- Poster presentation
- Open Access
Spike-based reinforcement learning of navigation
© Vasilaki et al; licensee BioMed Central Ltd. 2008
- Published: 11 July 2008
- Reinforcement Learning
- Action Space
- Random Search
- Place Cell
- Maze Task
We have studied a spiking, reinforcement learning model derived from reward maximization [1, 2] where causal relations between pre-and postsynaptic activity set a synaptic eligibility trace [2, 3]. Neurons are modeled according to the "Integrate-and-Fire" model with escape noise. Synapses are binary and are modulated via the release probability. The synaptic release probability is updated when a global reward signal (such as dopamine) is received.
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