Sequence Learning in Associative Neuronal-Astrocytic Networks

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 even its most brain-derived branch, neuromorphic computing. Overturning our assumptions of how the brain works, the recent exploration of astrocytes reveals how these long-neglected brain cells dynamically regulate learning by interacting with neuronal activity at the synaptic level. Following recent experimental studies, we designed an associative, Hopfield-type, neuronal-astrocytic network and analyzed the dynamics of the interaction between neurons and astrocytes. We show how astrocytes were sufficient to trigger transitions between learned memories in the network and derived the timing of these transitions based on the dynamics of the calcium-dependent slow-currents in the astrocytic processes. We further evaluated the proposed brain-morphic mechanism for sequence learning by emulating astrocytic atrophy. We show that memory recall became largely impaired after a critical point of affected astrocytes was reached. These results support our ongoing efforts to harness the computational power of non-neuronal elements for neuromorphic information processing.