Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Neural Computation
SIAM Journal on Control and Optimization
Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning
Neural Computation
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Infinite-horizon policy-gradient estimation
Journal of Artificial Intelligence Research
A spiking neural network model of an actor-critic learning agent
Neural Computation
A Model of Neuronal Specialization Using Hebbian Policy-Gradient with "Slow" Noise
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A Convergent Online Single Time Scale Actor Critic Algorithm
The Journal of Machine Learning Research
Compositionality of arm movements can be realized by propagating synchrony
Journal of Computational Neuroscience
Observational learning based on models of overlapping pathways
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
A reinforcement learning framework for spiking networks with dynamic synapses
Computational Intelligence and Neuroscience
Does high firing irregularity enhance learning?
Neural Computation
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Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is influenced by an environmental signal, termed a reward, that directs the changes in appropriate directions. We apply a recently introduced policy learning algorithm from machine learning to networks of spiking neurons and derive a spike-time-dependent plasticity rule that ensures convergence to a local optimum of the expected average reward. The approach is applicable to a broad class of neuronal models, including the Hodgkin-Huxley model. We demonstrate the effectiveness of the derived rule in several toy problems. Finally, through statistical analysis, we show that the synaptic plasticity rule established is closely related to the widely used BCM rule, for which good biological evidence exists.