T-maze Training of a Recurrent CA3 Model Reveals the Necessity of Novelty-Based Modulation of LTP in Hippocampal Region CA3

The rat hippocampus has been shown to mediate a large set of spatial navigation tasks such as the simple T-maze. We investigated the performance of a minimal computational, but biologically based, model of CA3 on this task. For successful performance, the model needs to generate and maintain neuronal codes for each of the two arms of the T-maze. Moreover, each code must be distinctively recalled in a goal-dependent manner. The development of such neuronal codes is aided by the appearance of repetitively firing recurrent neurons – known as local context units, analogous to hippocampal place cells – which promote spatiotemporal association within the T-maze training sequences. The number, longevity, and connectivity of local context units exclusively coding for each arm of the maze grow with training. Although with too much training, the coding for one arm uncontrollably dominates over the other code, and goal appropriate choice-behavior is lost. That is, successful network codes can easily deteriorate with overtraining. The amount of training that produces this deterioration in performance depends on other network parameters. Rather than a failure of the model, we believe these results tell us something important about the biology of the hippocampal system. That is, this result provides support for the hypothesis of a hippocampal afferent system which down-regulates LTP once a task has been successfully learned. Modulatory systems (e.g., dopaminergic, generally D1/D5r) exist which are candidates for this functional role.