Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules
By Wulfram Gerstner, Marco P Lehmann, Vasiliki Liakoni, Dane S. Corneil, Johanni Brea. Front. Neural Circuits, 2018
347 citations recorded by the source corpus.
Most elementary behaviors such as moving the arm to grasp an object or walking into the next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal action potentials occur on the time scale of a few milliseconds. Learning rules of the brain must therefore bridge the gap between these two different time scales. Modern theories of synaptic plasticity have postulated that the co-activation of pre- and postsynaptic neurons sets a flag at the synapse, called an eligibility trace, that leads to a weight change only if an additional factor is present while the flag is set. This third factor, signaling reward, punishment, surprise, or novelty, could be implemented by the phasic activity of neuromodulators or specific neuronal inputs signaling special events. While the theoretical framework has been developed over the last decades, experimental evidence in support of eligibility traces on the time scale of seconds has been collected only during the last few years. Here we review, in the context of three-factor rules of synaptic plasticity, four key experiments that support the role of synaptic eligibility traces in combination with a third factor as a biological implementation of neoHebbian three-factor learning rules.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/d367d4de86dce92d30fba888ad190957f9ae14d8
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Citations in the corpus
- Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity (2018, background): “Such mechanisms have been modelled in computational neuroscience studies, e.g. (Izhikevich, 2007; Hoerzer et al., 2014; Fiete et al., 2007; Soltoggio & Steil, 2013; Miconi, 2017) (see Gerstner et al. 2018 for a recent review of this concept).”
- A solution to the learning dilemma for recurrent networks of spiking neurons (2019, background): “Resulting synaptic plasticity rules (see Methods) look very similar to previously proposed plasticity rules (Gerstner et al., 2018).”
- Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits (2020, background): “Importantly, M ( t ) in our model gates plasticity without changing its sign, contrary to some models on the role of neuromodulation in plasticity [36].”