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JMLR
2006
389views more  JMLR 2006»
13 years 8 months ago
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
This paper introduces a learning method for two-layer feedforward neural networks based on sensitivity analysis, which uses a linear training algorithm for each of the two layers....
Enrique Castillo, Bertha Guijarro-Berdiñas,...
TCAD
2010
150views more  TCAD 2010»
13 years 3 months ago
Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks
Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process paramete...
Janakiraman Viraraghavan, Bharadwaj Amrutur, V. Vi...
SIAMCO
2010
93views more  SIAMCO 2010»
13 years 7 months ago
Small Gain Theorems for Large Scale Systems and Construction of ISS Lyapunov Functions
We consider a network consisting of n interconnected nonlinear subsystems. For each subsystem an ISS Lyapunov function is given that treats the other subsystems as independent inpu...
Sergey Dashkovskiy, Björn Sebastian Rüff...
JCNS
2000
165views more  JCNS 2000»
13 years 8 months ago
A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Analysis and an Application to Orientati
We explore a computationally efficient method of simulating realistic networks of neurons introduced by Knight, Manin, and Sirovich (1996) in which integrate-and-fire neurons are ...
Duane Q. Nykamp, Daniel Tranchina
SIAMADS
2010
121views more  SIAMADS 2010»
13 years 3 months ago
Binocular Rivalry in a Competitive Neural Network with Synaptic Depression
We study binocular rivalry in a competitive neural network with synaptic depression. In particular, we consider two coupled hypercolums within primary visual cortex (V1), represent...
Zachary P. Kilpatrick, Paul C. Bressloff