Computational principles of biological memory

By Marcus K. Benna, Stefano Fusi. 2015

17 citations recorded by the source corpus.

Memories are stored, retained, and recollected 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 harnesses biological complexity to preserve numerous memories. 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. Importantly, 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.

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

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

In Neuroscience

Citations in the corpus

Synaptic weight decay with selective consolidation enables fast learning without catastrophic forgetting (2019, methodology): “The synaptic cascade model (Fusi et al., 2005; Benna and Fusi, 2016; Kaplanis et al., 2018) postulates hidden states that, diﬀerent from our model, do not contribute to the eﬀective synaptic strength.”