2017 International Joint Conference on Neural Networks (IJCNN) 2017
DOI: 10.1109/ijcnn.2017.7966306
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A biologically inspired neuronal model of reward prediction error computation

Abstract: The neurocomputational model described here proposes that two dimensions involved in computation of reward prediction errors i.e magnitude and time could be computed separately and later combined unlike traditional reinforcement learning models. The model is built on biological evidences and is able to reproduce various aspects of classical conditioning, namely, the progressive cancellation of the predicted reward, the predictive firing from conditioned stimuli, and delineation of early rewards by showing firi… Show more

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Cited by 2 publications
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References 29 publications
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“…On the other hand, 569 the ventral striatum was found to represent a unified quantity as a combination of 570 subjective value and internal motivation using different kind of neurons [55]. Several 571 computational accounts have explained possible implementations of such 572 representations [103][104][105][106][107]. A much detailed representation and role of ventral striatum 573 and its distinct relation to lateral and medial OFC also could be a key factor to 574 study [108][109][110][111].…”
mentioning
confidence: 99%
“…On the other hand, 569 the ventral striatum was found to represent a unified quantity as a combination of 570 subjective value and internal motivation using different kind of neurons [55]. Several 571 computational accounts have explained possible implementations of such 572 representations [103][104][105][106][107]. A much detailed representation and role of ventral striatum 573 and its distinct relation to lateral and medial OFC also could be a key factor to 574 study [108][109][110][111].…”
mentioning
confidence: 99%