Efficient Partitioning of Memory Systems and Its Importance for Memory Consolidation

By Alex Roxin, Stefano Fusi. 2013

76 citations recorded by the source corpus.

Long-term memories are likely stored in the synaptic weights of neuronal networks in the brain. The storage capacity of such networks depends on the degree of plasticity of their synapses. Highly plastic synapses allow for strong memories, but these are quickly overwritten. On the other hand, less labile synapses result in long-lasting but weak memories. Here we show that the trade-off between memory strength and memory lifetime can be overcome by partitioning the memory system into multiple regions characterized by different levels of synaptic plasticity and transferring memory information from the more to less plastic region. The improvement in memory lifetime is proportional to the number of memory regions, and the initial memory strength can be orders of magnitude larger than in a non-partitioned memory system. This model provides a fundamental computational reason for memory consolidation processes at the systems level.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Synaptic weight decay with selective consolidation enables fast learning without catastrophic forgetting (2019, background): “In systems consolidation, memories are suggested to be transferred to other brain areas for long-term storage such as the hippocampus (Squire and Alvarez, 1995; McClelland et al., 1995; Roxin and Fusi, 2013).”
Computational principles of synaptic memory consolidation (2016, background): “It was proposed to explain memory consolidation at the systems level 8,11 and incorporated into a cascade model of synaptic memory based on multiple biochemical processes that operate on different timescales 9 .”
The Persistence and Transience of Memory. (2017, background): “Given that it is possible to encounter many different environments with different temporal dynamics, a good strategy may be to rely on multiple memory systems that have different balances between persistence and transience (Benna and Fusi, 2016; Roxin and Fusi, 2013).”
Computational principles of biological memory (2015, methodology): “In this case we find an inverse proportionality of the memory trace to time, r(t) = 1/(2λt), which is approximately realized by the cascade and multistage models of Fusi et al. (2005); Roxin and Fusi (2013).”