THE ROLE OF STRUCTURAL PLASTICITY AND SYNAPTIC CONSOLIDATION FOR MEMORY AND AMNESIA IN A MODEL OF CORTICO-HIPPOCAMPAL INTERPLAY

摘要

This simulation study explores how structural processes and synaptic consolidation during hippocampal memory replay can improve the performance of neocortical neural networks by emulating high effective connectivity in networks that have only low anatomical connectivity. We model ongoing structural plasticity such that, in each time step, a certain fraction of the unconsolidated synapses are eliminated and replaced by new synapses generated at random locations. Simultaneous replay of novel memories consolidates some of the cortical synapses according to Hebbian learning. By this procedure sparsely connected networks can become functionally equivalent to densely connected networks, thereby storing a large amount of information with a tiny number of synapses. In particular, it is possible to store up to E~s ≤ log_2 n bits of information per synapse in simple networks of n neurons. This is much more than the well-known bound E ≤0.72 bits per synapse for static networks. It turns out that sufficiently fast learning requires a significant number of silent unconsolidated synapses. Thus, with lifetime and stored memories, the number of unconsolidated synapses and thus the ability to learn will decrease gradually. This leads to the discussion of various memory-related effects such as catastrophic forgetting and Ribot gradients in retrograde amnesia.