Original Contribution: A learning rule based on empirically-derived activity-dependent neuromodulation supports operant conditioning in a small network: Neural Networks: Vol 5, No 5

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.

1. Original Contribution
   1. Applied computing
   2. Computing methodologies
      1. Machine learning