Definition
Reinforcement learning occurs when an agent optimizes its actions, based on past experience, in order to maximize the reward generated by its actions. Spiking neural networks are neural models where neurons transmit information among each other by firing action potentials, or spikes, as real neurons do. Reinforcement learning in spiking neural networks refers to how spiking neurons modify their parameters in order to maximize a reward that depends on their activity.
Theoretical Background
Humans and animals learn through coordinated changes in the properties of their neural systems. In neural models, this is simulated by changes of the parameters of these models, such as synaptic efficacies. The study of learning in neural networks focuses on the rules that govern these changes such that they allow the network to process and memorize information. Learning rules for neural models are studied analytically and in computer...
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Florian, R.V. (2012). Reinforcement Learning in Spiking Neural Networks. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_1713
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