Neural Comput - Solving the distal reward problem with rare correlations.

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Resumo

In the course of trial-and-error learning, the results of actions, manifested as rewards or punishments, occur often seconds after the actions that caused them. How can a reward be associated with an earlier action when the neural activity that caused that action is no longer present in the network? This problem is referred to as the distal reward problem. A recent computational study proposes a solution using modulated plasticity with spiking neurons and argues that precise firing patterns in the millisecond range are essential for such a solution. In contrast, the study reported in this letter shows that it is the rarity of correlating neural activity, and not the spike timing, that allows the network to solve the distal reward problem. In this study, rare correlations are detected in a standard rate-based computational model by means of a threshold-augmented Hebbian rule. The novel modulated plasticity rule allows a randomly connected network to learn in classical and instrumental conditioning scenarios with delayed rewards. The rarity of correlations is shown to be a pivotal factor in the learning and in handling various delays of the reward. This study additionally suggests the hypothesis that short-term synaptic plasticity may implement eligibility traces and thereby serve as a selection mechanism in promoting candidate synapses for long-term storage.

Resumo Limpo

cours trialanderror learn result action manifest reward punish occur often second action caus can reward associ earlier action neural activ caus action longer present network problem refer distal reward problem recent comput studi propos solut use modul plastic spike neuron argu precis fire pattern millisecond rang essenti solut contrast studi report letter show rariti correl neural activ spike time allow network solv distal reward problem studi rare correl detect standard ratebas comput model mean thresholdaug hebbian rule novel modul plastic rule allow random connect network learn classic instrument condit scenario delay reward rariti correl shown pivot factor learn handl various delay reward studi addit suggest hypothesi shortterm synapt plastic may implement elig trace therebi serv select mechan promot candid synaps longterm storag

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