An adaptive threshold learning algorithm for classical conditioning

A neuronal model featuring the ability to encode the spatiotemporal relations between input signals is proposed to delineate some of the aspects of classical conditioning. The model uses a spatiotemporal neuron (STEN) and adaptive threshold learning (ATL). During learning, both threshold, and weights are updated as training proceeds. Computer simulations demonstrate that the model exhibits the basic properties of delay and trace conditioning, different ISI effects, blocking, overshadowing, and compound stimulus.