Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules¶
Frémaux, & Gerstner. (2016). Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules.
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Domain-specific¶
- Synaptic Plasticity
- What genuinely recurs across substrates is the Hebbian-learning rule-shape — modifiable couplings between units update in response to their joint activity, often gated by a third signal — and that pattern reappears as legitimate co-instances, not metaphors: weight-update rules in artificial neural networks (contrastive Hebbian learning, BCM-like dynamics), neuromodulator-inspired eligibility traces and three-factor learning rules (Williams' REINFORCE)
This sourceFremaux and Gerstner's review of neuromodulated STDP and three-factor learning rules, in which a pre/post eligibility trace is gated by a neuromodulatory signal.
Supported in partVerified against the work's full text
“a synaptic plasticity rule that is influenced in addition by a neuromodulator will be called a "three-factor rule"”
- What genuinely recurs across substrates is the Hebbian-learning rule-shape — modifiable couplings between units update in response to their joint activity, often gated by a third signal — and that pattern reappears as legitimate co-instances, not metaphors: weight-update rules in artificial neural networks (contrastive Hebbian learning, BCM-like dynamics), neuromodulator-inspired eligibility traces and three-factor learning rules (Williams' REINFORCE)
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