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Word - Egodeath.com
Word - Egodeath.com

... The Workspace — Copycat’s Locus of Perceptual Activity The Constant Fight for Probabilistic Attention The Parallel Emergence of Multi-Level Perceptual Structures The Drive Towards Global Coherence and Towards Deep Concepts The Coderack — Source of Emergent Pressures in Copycat Pressures Determine th ...
Mirror Proposal 8-01 - USC - University of Southern California
Mirror Proposal 8-01 - USC - University of Southern California

... The modeling environment will include a primatoid hand-arm avatar for generating actions (to provide output in studies of learning to grasp, and input stimuli for studies of action recognition); preprocessing routines for visual input; and tools for modeling adaptive networks of biologically plausib ...
Consolidation
Consolidation

- MIT Press Journals
- MIT Press Journals

PVLV: The Primary Value and Learned Value
PVLV: The Primary Value and Learned Value

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The Quantitative Analysis Approach

... misunderstood (and feared) because of their mathematical complexity • Tend to downplay the role and value of nonquantifiable information • Often have assumptions that oversimplify the variables of the real world To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna ...
An Efficient Learning Procedure for Deep Boltzmann Machines
An Efficient Learning Procedure for Deep Boltzmann Machines

The Relationship Between Synchronization Among Neuronal
The Relationship Between Synchronization Among Neuronal

Towards a Cognitive Architecture for Music Perception
Towards a Cognitive Architecture for Music Perception

Probabilistic models for spike trains of single neurons
Probabilistic models for spike trains of single neurons

A Parallel-Process Model of On-Line Inference Processing
A Parallel-Process Model of On-Line Inference Processing

... If the lexical access process does in fact work as described above, and if individual words trigger the higherlevel pragmatic inferences, then it is likely that the pragmatic inference decision process is much the same as the lexical inference decision process. Work on ATLAST goes under the assumpti ...
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Functional Connectivity during Surround Suppression in

Computational Intelligence: Neural Networks and
Computational Intelligence: Neural Networks and

... Neural networks (NNs) are an abstraction of natural processes, more specifically of the functioning of biological neurons. Since early 1990s, they have been combined into a general and unified computational model of adaptive systems to utilize their learning power and adaptive capabilities. The two ...
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Deep Learning Overview

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Insect Bio-inspired Neural Network Provides New Evidence on How

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Research and Development of Granular Neural Networks

... then transform the problem into the corresponding problem of this space to solve. If the problem has no solution in the coarse granularity space, in according to “false principle of protection”, we know immediately that the original problem has no solution. Because of small spatial scale of the coar ...
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... thresholds for the 2T conditions were in the range of 20% of the target (Wright et al., 1997; Karmarkar and Buonomano, 2003). A two-way analysis of variance (ANOVA) revealed a significant interaction between conditions (FIX 3 VAR) and tone number (2T 3 3T; F = 57.75; n = 15; p < 0.0001), demonstrati ...
Mapping Function Onto Neuronal Morphology
Mapping Function Onto Neuronal Morphology

... final morphologies and simulations of the electrophysiological neuronal dynamics should be compared with biological data. The compartmental models we used are based on the “well-established” description of current flow in neurons by the cable equation (Rall ...
Are fast/slow process in motor adaptation and forward/inverse
Are fast/slow process in motor adaptation and forward/inverse

... time constant. They could accurately predict motor responses to novel force fields and other forms of disturbance and quantify the patterns of generalization [5–8]. However, most of these models were unable to explain some of the observations such as the phenomenon of savings, spontaneous recovery, a ...
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1 WEATHER PREDICTION EXPERT SYSTEM

... fields of expertise. “There are only two methods to predict weather: the empirical approach and the dynamical approach” (Lorenz 19) [1]. Lorenz thus separated weather forecasting methodologies into two main branches in terms of numerical modeling and scientific processing (AI) of meteorological data ...
PHS 398 (Rev. 9/04), Biographical Sketch Format Page
PHS 398 (Rev. 9/04), Biographical Sketch Format Page

... significant shear forces due to the fact that the electrodes are made of materials that are much less compliant than the neural tissue. These shear forces, exacerbated by the tethering forces generated by the electrode interconnects, cause an encapsulation tissue that forms in long term implants. Th ...
Understanding mirror neurons - LIRA-Lab
Understanding mirror neurons - LIRA-Lab

... Recently, the visual responses of F5 “canonical” neurons have been re-examined using a formal behavioral paradigm, which allowed testing the response related to object observation both during the waiting phase between object presentation and movement onset and during movement execution (Murata et al ...
Learning Efficient Markov Networks - Washington
Learning Efficient Markov Networks - Washington

Neural Coding and Auditory Perception
Neural Coding and Auditory Perception

A Stereoscopic Look at Visual Cortex
A Stereoscopic Look at Visual Cortex

... and receives extensive inputs from the dorsal stream (Baizer et al. 1991; Saleem et al. 2000), leaving open the possibility that the relevant processing is happening in dorsal cortex and is later relayed to ventral cortex. Three very recent papers now provide us with a clearer picture. They indicate ...
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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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