Code-specific policy gradient rules for spiking neurons

NeurIPS 2009 Henning SprekelerGuillaume HennequinWulfram Gerstner

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i.e., depending on which neural code is in effect... (read more)

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