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» Simplifying mixture models through function approximation
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IJCNN
2008
IEEE
14 years 2 months ago
Biologically realizable reward-modulated hebbian training for spiking neural networks
— Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximat...
Silvia Ferrari, Bhavesh Mehta, Gianluca Di Muro, A...
ICIP
1995
IEEE
13 years 11 months ago
Error bound for multi-stage synthesis of narrow bandwidth Gabor filters
This paper develops an error bound for narrow bandwidth Gabor filters synthesized using multiple stages. It is shown that the error introduced by approximating narrow bandwidth Ga...
R. Neil Braithwaite, Bir Bhanu
BMCBI
2008
77views more  BMCBI 2008»
13 years 7 months ago
Stochastic models for the in silico simulation of synaptic processes
Background: Research in life sciences is benefiting from a large availability of formal description techniques and analysis methodologies. These allow both the phenomena investiga...
Andrea Bracciali, Marcello Brunelli, Enrico Catald...
CEC
2011
IEEE
12 years 7 months ago
Stochastic Natural Gradient Descent by estimation of empirical covariances
—Stochastic relaxation aims at finding the minimum of a fitness function by identifying a proper sequence of distributions, in a given model, that minimize the expected value o...
Luigi Malagò, Matteo Matteucci, Giovanni Pi...
VLSID
2006
IEEE
192views VLSI» more  VLSID 2006»
14 years 1 months ago
Beyond RTL: Advanced Digital System Design
This tutorial focuses on advanced techniques to cope with the complexity of designing modern digital chips which are complete systems often containing multiple processors, complex...
Shiv Tasker, Rishiyur S. Nikhil