Abstract
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.