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Rare neural correlations implement robotic conditioning with delayed rewards and disturbances

By Andrea Soltoggio, Andre Lemme, Felix Reinhart and Jochen Steil


This is an open access article published under CC-BY licence. © the authors.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

Topics: Classical conditioning, Distal reward, Instrumental conditioning, Neuromodulation, Robotics
Publisher: Frontiers Research Foundation
Year: 2013
DOI identifier: 10.3389/fnbot.2013.00006
OAI identifier:

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