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Neural Networks (Computer)
Research News and Information
Definition of 'Neural Networks (Computer)'A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. |
Saturday, November 21, 2009
Embedding multiple trajectories in simulated recurrent neural networks in a self-organizing manner.
19 Oct 2009 
Complex neural dynamics produced by the recurrent architecture of neocortical circuits is critical to the cortex's computational power. However, the synaptic learning rules underlying the creation of stable propagation and reproducible neural ... Read more...
Physical binding pocket induction for affinity prediction.
6 Oct 2009 Computational methods for predicting ligand affinity where no protein structure is known generally take the form of regression analysis based on molecular features that have only a tangential relationship to a protein/ligand binding event. Such ... Read more...
An integral upper bound for neural network approximation.
29 Sep 2009 Complexity of one-hidden-layer networks is studied using tools from nonlinear approximation and integration theory. For functions with suitable integral representations in the form of networks with infinitely many hidden units, upper bounds are ... Read more...
Latest indexed articles for 'Neural Networks (Computer)'
These are the very latest articles for this heading:
- Embedding multiple trajectories in simulated recurrent neural networks in a self-organizing manner. 19 Oct 2009
- Physical binding pocket induction for affinity prediction. 6 Oct 2009
- An integral upper bound for neural network approximation. 29 Sep 2009
- Experience-induced neural circuits that achieve high capacity. 29 Sep 2009
- Correlation between eigenvalue spectra and dynamics of neural networks. 29 Sep 2009
- Distance learning in discriminative vector quantization. 29 Sep 2009
- Receptive field self-organization in a model of the fine structure in v1 cortical columns. 29 Sep 2009
- Artificial neural networks in positron emission tomography-computed tomography: is it time yet?
29 Sep 2009 - A computational approach for the identification of small GTPases based on preprocessed amino acid sequences. 29 Sep 2009
- Prediction by modeling that epilepsy may be caused by very small functional changes in ion channels. 29 Sep 2009
- Artificial neural networks for prediction of response to chemoradiation in HT29 xenografts. 29 Sep 2009
- Membrane-induced conformational change of alpha1-acid glycoprotein characterized by vacuum-ultraviolet circular dichroism spectroscopy. 27 Sep 2009
- Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models. 12 Sep 2009
- An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images. 8 Sep 2009
- [Artificial neural networks make difficult medical decisions easier. Examples from pancreatology] 30 Aug 2009
- Modeling the categorical perception of speech sounds: a step toward biological plausibility. 30 Aug 2009
- Contrast effects in priming paradigms: Implications for theory and research on implicit attitudes. 30 Aug 2009
- Belief propagation in networks of spiking neurons. 30 Aug 2009
- A bound on modeling error in observable operator models and an associated learning algorithm. 30 Aug 2009
- Sequential effects in two-choice reaction time tasks: decomposition and synthesis of mechanisms. 30 Aug 2009
See a longer list of these articles.
Technical information about 'Neural Networks (Computer)'
Definition: A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
Descriptor UI: D016571
Alternative terms: Neural Networks (Computer); Network, Neural (Computer); Networks, Neural (Computer); Neural Network (Computer); Models, Neural Network; Model, Neural Network; Network Model, Neural; Network Models, Neural; Neural Network Model; Perceptrons; Perceptron; Connectionist Models; Connectionist Model; Model, Connectionist; Models, Connectionist; Neural Network Models;
Tree Number: G17.485; L01.224.065.605; L01.725.500;
History Note: 92
Technical Notes: no qualif; do not confuse with NEURAL NETWORKS (ANATOMIC) see NERVE NET