UTCS Artificial Intelligence
Structure and Capacity of Hippocampal Memory: The Convergence-Zone model
Active from 1994 - 2000
Inspired by Damasio's convergence-zone idea, the inputs to the memory are assumed to be represented locally in perceptual maps, and the memory encoding is a sparse random pattern in the hippocampus. Such a memory can be analyzed mathematically and simulated computationally, and it suggests how the hippocampal memory can have a high capacity even with sparse connectivity and a relatively small number of computational units. One-shot storage is shown to require large learning rates, and temporary storage (during transfer to neocortex) possible through weight normalization.
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