  Astrocytes, the most abundant type of glial cell, play a fundamental role in
memory. Despite most hippocampal synapses being contacted by an astrocyte,
there are no current theories that explain how neurons, synapses, and
astrocytes might collectively contribute to memory function. We demonstrate
that fundamental aspects of astrocyte morphology and physiology naturally lead
to a dynamic, high-capacity associative memory system. The neuron-astrocyte
networks generated by our framework are closely related to popular machine
learning architectures known as Dense Associative Memories or Modern Hopfield
Networks. In their known biological implementations the ratio of stored
memories to the number of neurons remains constant, despite the growth of the
network size. Our work demonstrates that neuron-astrocyte networks follow
superior, supralinear memory scaling laws, outperforming all known biological
implementations of Dense Associative Memory. This theoretical link suggests the
exciting and previously unnoticed possibility that memories could be stored, at
least in part, within astrocytes rather than solely in the synaptic weights
between neurons.
