Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule

By Dorit Baras, R. Meir. 2007

74 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Frontiers in Synaptic Neuroscience Synaptic Neuroscience (unknown): “…to be achieved (for experimental evidence, see Section “Experimental Evidence for Involvement of Neuromodulators in STDP”; for modeling approaches see: Baras and Meir, 2007; Florian, 2007; Izhikevich, 2007; Legenstein et al., 2008; Vasilaki et al., 2009; Fremaux et al., 2010; Potjans et al., 2010).”
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules (2016, background): “…the action taken at the junction leads to a reward or not (Arleo and Gerstner, 2000; Foster et al., 2000; Xie and Seung, 2004; Pfister et al., 2006; Baras and Meir, 2007; Florian, 2007; Di Castro et al., 2009; Sheynikhovich et al., 2009; Urbanczik and Senn, 2009; Vasilaki et al., 2009; Frémaux et…”
Reinforcement Learning on Slow Features of High-Dimensional Input Streams (2010, background): “Several theoretical studies have investigated biologically plausible reward-based learning rules [46–55].”
Spatio-Temporal Credit Assignment in Neuronal Population Learning (2011, unknown): “As has been widely noted [5–11], one can connect the STDP-findings with reinforcement learning by assuming that the transcription of the second eligibility trace E 2 into the synaptic change D w is modulated by neurotransmitters like dopamine which provide feedback about external reward (Fig.”
Introduction to spiking neural networks: Information processing, learning and applications. (2011, methodology): “Based on these observations several models for reinforcement learning in spiking neural networks have been proposed (Florian 2005, Baras and Meir 2007, Farries and Fairhall 2007, Florian 2007, Izhikevich 2007, Vasilaki et al. 2009).”
Computational models of reinforcement learning: the role of dopamine as a reward signal (2010, background): “Other RL models demonstrated how neural networks could be trained to fire toward an optimum (Baras and Meir 2007; Potjans et al. 2009).”
Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity (2010, methodology): “…rules: the R-max rule, which was theoretically designed to increase the amount of reward during learning (Xie and Seung, 2004; Pfister et al., 2006; Baras and Meir, 2007; Florian, 2007); and the R-STDP rule, which is a simple STDP rule with amplitude and sign modulated by positive or negative…”