7 - Classical Conditioning Phenomena Predicted by a Drive-Reinforcement Model of Neuronal Function

Abstract

Publisher Summary This chapter discusses the classical conditioning phenomena predicted by a drive-reinforcement model of the neuronal function and describes several modifications to the Hebbian neuronal model. The modifications yield a model that is shown to be more nearly in accord with animal learning phenomena than those that are observed experimentally. The proposed model is an extension of the Sutton–Barto (SB) model. The chapter further describes how the neuronal model predicts a wide range of classical conditioning phenomena. The model offers a way of defining drives and reinforces at a neuronal level such that a neurobiological basis is suggested for animal learning. The ability of the neuronal model to predict animal learning phenomena is improved if, instead of correlating positive and negative changes in neuronal inputs with changes in neuronal outputs, only positive changes in inputs are correlated with changes in outputs. The positive changes in inputs refer to increases in the frequency of action potentials at a synapse, whether the synapse is excitatory or inhibitory. The negative changes in inputs refer to decreases in the frequency of action potentials at a synapse, whether the synapse is excitatory or inhibitory.