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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 6 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 6 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
14 years 7 days 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...