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» Neural Dynamics with Stochasticity
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JCNS
2000
165views more  JCNS 2000»
14 years 9 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 2 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 4 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 9 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
14 years 11 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