The hippocampus and the brain: a neural network model

A brain-mapped neural network that combines attentional and configural mechanisms is able to characterize the attributes of multiple classical conditioning paradigms and to describe the effects of many neurophysiological manipulations. As shown in Buhusi and Schmajuk (1996), the attentional-configural model describes neural activity in several brain regions. As also shown in Buhusi and Schmajuk (1996), the attentional-configural model has been applied to the description of the effects of lesions. Schmajuk and Buhusi (1997) illustrated how the configural model correctly describes the effect of these lesions on discrimination paradigms in which stimuli can act as a simple conditioned stimulus (CS) or an occasion setter. Buhusi, et al. (1998) showed that the model can offer a resolution for the apparently conflicting results of hippocampal selective and nonselective lesions on latent inhibition. Schmajuk, et al. (1998) showed that the attentional model offers a description of the interaction between the procedural design and administration of dopaminergic drugs on latent inhibition. Many times, the effect of brain manipulations seems to be specific to the parametric conditions of the experiment, duration of the CS in the case of hippocampal lesions and trace conditioning, procedure and total time of preexposure in the case of hippocampal lesions and latent inhibition, and CS and unconditioned stimulus (US) intensity and duration in the case of dopaminergic agents and latent inhibition. The specificity of these results is well captured by the neural network approaches described in this article. We can now describe, in terms of the model, the functional anatomy of eyeblink conditioning, in animals and humans presented. Although the combination of traditional and advanced technologies can bring an enormous amount of exciting information about how different regions in the human brain participate in eyeblink conditioning, our understanding of the functionality of these regions can only be achieved with the help of formal neural network models