No abstract

Restoration of the orienting response to a light by a change in its predictive accuracy.

Hippocampal participation in classical conditioning is described in terms of a multilayer network that portrays stimulus configuration. The network (a) describes behavior in real time, (b) incorporates a layer of "hidden" units positioned between input and output units, (c) includes inputs that are connected to the output directly as well as indirectly through the hidden-unit layer, and (d) uses a biologically plausible backpropagation procedure to train the hidden-unit layer. Nodes and connections in the neural network are mapped onto regional cerebellar, cortical, and hippocampal circuits, and the effect of lesions of different brain regions is formally studied. Computer simulations of the following classical conditioning paradigms are presented: acquisition of delay and trace conditioning, extinction, acquisition-extinction series of delay conditioning, blocking, over-shadowing, discrimination acquisition, discrimination reversal, feature-positive discrimination, conditioned inhibition, negative patterning, positive patterning, and generalization. The model correctly describes the effect of hippocampal and cortical lesions in many of these paradigms, as well as neural activity in hippocampus and medial septum during classical conditioning. Some of these results might be extended to the description of anterograde amnesia in human patients.

Classical conditioning data show that a conditioned stimulus (CS) can act either as a simple CS--eliciting conditioned responses (CRs) by signaling the occurrence of an unconditioned stimulus (US)--or as an occasion setter--controlling the responses generated by another CS. In this article, the authors apply a simple extension of a network model of conditioning, originally presented by N. A. Schmajuk and J. J. DiCarlo (S-D; 1992), to the description of these 2 different CS functions. In the model, CS inputs are connected to the CR output both directly and indirectly through a hidden unit layer that codes configural stimuli. In this framework, a CS acts as (a) a simple stimulus through its direct connections with the output units and as (b) an occasion setter through its indirect configural connections via the hidden units. Computer simulations demonstrate that the network accounts for a large part of the data on occasion setting.