NDLI: A reinforcement learning algorithm for spiking neural networks

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A reinforcement learning algorithm for spiking neural networks

Content Provider	IEEE Xplore Digital Library
Author	Florian, R.V.
Copyright Year	2005
Description	Author affiliation: Center for Cognitive & Neural Studies (Coneural), Cluj-Napoca, Romania (Florian, R.V.)
Abstract	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 be also applied to generic spiking neural networks. Learning is achieved by synaptic changes that depend on the firing of pre- and postsynaptic neurons, and that are modulated with a global reinforcement signal. The efficacy of the algorithm is verified in a biologically-inspired experiment, featuring a simulated worm that searches for food. Our model recovers a form of neural plasticity experimentally observed in animals, combining spike-timing-dependent synaptic changes of one sign with non-associative synaptic changes of the opposite sign determined by presynaptic spikes. The model also predicts that the time constant of spike-timing-dependent synaptic changes is equal to the membrane time constant of the neuron, in agreement with experimental observations in the brain. This study also led to the discovery of a biologically-plausible reinforcement learning mechanism that works by modulating spike-timing-dependent plasticity (STDP) with a global reward signal.
File Size	213733
File Format	PDF
ISBN	0769524532
DOI	10.1109/SYNASC.2005.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-25
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Animals Biological system modeling Neural networks Neurons Stochastic processes Predictive models Brain modeling Biomembranes Biological neural networks
Content Type	Text
Resource Type	Article

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