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Instability-driven electrohydrodynamic neurons for probabilistic computing
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DOI：10.1038/s41467-026-77036-8.png)
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Abstract
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Fluidic memristors provide a liquid-state alternative to semiconductor hardware for energy-efficient neuromorphic computing. Yet, despite progress in synaptic plasticity, fluidic platforms have lacked a threshold-activated leaky integrate-and-fire (LIF) spiking primitive-the core building block of neuronal computation. Here we introduce an electrohydrodynamic neuron based on a floating liquid bridge, where resistance switching is driven by reversible interfacial instability rather than ionic migration or defect evolution in prior devices. This instability-driven mechanism implements LIF integration with a well-defined activation threshold, generates rhythmic spiking, and exhibits an excitation-refractory cycle. The threshold-integration spike encoding retrieves image content from low-signal-to-noise inputs by converting noisy analog signals into sparse spike events, and its intrinsic fluctuations further enable stochastic firing for probabilistic computation. Together with its enhanced performance, these results establish interfacial hydrodynamics as a device-physics route to fluid-based spiking neuromorphic hardware. Fluidic memristors offer a liquid-state alternative to semiconductor hardware but lack a threshold-activated leaky integrate-and-fire (LIF) spiking primitive. Sun et al. address this by reporting a microscale floating liquid bridge neuron that enables LIF spiking and probabilistic computing, completing the core building block of neuronal computation.
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