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GECCO
2010
Springer
152views Optimization» more  GECCO 2010»
15 years 11 months ago
Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia
Neuro-evolution and computational neuroscience are two scientific domains that produce surprisingly different artificial neural networks. Inspired by the “toolbox” used by ...
Jean-Baptiste Mouret, Stéphane Doncieux, Be...
IWANN
2005
Springer
15 years 12 months ago
Characterizing Self-developing Biological Neural Networks: A First Step Towards Their Application to Computing Systems
Carbon nanotubes are often seen as the only alternative technology to silicon transistors. While they are the most likely short-term alternative, other longer-term alternatives sho...
Hugues Berry, Olivier Temam
IWINAC
2005
Springer
15 years 12 months ago
Estimation of Fuel Moisture Content Using Neural Networks
Fuel moisture content (FMC) is one of the variables that drive fire danger. Artificial Neural Networks (ANN) were tested to estimate FMC by calculating the two variables implicat...
David Riaño, S. L. Ustin, L. Usero, Miguel ...
JCNS
2010
126views more  JCNS 2010»
15 years 4 months ago
Calibration of the head direction network: a role for symmetric angular head velocity cells
Abstract Continuous attractor networks require calibration. Computational models of the head direction (HD) system of the rat usually assume that the connections that maintain HD n...
Peter Stratton, Gordon Wyeth, Janet Wiles
CORR
2010
Springer
163views Education» more  CORR 2010»
15 years 4 months ago
Faster Rates for training Max-Margin Markov Networks
Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (M3 N) is an effective approach. All state-of-...
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan