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Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle.

Genetics, selection, evolution : GSE
BACKGROUND: Recently, artificial neural networks (ANN) have been proposed as promising machines for marker-based genomic predictions of complex traits in animal and plant breeding. ANN are universal approximators of complex functions, that can captur...

Stability analysis of neutral type neural networks with mixed time-varying delays using triple-integral and delay-partitioning methods.

ISA transactions
This paper investigates the asymptotical stability problem for a class of neutral type neural networks with mixed time-varying delays. The system not only has time-varying discrete delay, but also distributed delay, which has never been discussed in ...

Granger causality-based synaptic weights estimation for analyzing neuronal networks.

Journal of computational neuroscience
Granger causality (GC) analysis has emerged as a powerful analytical method for estimating the causal relationship among various types of neural activity data. However, two problems remain not very clear and further researches are needed: (1) The GC ...

Multistability for Delayed Neural Networks via Sequential Contracting.

IEEE transactions on neural networks and learning systems
In this paper, we explore a variety of new multistability scenarios in the general delayed neural network system. Geometric structure embedded in equations is exploited and incorporated into the analysis to elucidate the underlying dynamics. Criteria...

A rational model of function learning.

Psychonomic bulletin & review
Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We pr...

Stochastic mean-field formulation of the dynamics of diluted neural networks.

Physical review. E, Statistical, nonlinear, and soft matter physics
We consider pulse-coupled leaky integrate-and-fire neural networks with randomly distributed synaptic couplings. This random dilution induces fluctuations in the evolution of the macroscopic variables and deterministic chaos at the microscopic level....

L1-norm locally linear representation regularization multi-source adaptation learning.

Neural networks : the official journal of the International Neural Network Society
In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the larg...

Does surgeon subjective nerve sparing score predict recovery time of erectile function following robot-assisted radical prostatectomy?

The journal of sexual medicine
INTRODUCTION: During robot-assisted radical prostatectomy (RARP), the quality of nerve sparing (NS) was usually classified by laterality of NS (none, unilateral, and bilateral) or degree of NS (none, partial, and full). Recently, side-specific NS hav...

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

Journal of neuroscience methods
BACKGROUND: Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are prom...

Prediction of Henry's Law Constants via group-specific quantitative structure property relationships.

Chemosphere
Henry's Law Constants (HLCs) for several hundred organic compounds in water at 25 °C were predicted by Quantitative Structure Property Relationship (QSPR) models, with the division of organic compounds into specific classes to yield more accurate mod...