Sequence learning in Associative Neuronal-Astrocytic Network

TL;DR

This paper introduces a brain-inspired associative network model incorporating astrocytes, demonstrating their role in sequence learning, memory transitions, and the impact of astrocytic atrophy on memory recall, advancing neuromorphic computing.

Contribution

It presents a novel neuronal-astrocytic network model that captures astrocyte-mediated sequence learning and memory dynamics, supported by mathematical analysis and simulations.

Findings

Astrocytes can trigger memory transitions in neuronal networks.

Timing of transitions is governed by calcium-dependent astrocytic currents.

Memory recall deteriorates with astrocytic atrophy.

Abstract

The neuronal paradigm of studying the brain has left us with limitations in both our understanding of how neurons process information to achieve biological intelligence and how such knowledge may be translated into artificial intelligence and its most brain-derived branch, neuromorphic computing. Overturning our fundamental assumptions of how the brain works, the recent exploration of astrocytes is revealing that these long-neglected brain cells dynamically regulate learning by interacting with neuronal activity at the synaptic level. Following recent experimental evidence, we designed an associative, Hopfield-type, neuronal-astrocytic network and analyzed the dynamics of the interaction between neurons and astrocytes. We show that astrocytes were sufficient to trigger transitions between learned memories in the neuronal component of the network. Further, we mathematically derived the timing of the transitions that was governed by the dynamics of the calcium-dependent slow-currents in the astrocytic processes. Overall, we provide a brain-morphic mechanism for sequence learning that is inspired by, and aligns with, recent experimental findings. To evaluate our model, we emulated astrocytic atrophy and showed that memory recall becomes significantly impaired after a critical point of affected astrocytes was reached. This brain-inspired and brain-validated approach supports our ongoing efforts to incorporate non-neuronal computing elements in neuromorphic information processing.