Reinforcement learning with modulated spike timing dependent synaptic plasticity.

By Michael A. Farries, Adrienne L. Fairhall. 2007

147 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Short-term plasticity as cause–effect hypothesis testing in distal reward learning (2014, background): “The maximization of reward functions was also implemented in a variety of neural network models (Lin, 1993; Pennartz, 1997; Schultz et al, 1997; Bosman et al, 2004; Xie and Seung, 2004; Florian, 2007; Farries and Fairhall, 2007; Baras and Meir, 2007; Legenstein et al, 2010; Frémaux et al, 2010; Frie”
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).”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, background): “, 2010), making neuromodulation particularly suitable for modeling and implementing learning processes (Sporns and Alexander, 2002; Doya, 2002; Doya and Uchibe, 2005; Soula et al., 2005; Farries and Fairhall, 2007; Krichmar, 2008; Cox and Krichmar, 2009).”
Perceptual learning, roving and the unsupervised bias. (2012, unknown): “…R av augments Hebbian learning by a reward prediction error and, hence, relies on reward prediction R rather than on activity prediction post j (Farries & Fairhall , 2007; Frémaux, Sprekeler, & Gerstner, 2010; Izhikevich, 2007; Legenstein, Pecevski, & Maass, 2008): Dw ij ¼ pre i Â post j Â ðR…”
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules (2016, methodology): “(B) R-STDP (Farries and Fairhall, 2007; Florian, 2007; Izhikevich, 2007; Legenstein et al., 2008; Vasilaki et al., 2009; Frémaux et al., 2010).”
Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances (2013, background): “A reward is generally conveyed by means of a modulatory signal (Montague et al., 1996; Farries and Fairhall, 2007; Florian,2007; Porr and Wörgötter,2007; Soltoggio et al., 2008; Pfeiffer et al., 2010).”
Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, background): “The problem is named the credit assignment problem or distal reward problem (Izhikevich, 2007a; Nitz et al., 2007; Farries and Fairhall, 2007).”
Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting (2010, unknown): “Thealgo-rithm,knownasreward-modulatedspike-timing-dependent-plasticity (RM-STDP),hasbeenintroducedinFlorian(2007)andIzhikevich(2007) (asimilarmethodhasalsobeendescribedinFarries&Fairhall,2007).”
The spike timing dependence of plasticity (2012, background): “STDP can also mediate temporal difference learning (Rao and Sejnowski, 2003) and reinforcement learning (Farries and Fairhall, 2007; Izhikevich, 2007; Cassenaer and Laurent, 2012) and can tune neurons for temporal features of input (Masquelier et al., 2009).”
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].”
Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity (2010, methodology): “, 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 reward (Farries and Fairhall, 2007; Izhikevich, 2007; Legenstein et al., 2008).”