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» Neural Dynamics with Stochasticity
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JCNS
2000
165views more  JCNS 2000»
14 years 11 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
ICANN
2001
Springer
15 years 4 months ago
Fast Curvature Matrix-Vector Products
The method of conjugate gradients provides a very effective way to optimize large, deterministic systems by gradient descent. In its standard form, however, it is not amenable to ...
Nicol N. Schraudolph
CIE
2009
Springer
15 years 6 months ago
Stochastic Programs and Hybrid Automata for (Biological) Modeling
We present a technique to associate to stochastic programs written in stochastic Concurrent Constraint Programming a semantics in terms of a lattice of hybrid automata. The aim of ...
Luca Bortolussi, Alberto Policriti
IJON
2007
91views more  IJON 2007»
14 years 11 months ago
Dynamics of parameters of neurophysiological models from phenomenological EEG modeling
We investigate a recently proposed method for the analysis of oscillatory patterns in EEG data, with respect to its capacity of further quantifying processes on slower (< 1 Hz)...
E. Olbrich, Thomas Wennekers
AIPS
2006
15 years 1 months ago
Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of ...
Malcolm J. A. Strens