Dissemin is shutting down on January 1st, 2025

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Elsevier, Neural Networks, 5(5), p. 789-803

DOI: 10.1016/s0893-6080(05)80140-6

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A learning rule based on empirically-derived activity-dependent neuromodulation supports operant conditioning in a small network

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Activity-dependent neuromodulation has been proposed as a cellular mechanism for classical conditioning in Aplysia. Previously, we developed a mathematical model of an Aplysia sensory neuron that reflects the subcellular processes underlying this form of associative plasticity. This model could simulate features of nonassociative learning and classical conditioning. In the present study, we tested the hypothesis that activity-dependent neuromodulation could also support operant conditioning. We used a network of six neurons, two of which were adaptive elements with an associative learning rule based on activity-dependent neuromodulation. A two-neuron central pattern generator (CPG) drove the network between two output states. We simulated operant conditioning by delivering reinforcement when one selected output occurred. The network exhibited several features of operant conditioning, including extinction and sensitivity to reversed contingencies, the magnitude of reinforcement, the delay of reinforcement, and contingency.