A general error-based spike-timing dependent learning rule for the Neural Engineering Framework
نویسنده
چکیده
Previous attempts at integrating spike-timing dependent plasticity rules in the NEF have met with little success. This project proposes a spike-timing dependent plasticity rule that uses local information to learn transformations between populations of neurons. The rule is implemented and tested on a simple one-dimensional communication channel, and is compared to a similar rate-based learning rule.
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