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SYNASC
2005
IEEE
97views Algorithms» more  SYNASC 2005»
13 years 11 months ago
A Reinforcement Learning Algorithm for Spiking Neural Networks
The paper presents a new reinforcement learning mechanism for spiking neural networks. The algorithm is derived for networks of stochastic integrate-and-fire neurons, but it can ...
Razvan V. Florian
IJON
2007
76views more  IJON 2007»
13 years 5 months ago
Modeling L-LTP based on changes in concentration of pCREB transcription factor
We simulate the induction and maintenance of late long-term potentiation (L-LTP) in the hippocampal dentate gyrus by means of a new synaptic plasticity rule that is the result of ...
Lubica Benuskova, Nikola Kasabov
BC
2002
97views more  BC 2002»
13 years 5 months ago
A spatial stochastic neuronal model with Ornstein-Uhlenbeck input current
We consider a spatial neuron model in which the membrane potential satisfies a linear cable equation with an input current which is a dynamical random process of the Ornstein
Henry C. Tuckwell, Frederic Y. M. Wan, Jean-Pierre...
GECCO
2005
Springer
149views Optimization» more  GECCO 2005»
13 years 11 months ago
There's more to a model than code: understanding and formalizing in silico modeling experience
Mapping biology into computation has both a domain specific aspect – biological theory – and a methodological aspect – model development. Computational modelers have implici...
Janet Wiles, Nicholas Geard, James Watson, Kai Wil...
IPPS
2006
IEEE
13 years 11 months ago
Performance analysis of stochastic process algebra models using stochastic simulation
We present a translation of a generic stochastic process algebra model into a form suitable for stochastic simulation. By systematically generating rate equations from a process d...
Jeremy T. Bradley, Stephen T. Gilmore, Nigel Thoma...