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Longtin - noise in neural systems
Longtin - noise in neural systems

... which analyses must contend, and the simplest to deal with mathemati- ...
Functionally Independent Columns of Rat Somatosensory Barrel
Functionally Independent Columns of Rat Somatosensory Barrel

concurrent, distributed control of saccade initiation in the frontal eye
concurrent, distributed control of saccade initiation in the frontal eye

... with saccadic eye movements and visual fixation33-49. These neurons are organised into a two-dimensional motor map coding for saccades directed to the contralateral visual field 50 . Neurons increasing their discharges before and during saccades – referred to hereafter as saccade neurons – are distr ...
presentation
presentation

Neural Coding of Distinct Statistical Properties of
Neural Coding of Distinct Statistical Properties of

... a ‘gamble’ depends on the perceived value and likelihood of both the obtained outcome and its alternatives (Mellers, 2000). Without this common null outcome, smaller wins could be experienced as more pleasurable than larger wins for the same reward probabilities between two slot machines (e.g. it wo ...
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Man to Machine, Applications in Electromyography
Man to Machine, Applications in Electromyography

... muscle fibers of one motor unit are not bundled within a muscle. Rather they are spread throughout the muscle with a relatively uniform distribution. When a series of NAP’s reach the cells of a muscle unit they cause a release of chemical transmitters which depolarize the cell to a threshold value, ...
Aalborg Universitet
Aalborg Universitet

... This thesis is about decision theoretic troubleshooting (or simply troubleshooting) which in many ways can be seen as an exact science for "detection". However, at the core of decision theoretic troubleshooting is not only the goal of arriving at the true diagnosis or conclusion of a given problem, ...
Artificial Intelligence
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... Preface Preface to the second edition ...
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Independent component analysis of neural populations from

... within the populations. It is assumed that such independent information flows may occur regardless of possible feedbacks, while to make conclusions on independent populations one must carefully consider the underlying neuroanatomy. Conclusions on the neural populations may be made with higher confid ...
The Simulation of Action Strategies of Different Personalities
The Simulation of Action Strategies of Different Personalities

... the mean of the participants’ results of those who used the nucleotides-first-strategy (n=6).…………………………214 Mean of the results for the set of parameters (B) and the mean of the participants’ results of those who used the nucleotides-first-strategy (n=6) ……………………….214 Mean of the results for the set ...
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... Little is known about the role of feed-forward inhibition in shaping the receptive field properties of neurons sensitive to approaching objects (looming stimuli). Such neurons have been described in the visual system of several species, including monkeys, pigeons, and insects (Cooke and Graziano 200 ...
What clinical disorders tell us about the neural
What clinical disorders tell us about the neural

... Saccades are rapid eye movements that redirect the fovea from one object to another. A great deal has been learned about the anatomy and physiology of saccades, making them an ideal system for studying the neural control of movement. Basic research on normal eye movements has greatly increased our u ...
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Motif distribution, dynamical properties, and computational

A self-organizing model of disparity maps in the primary visual cortex
A self-organizing model of disparity maps in the primary visual cortex

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fulltext

Statistics and geometry of orientation selectivity in primary visual
Statistics and geometry of orientation selectivity in primary visual

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Cortical areas are linked through pathways which originate and

... Structural analysis as defined in this study classifies areas into a few cortical types, determined by the number of identifiable layers in each area and by how distinct the layers are from each other. By contrast, cytoarchitectonic analysis is a more detailed process, which identifies cortical type ...
differential evolution based classification with pool of
differential evolution based classification with pool of

... total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method con ...
NIH Public Access
NIH Public Access

Analysis of sleep spindles and model of their generation
Analysis of sleep spindles and model of their generation

Understanding Circuit Dynamics Using the Stomatogastric Nervous
Understanding Circuit Dynamics Using the Stomatogastric Nervous

... ANRV300-PH69-13 ARI 8 January 2007 ...
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Philosophical Aspects in Pattern Recognition Research

Adding Consciousness to Cognitive Architectures
Adding Consciousness to Cognitive Architectures

... 14.1 Example of a RCS hierarchy from [1], in which there can be appreciated the different resolution levels . . . . . . . . . . . 14.2 Functional structure of a RCS node . . . . . . . . . . . . . . 14.3 World Modelling and Value Judgement processes (from [1]) . 14.4 Internal structure of Behaviour Ge ...
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Neural modeling fields

Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS).This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of mind’s mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, hetero-hierarchical system. At each level in NMF there are concept-models encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are governed by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals.
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