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» Modeling spiking neural networks
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NIPS
2004
13 years 9 months ago
Bayesian inference in spiking neurons
We propose a new interpretation of spiking neurons as Bayesian integrators accumulating evidence over time about events in the external world or the body, and communicating to oth...
Sophie Deneve
SAB
2010
Springer
150views Optimization» more  SAB 2010»
13 years 5 months ago
Internal Models in the Cerebellum: A Coupling Scheme for Online and Offline Learning in Procedural Tasks
The cerebellum plays a major role in motor control. It is thought to mediate the acquisition of forward and inverse internal models of the bodyenvironment interaction [1]. In this ...
Jean-Baptiste Passot, Niceto Luque, Angelo Arleo
GECCO
2007
Springer
182views Optimization» more  GECCO 2007»
13 years 11 months ago
Stochastic training of a biologically plausible spino-neuromuscular system model
A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control systems tha...
Stanley Phillips Gotshall, Terence Soule
NIPS
2004
13 years 9 months ago
Maximising Sensitivity in a Spiking Network
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Anthony J. Bell, Lucas C. Parra
BC
2000
77views more  BC 2000»
13 years 7 months ago
Generic origins of irregular spiking in neocortical networks
We identify generic sources of complex and irregular spiking in biological neural networks. For the network description, we operate on a mathematically exact mesoscopic approach. S...
Ruedi Stoop, L. A. Bunimovich, Willi-Hans Steeb