Neuron-Astrocyte Associative Memory
arXiv (Cornell University)(2023)
Abstract
Astrocytes, a unique type of glial cell, are thought to play a significant
role in memory due to their involvement in modulating synaptic plasticity.
Nonetheless, no existing theories explain how neurons, synapses, and astrocytes
could collectively contribute to memory function. To address this, we propose a
biophysical model of neuron-astrocyte interactions that unifies various
viewpoints on astrocyte function in a principled, biologically-grounded
framework. A key aspect of the model is that astrocytes mediate long-range
interactions between distant tripartite synapses. This effectively creates
``multi-neuron synapses" where more than two neurons interact at the same
synapse. Such multi-neuron synapses are ubiquitous in models of Dense
Associative Memory (also known as Modern Hopfield Networks) and are known to
lead to superlinear memory storage capacity, which is a desirable computational
feature. We establish a theoretical relationship between neuron-astrocyte
networks and Dense Associative Memories and demonstrate that neuron-astrocyte
networks have a larger memory storage capacity per compute unit compared to
previously published biological implementations of Dense Associative Memories.
This theoretical correspondence suggests the exciting hypothesis that memories
could be stored, at least partially, within astrocytes instead of in the
synaptic weights between neurons. Importantly, the many-neuron synapses can be
influenced by feedforward signals into the astrocytes, such as neuromodulators,
potentially originating from distant neurons.
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