Design of Scalable Neurotransmitter-mediated Biohybrid Synapse

Bio-inspired computing revolutionizes machine learning and artificial intelligence by mimicking efficient biological systems. Artificial neural networks (ANNs) are inspired by the brain, in which the synaptic connectivity between neurons resolves complex classification and data processing tasks. Although ANNs closely emulate biological neural networks (BNNs), the BNN relies heavily on neurochemical synaptic transmission which contributes to the stochastic computing of the brain. In biological synapses, membrane-bound neurotransmitters are released in quantal events to the extracellular space at synaptic clefts. Toward closely mimicking the biological system, we introduce a new biohybrid synapse that produces quantal neurotransmitter secretion to relay information to the artificial postsynaptic receptor. The presented neurochemical biohybrid synapse paves the way for a new generation of brain-inspired computing by enabling single biological cell to single artificial neuron connectivity. We also demonstrate the dynamic synaptic weighting in the biohybrid synapse which affects synaptic activities.