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Short- and Long-Term Changes in Joint Co
Short- and Long-Term Changes in Joint Co

Learning Sum-Product Networks with Direct and Indirect Variable
Learning Sum-Product Networks with Direct and Indirect Variable

... the grid independent from each other. Of all the possible clusterings that could be found, happening to find one of the separator sets is extremely unlikely. Learning a good structure for the next level of the SPN is even less likely, since it consists of 64 clustering problems, each working with 1/ ...
Learning Sum-Product Networks with Direct and Indirect Variable
Learning Sum-Product Networks with Direct and Indirect Variable

... the grid independent from each other. Of all the possible clusterings that could be found, happening to find one of the separator sets is extremely unlikely. Learning a good structure for the next level of the SPN is even less likely, since it consists of 64 clustering problems, each working with 1/ ...
Time representation in reinforcement learning models of
Time representation in reinforcement learning models of

Semantics and derivation for Stochastic Logic Programs
Semantics and derivation for Stochastic Logic Programs

lec12-dec11
lec12-dec11

The Past, Present, and Future of Cognitive Architectures - ACT-R
The Past, Present, and Future of Cognitive Architectures - ACT-R

Automatically Building Special Purpose Search Engines with
Automatically Building Special Purpose Search Engines with

... These arbitrary features are not independent. – Multiple levels of granularity (chars, words, phrases) ...
Integrating Programming by Example and Natural Language Programming
Integrating Programming by Example and Natural Language Programming

Data Visualization Optimization Computational Modeling of Perception
Data Visualization Optimization Computational Modeling of Perception

Using Expectations to Drive Cognitive Behavior
Using Expectations to Drive Cognitive Behavior

... In ACT-R, productions make requests for chunks in declarative memory by specifying certain constraints on the slot values of chunks. These constraints can range from the very specific where every slot and value of the desired chunk is specified to the very general (akin to free association) where th ...
The Computation and Comparison of Value in Goal
The Computation and Comparison of Value in Goal

... target. The difficulty of the task is varied across trials by changing the percentage of dots that are moving coherently. Subjects are rewarded a fixed amount for correct answers, and receive nothing for incorrect responses. (For more details on this task see Chapters 4 and 30 of this volume). At fi ...
The Relative Expressiveness of Abstract Argumentation and Logic
The Relative Expressiveness of Abstract Argumentation and Logic

Development of neuromotor prostheses
Development of neuromotor prostheses

... electrode arrays, which provide flexibility using a biocompatible material, are also in development (Moxon, 1999; Rousche et al., 2001). These electrodes can conform to different shaped surfaces, but flexibility can make them difficult to insert. Trophic factors are being developed to attract neurit ...
Teacher Guide
Teacher Guide

View PDF - Advances in Cognitive Systems
View PDF - Advances in Cognitive Systems

... Thought lecture, she argued that the right approach to AI is to: • Divide the problem into well-defined pieces • Make progress on each one • Build bridges to create a unified whole The problem with this model is that the individual solutions may be too far apart (as Koller herself points out), and n ...
Neural Network Applications in Stock Market Predictions
Neural Network Applications in Stock Market Predictions

Improved Gaussian Mixture Density Estimates Using Bayesian
Improved Gaussian Mixture Density Estimates Using Bayesian

... The performances on training and test set are measured in terms of the model loglikelihood. Larger values indicate a better performance. We report separate results for dass A and B, since the densities of both were estimated separately. The final column shows the prediction accuracy in terms of the ...
www.informatik.uni
www.informatik.uni

... A standard way to achieve such a mapping is to feed two inputs into the hidden layer such as Cartesian coordinates c and head rotation r. These inputs use population codes xc and xr where the location of an approximately Gaussianshaped activation hill encodes the value. Both inputs are used in a sym ...
Neural Networks algorithms. ppt
Neural Networks algorithms. ppt

... • Mapping character strings into phonemes so they can be pronounced by a computer • Neural network trained how to pronounce each letter in a word in a sentence, given the three letters before and three letters after it in a window • Output was the correct phoneme ...
Complex Cell-like Direction Selectivity through Spike
Complex Cell-like Direction Selectivity through Spike

... In the other chain. (b) Recurrent connections to a given neuron (labeled '0') arise from 4 preceding and 4 succeeding neurons in its chain. Inhibition at a given neuron IS mediated via a GABAergic interneuron (darkened circle). (c) Synaptic strength of recurrent excitatory (EXC) and inhibitory (INH) ...
Voltage-Sensitive Dye Imaging: Technique review and Models
Voltage-Sensitive Dye Imaging: Technique review and Models

Spring 2002
Spring 2002

Aggregate Input-Output Models of Neuronal Populations
Aggregate Input-Output Models of Neuronal Populations

ppt - of Dushyant Arora
ppt - of Dushyant Arora

... • Simplified model of human brain. • Massively parallel distributed processing system. ...
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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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