An Embodied Model of Learning, Plasticity, and Reward

By William H. Alexander, Olaf Sporns. 2002

33 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “Such a modulation of signals, or gated learning (Abbott, 1990), allows for the regulation and learning of behaviors as shown in numerous computational models (Baxter et al., 1999; Suri et al., 2001; Birmingham, 2001; Alexander and Sporns, 2002; Doya, 2002; Fujii et al., 2002; Suri, 2002; Ziemke and ”
Short-term plasticity as cause–effect hypothesis testing in distal reward learning (2014, background): “The utility of modulatory dynamics in models of reward learning and behavior is also validated by closed-loop robotic neural controllers (Ziemke and Thieme, 2002; Sporns and Alexander, 2002; Alexander and Sporns, 2002; Sporns and Alexander, 2003; Soltoggio et al, 2008; Cox and Krichmar, 2009).”
Neural Plasticity and Minimal Topologies for Reward-Based Learning (2008, background): “Even with the current limitation of evolutionary algorithms and scalability problems, experiments in simple settings have provided remarkable examples of learning networks that implement homosynaptic and heterosynaptic plasticity (or neuromodulation) [6, 12], resulting in various advantages of…”
as a Robot Controller Neuromodulation as a Robot Controller A Brain-Inspired Strategy for Controlling Autonomous Robots (2009, background): “The phasic response of dopamine has been modeled to examine reward anticipation behavior in a robot [21].”
Neuromodulation as a robot controller (2009, background): “The phasic response of dopamine has been modeled to examine reward anticipation behavior in a robot [21].”
Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances (2013, background): “The increasing evidence of the important role of neuromodulation in reward-driven learning led to the formulation of models of modulated plasticity with rate-based neurons (e.g., Montague et al., 1996; Alexander and Sporns, 2002; Sporns and Alexander, 2002; Ziemke and Thieme, 2002; Soltoggio et al.,”
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World (2008, background): “Some of these reinforcement learning robot controllers are neurally inspired by reward expectation signals found in the dopaminergic system of the brain (Alexander & Sporns, 2002; Arleo, Smeraldi, & Gerstner, 2004; Krichmar & Edelman, 2002; Sporns & Alexander, 2002).”
Neuromodulation in a learning robot: interactions between neural plasticity and behavior (2003, unknown): “This paper represents the continuation of an earlier study [3] aiming at characterizing the interplay between “internal” neural dynamics and plasticity and “external” factors such as motor variables and environmental composition.”
Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, background): “Although most studies seem to indicate the suitability of weight update for adaptive behaviour (Montague et al., 1995; Suri and Schultz, 1999; Floreano and Urzelai, 2001b; Urzelai and Floreano, 2001; Alexander and Sporns, 2002; Soltoggio et al., 2007), there are examples where adaptive behaviour is ”