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The Role of Knowledge Modeling Techniques in Software
The Role of Knowledge Modeling Techniques in Software

... support an adequate conversation model between the user and the system. These top-level tasks may be the basic types of answers required in such a conversation model. For each toplevel task, a hierarchical structure of task-method-domain may be used to show the way the final task supports an answer ...
Spike-timing dependent plasticity and the cognitive map
Spike-timing dependent plasticity and the cognitive map

Solving the Distal Reward Problem through
Solving the Distal Reward Problem through

... where sd is the time constant of DA uptake and DA(t) models the source of DA due to the activity of dopaminergic neurons in the midbrain structures VTA and substantia nigra pars compacta. A better description of DA kinetics, based on Michaelis--Menten formalism, was recently suggested by Montague et ...
The Control of Rate and Timing of Spikes in the Deep Cerebellar
The Control of Rate and Timing of Spikes in the Deep Cerebellar

A Hebbian learning rule gives rise to mirror neurons and links them
A Hebbian learning rule gives rise to mirror neurons and links them

Learning Vector Representations for Sentences
Learning Vector Representations for Sentences

... 2.3 (a) One-layer neural network. (b) Two-layer neural network (biases are removed for simplicity). . . . . . . . . . . . . . . . . . . . . . . 11 2.4 The role of the hidden layer in a two-layer feed-forward neural network is to project the data onto another vector space in which they are now linear ...
MIrror neuRons based RObot Recognition - LIRA-Lab
MIrror neuRons based RObot Recognition - LIRA-Lab

Synaptic function: Dendritic democracy
Synaptic function: Dendritic democracy

Automatic Melakarta Raaga Identification Syste Carnatic
Automatic Melakarta Raaga Identification Syste Carnatic

An Introduction to Variational Methods for Graphical Models
An Introduction to Variational Methods for Graphical Models

... by-product of the calculation of P(H | E). Moreover, algorithms that maximize likelihood (and related quantities) generally make use of the calculation of P(H | E) as a subroutine. Although there are many cases in which the exact algorithms provide a satisfactory solution to inference and learning p ...
Optimal Sizes of Dendritic and Axonal Arbors
Optimal Sizes of Dendritic and Axonal Arbors

... output layer, Figure 1. As a result, output neurons form an orderly map of the input layer. I characterize inter-neuronal connectivity for a topographic projection by divergence and convergence factors defined as follows, Figure 1. Divergence, D, of the projection is the number of output neurons whi ...
Down - 서울대 : Biointelligence lab
Down - 서울대 : Biointelligence lab

...  The activation of the middle node (which responds maximally to an orientation of 0 degrees) is plotted against the input orientation with open squares (in Fig. 7.1B)  The model data match the experimental data reasonably well ...
A model for experience-dependent changes in the responses of inferotemporal neurons
A model for experience-dependent changes in the responses of inferotemporal neurons

... response of all recorded IT neurons during a DMS task with delay (Miller and Desimone 1993, Dudkin et al 1994). Finally, cholinergic neurons of the basal forebrain project to IT cortex (Mesulam et al 1983). This suggests that acetylcholine can modulate the responses of IT neurons. Cholinergic agonis ...
reviews - Center for Complex Systems and Brain Sciences
reviews - Center for Complex Systems and Brain Sciences

... indicate that we are not blind to the world outside the focus of attention. Thus we can make simple judgments on objects to which we are not attending32, although those judgments are limited and less accurate than those made in the presence of attention2,12,13,33–36. So although attention does not s ...
ppt - CSE, IIT Bombay
ppt - CSE, IIT Bombay

The functional asymmetry of auditory cortex is reflected
The functional asymmetry of auditory cortex is reflected

... We began by studying the input to neurons in L2/3 in a coronal slice that preserves isofrequency bands (Fig. 1a, top); that is, in a slice containing neurons that respond to similar frequencies9. The organi­ zation of the primary auditory cortex along this axis has recently been characterized in the ...
Dopamine – CNS Pathways and Neurophysiology
Dopamine – CNS Pathways and Neurophysiology

C:\Vision\15Higher level Pt 2.wpd
C:\Vision\15Higher level Pt 2.wpd

... 1.2.3 on the phylogeny of vision. This material highlights the fact that a selection of primates can no longer be used in research related to the maximum performance of the human visual system. The system contains components and circuits that are either rudimentary or absent in these lower species. ...
Functional territories in primate substantia nigra pars reticulata
Functional territories in primate substantia nigra pars reticulata

... respectively. For each object set, no more than one training session was conducted in 1 day. To test the neuronal representation of stable object-value memories, we used a passive viewing task (Fig. 1C). While the monkey was fixating a central spot of light, one to four fractal objects (pseudorandom ...
User Models, Intelligent Interface Agents and Expert Systems
User Models, Intelligent Interface Agents and Expert Systems

... particular class. This may allow for future improvements to the system based on usage. Several problems can arise from user classification. First, a user may belong to more than one user class. For example, consider the computer scientist concerned with the development of the expert system (i.e., re ...
Condition interference in rats performing a choice task with switched
Condition interference in rats performing a choice task with switched

... store outcomes of options in any situation are crucial. Despites its importance in action learning, decision processes and neural substrates involved in various situations are still unclear, partly because behavioral experiments have usually been designed to eliminate situational effects as far as p ...
Neural Correlates of Object-Associated Choice Behavior
Neural Correlates of Object-Associated Choice Behavior

... the neuronal firing patterns associated with the correct and error trials were significantly different from each another while holding either the object or the choice factor constant. For example, the trials associated with the toy category were sorted into correct and error trial types (associated ...
Decoding Complete Reach and Grasp Actions from Local Primary
Decoding Complete Reach and Grasp Actions from Local Primary

Concepts of Object- and Agent-Oriented Simulation
Concepts of Object- and Agent-Oriented Simulation

Classification with Incomplete Data Using Dirichlet Process Priors
Classification with Incomplete Data Using Dirichlet Process Priors

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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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