A Model of Neuronal Specialization Using Hebbian Policy-Gradient with "Slow" Noise
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Phase precession and recession with STDP and Anti-STDP
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Spiking neural controllers for pushing objects around
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
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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 ef- ficacy 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-dependentsynaptic changes of one sign with nonassociative 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.