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ARTIFICIAL NEURAL NETWORKS TO INVESTIGATE
ARTIFICIAL NEURAL NETWORKS TO INVESTIGATE

... influential at characterizing the risk of occurrence of these types of chromosomal anomalies. Then, the PAPP-A and the b-hCG were removed from the in-puts in order to ascertain their contributory effects. The best results were obtained when using a multilayer neural structure having an input, an out ...
Differentiating Upper from Lower Motor Neuron Lesions
Differentiating Upper from Lower Motor Neuron Lesions

PowerPoint - University of Virginia
PowerPoint - University of Virginia

... – If we have three points and three unknowns we can solve – If we have more points we must use another technique ...
NIHMS263877-supplement-1
NIHMS263877-supplement-1

... In order to gain some intuition about the uniform distribution of orthogonal matrices V, it is useful to recognize that each orthogonal matrix corresponds to the rotation of a vector by a given angle. In a space of dimension M, the rotation is defined by specifying the values of M-1 angles, where ea ...
JavaParser: A Fine-Grain Concept Indexing Tool for Java Problems
JavaParser: A Fine-Grain Concept Indexing Tool for Java Problems



Repairing Incorrect Knowledge with Model Formulation and
Repairing Incorrect Knowledge with Model Formulation and

... resolve contradictions and to guide knowledge integration. The system’s knowledge is organized using the knowledgebased network of Friedman and Forbus [2010]. We simulate results from the cognitive science literature concerning the self-explanation effect in learning about the human circulatory syst ...
A Computer Simulation of Olfactory Cortex with Functional
A Computer Simulation of Olfactory Cortex with Functional

... the fiber systems are modifiable in an activity-dependent fashion (Fig. 2). The basic modification rule in each case is Hebb-like; i.e. change in synaptic strength is proportional to presynaptic activity multiplied by the offset of the postsynaptic membrane potential from a baseline potential. This ...
Optimal decision making theories - Bristol CS
Optimal decision making theories - Bristol CS

Intelligent Systems - Teaching-WIKI
Intelligent Systems - Teaching-WIKI

... "test set”, which must not be used during training. – The test set must represent the cases that the ANN should generalize to. A re-run with the test set provides an unbiased estimate of the generalization error, provided that the test set was chosen randomly. – The disadvantage of split-sample vali ...
14.10 Insight 775 Gilbert
14.10 Insight 775 Gilbert

... case of contrast discrimination. Adini et al.7 assume that perceptual learning is mediated by an increase in contrast sensitivity. This, in turn, results from stimulus-evoked modifications to recurrent connections in the local network in the primary visual cortex. The model assumes that contrast dis ...
Cell body, axon, dendrite, synapse
Cell body, axon, dendrite, synapse

One-class to multi-class model update using the class
One-class to multi-class model update using the class

module 6 - sandrablake
module 6 - sandrablake

Spike Train - CMU Statistics
Spike Train - CMU Statistics

ling411-16 - Rice University
ling411-16 - Rice University

... HAS HANDLE ...
Search and forward chaining
Search and forward chaining

Lexical Plasticity in Early Bilinguals Does Not Alter Phoneme
Lexical Plasticity in Early Bilinguals Does Not Alter Phoneme

... interconnected and each represents one or more phonemes. The weights of certain connections between pools are the parameters of these models, whose values will determine the behavior of each model. The two models differ in terms of which of all possible interpool connection weights are considered pa ...
Close - IJCAI
Close - IJCAI

... artifacts, where explanations are trees of if-then rules over artifact features. Upon misclassification, the system analyzes its explanations and creates censor rules to prevent future misclassification. Like their system, our model diagnoses inconsistencies within and across explanations in its an ...
Optimal Neural Spike Classification
Optimal Neural Spike Classification

... Since we intend to handle the overlap case, we have to use a set of features Xm which obeys the following. Given the features of two of the waveforms, then one can compute those of their overlap. A good such candidate is the set of the samples of the spike (or possibly also just part of the samples) ...
Chapter 3 Part 1 - Doral Academy Preparatory
Chapter 3 Part 1 - Doral Academy Preparatory

... (PSP) – Not all-or-none – Changes the probability of the postsynaptic neuron firing ...
+ p
+ p

... • Data was precious! Now it is overwhelming ... • Statistical data – clean, numerical, controlled ...
Model_Report_--_Schuler_Robert_-
Model_Report_--_Schuler_Robert_-

Modelling of essential fish habitat based on remote sensing
Modelling of essential fish habitat based on remote sensing

Supervised learning - TKK Automation Technology Laboratory
Supervised learning - TKK Automation Technology Laboratory

... • Data (matrices P and T) is in “superdata.mat” • Input data (P) is recorded from four successful runs through a certain zig-zag route (Red Bull Air Race etc) using a simulator. First four rows of P are the rudder angles, next four rows of P are the elevator angles of the same run. The first row of ...
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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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