SMALL NETWORKS OF EMPIRICALLY DERIVED ADAPTIVE ELEMENTS SIMULATE SOME HIGHER-ORDER FEATURES OF CLASSICAL-CONDITIONING

Previously, we developed a single-cell mathematical model of the sensory neurons in Aplysia (Gingrich & Byrne, 1985, 1987). This single-cell model accurately simulated many aspects of empirically observed neuronal plasticity that contribute to simple forms of nonassociative and associative learning. In the present study, we incorporated this empirically derived adaptive element into small networks and examined the ability of these networks to simulate second-order conditioning and blocking. When the single-cell model was incorporated into a three-cell network (Hawkins & Kandel, 1984), we found that constraints imposed by the empirical data limited the ability of the network to simulate both second-order conditioning and blocking. On the other hand, we found that the detailed descriptions of subcellular processes unmasked phenomena relevant to the simulation of blocking, that are not captured by less detailed models. We also incorporated the model of the sensory neuron into a lateral inhibition-type network consisting of five elements. This network successfully simulated both second-order conditioning and blocking more readily than the three-cell network. © 1990.