Journal Article

Computational principles of synaptic memory consolidation

Fusi, Stefano, Benna, Marcus K

2016

Overview

The biological mechanisms underlying memory are complex and typically involve multiple molecular processes operating on timescales ranging from fractions of a second to years. The authors show using a mathematical model of synaptic plasticity and consolidation that this complexity can help explain the formidable memory capacity of biological systems. Memories are stored and retained through complex, coupled processes operating on multiple timescales. To understand the computational principles behind these intricate networks of interactions, we construct a broad class of synaptic models that efficiently harness biological complexity to preserve numerous memories by protecting them against the adverse effects of overwriting. The memory capacity scales almost linearly with the number of synapses, which is a substantial improvement over the square root scaling of previous models. This was achieved by combining multiple dynamical processes that initially store memories in fast variables and then progressively transfer them to slower variables. Notably, the interactions between fast and slow variables are bidirectional. The proposed models are robust to parameter perturbations and can explain several properties of biological memory, including delayed expression of synaptic modifications, metaplasticity, and spacing effects.

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 631/378/1595/2167 / 631/378/1595/2638 / Animal Genetics and Genomics / Animals / Behavioral Sciences / Biological Techniques / Biomedicine / Computer Simulation / Lifetime / Memory / Memory - physiology / Memory consolidation / Memory Consolidation - physiology / Models, Neurological / Nerve Net - physiology / Neurobiology / Neuronal Plasticity - physiology / Neurons - physiology / Neurosciences / Physiological aspects / Surgeons / Synapses / Synapses - physiology