ArticleFrontiers in neurorobotics2013
Rare neural correlations implement robotic conditioning with delayed rewards and disturbances.
Article in Frontiers in neurorobotics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Editorial: Neural plasticity for rich and uncertain robotic information streams.Frontiers in neurorobotics · 2015Article
- Self-organizing neural integration of pose-motion features for human action recognition.Frontiers in neurorobotics · 2015Article
- Learning touch preferences with a tactile robot using dopamine modulated STDP in a model of insular cortex.Frontiers in neurorobotics · 2015Article
- Reward-Modulated Hebbian Plasticity as Leverage for Partially Embodied Control in Compliant Robotics.Frontiers in neurorobotics · 2015Article
- RM-SORN: a reward-modulated self-organizing recurrent neural network.Frontiers in computational neuroscience · 2015Article
- Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot's controller.Frontiers in neurorobotics · 2014Article
- Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control.Frontiers in neural circuits · 2014Article
- Value and reward based learning in neurorobots.Frontiers in neurorobotics · 2013Article
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4 authors.
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Abstract
Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards. Such an uncertainty is handled by biological neural networks, but represents a challenge for computational models, suggesting the lack of a satisfactory theory for robotic neural conditioning. The present study demonstrates the use of rare neural correlations in making correct associations between rewards and previous cues or actions. Rare correlations are functional in selecting sparse synapses to be eligible for later weight updates if a reward occurs. The repetition of this process singles out the associating and reward-triggering pathways, and thereby copes with distal rewards. The neural network displays macro-level classical and operant conditioning, which is demonstrated in an interactive real-life human-robot interaction. The proposed mechanism models realistic conditioning in humans and animals and implements similar behaviors in neuro-robotic platforms.
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