Neural Network Models of the Hippocampus

For the past few days I've been busy making this presentation on neural network models of memory (here's a PDF version). Specifically, the presentation addresses the question of how the hippocampus interacts with the neocortex, beginning with a review of the role of the hippocampus from single cell recording and neuropsychological evidence, continuing into the phenomenon of catastrophic interference, and ending with a conclusion about some fundamental computational tradeoffs in how memory is represented.

One of the most interesting insights about the function of the hippocampus is that it appears to be specialized for quickly learning about specific features, in specific combinations, in specific contexts. This information is stored in a "compressed" format, with very sparse patterns of activation. The cortex cannot absorb this knowledge so quickly because representations there are far more overlapping, which would cause undue interference from a new learning experience on all older facts.

Instead, the hippocampus serves as a temporary "waystation" for these memories, while they undergo a process of memory consolidation, in which the hippocampus is actually interleaving experiences - during sleep, or as recently demonstrated in rats, even while awake - so that the slower neocortical learning systems can slowly extract the meaningful information from these experiences.

Full citations are provided in the presentation, as well as a basic animation of the phenomenon of spreading activation and hebbian learning.