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A conceptual model for game based intelligent tutoring
A conceptual model for game based intelligent tutoring

... acknowledge the history of basic computer aided learning (CAL) systems from which ITS have evolved. Basic CAL systems often contain simple multiple-choice tests or quizzes of de-contextualised knowledge and present students with simple questions, for domains such as maths or economics, and scripted ...
2 COMPUTATIONAL MODELLING IN ARTIFICIAL INTELLIGENCE
2 COMPUTATIONAL MODELLING IN ARTIFICIAL INTELLIGENCE

The neural basis of the speed–accuracy tradeoff - Eric
The neural basis of the speed–accuracy tradeoff - Eric

... have researchers begun to study the neural basis of SAT, using experimental methods and neurocomputational models. This review focuses on three key aspects of SAT that this recent work has helped bring to the fore: (i) what processing stage does SAT affect? (ii) does speed emphasis cause an increase ...
Neurophysiological investigation of the basis of the fMRI signal
Neurophysiological investigation of the basis of the fMRI signal

... standard deviation (s.d.) of the activity in the pre-stimulus period, and it therefore represents the signal-to-noise ratio of the response at that frequency. The maximum increase in power over all sessions (10 monkeys, N = 619 experiments) was found at 72.96 Hz (1 s.d. = 21.04 Hz), in other words w ...
Motivations behind modeling emotional agents: Whose
Motivations behind modeling emotional agents: Whose

Specific and Nonspecific Plasticity of the Primary
Specific and Nonspecific Plasticity of the Primary

18
18

... How does the human brain make sense of the 3D world while its visual input, the retinal images, are only two-dimensional? There are multiple depth-cues exploited by the brain to create a 3D model of the world. Despite the importance of this subject both for scientists and engineers, the underlying c ...
Rule Insertion and Rule Extraction from Evolving Fuzzy
Rule Insertion and Rule Extraction from Evolving Fuzzy

... of the fuzzy output values. At any time (phase) of the evolving (learning) process, fuzzy, or exact rules can be inserted and extracted. Insertion of fuzzy rules is achieved through setting a new rule node for each new rule, such as the connection weights W1 and W2 of the rule node represent the fuz ...
Copy of the full paper
Copy of the full paper

... In the classic work on the gill and siphon withdrawal reflex in Aplysia, changes in both neuronal excitability and synaptic strength are produced by serotonin and experience4. (2) Neuromodulation is the rule, not the exception. Individual neurons and individual synapses are often modulated by severa ...
Artificial Intelligence Winter 2004
Artificial Intelligence Winter 2004

Proceedings of 2014 BMI the Third International Conference on
Proceedings of 2014 BMI the Third International Conference on

... actually learned in perceptual learning? I will present evidence that perceptual learning may be a form of concept learning, in that the brain may learn a highly abstract “concept” of orientation/direction. On the other hand, why high-level perceptual learning shows specificity in the first place? I ...
A neural implementation of Bayesian inference based on predictive
A neural implementation of Bayesian inference based on predictive

A"computational"approach"towards"the"ontogeny"of" mirror"neurons
A"computational"approach"towards"the"ontogeny"of" mirror"neurons

... Currently, this threshold is imposed as a fixed constant. An extension of this work would be to model homeostatic plasticity by dynamically determining the threshold value based on the overall network activity. Second, mirror neuron behavior can only be imposed if the bounds for the excitatory neuro ...
ICAISC 2004 Preliminary Program
ICAISC 2004 Preliminary Program

... The importance of the papers is not related to the form of the presentation. Overhead and computer projectors will be available on all oral sessions. Posters should be prepared with the use of big fonts and figures and should not exceed 1m x 1,2m area (A0 or A1 paper size, portrait orientation). Ple ...
Action, time and the basal ganglia - Philosophical Transactions of
Action, time and the basal ganglia - Philosophical Transactions of

... In other words, it is necessary to use additional lower level control systems to exert effects on the environment. As the controlled variable must be sensed, each level of the hierarchy is defined by its sensory input (figure 2). Movement velocity control is largely independent of the distal senses ...
Artificial neural networks and their application in biological and
Artificial neural networks and their application in biological and

Catastrophic Forgetting in Connectionist Networks: Causes
Catastrophic Forgetting in Connectionist Networks: Causes

... Catastrophic interference versus gradual interference First, we need to make clear the distinction between what McCloskey & Cohen1 call “the mere occurrence of interference” and “catastrophic interference.” Barnes and Underwood8 conducted a series of experiments that measured the extent of retroact ...
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astic Strategy act ead

... overhead, which will not be true in general. While a stochastic contractor is deliberating on an optimal payment, another contractor may be able to contract and finish more tasks. In the following, we examine the impact of deliberation overhead on the performance of the stochastic strategy. To compa ...
Synapses and Neurotransmitters
Synapses and Neurotransmitters

... Summation It needs to be understood that in many cases, the neurotransmitters released from a single neuron are not enough to reach the threshold level in the postsynaptic neuron which means an action potential will NOT occur. The effect produced by the accumulation of neurotransmitters released f ...
Advanced Applications of Neural Networks and Artificial Intelligence
Advanced Applications of Neural Networks and Artificial Intelligence

Emergence of Mirror Neurons in a Model of Gaze Following
Emergence of Mirror Neurons in a Model of Gaze Following

Fuzzy Logic and Neural Nets
Fuzzy Logic and Neural Nets

The speed of learning instructed stimulus
The speed of learning instructed stimulus

Artificial Intelligence
Artificial Intelligence

LiuPoster - Department of Mathematics
LiuPoster - Department of Mathematics

... synchrony detector. Then, I applied the integrate-and-fire model to phase delayed inhibition in the rat barrel cortex and proposed a mechanism for sensory adaptation: that sensory adaptation is a result of decreased synaptic strengths. ...
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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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