the use of such computational models (CMs) in psychiatry. We present four fundamental challenges to the use of CMs in psychiatry: (a) the applicability of mathematical approaches to core concepts in psychiatry such as subjective experiences, conflict and suffering; in short, CMs have yet to influence psychiatric practice; (b) whether psychiatry is mature enough to allow informative modelling; (c) whether theoretical techniques are powerful enough to...

 

 

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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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