  Brain-inspired computing has the potential to revolutionise the current von
Neumann architecture, advancing machine learning applications. Signal
transmission in the brain relies on voltage-gated ion channels, which exhibit
the electrical behaviour of memristors, resistors with memory. State-of-the-art
technologies currently employ semiconductor-based neuromorphic approaches,
which have already demonstrated their efficacy in machine learning systems.
However, these approaches still cannot match performance achieved by biological
neurons in terms of energy efficiency and size. In this study, we utilise
molecular dynamics simulations, continuum models, and electrophysiological
experiments to propose and realise a bioinspired hydrophobically gated
memristive nanopore. Our findings indicate that hydrophobic gating enables
memory through an electrowetting mechanism, and we establish simple design
rules accordingly. Through the engineering of a biological nanopore, we
successfully replicate the characteristic hysteresis cycles of a memristor
\tr{and construct a synaptic device capable of learning and forgetting}. This
advancement offers a promising pathway for the realization of nanoscale, cost-
and energy-effective, and adaptable bioinspired memristors.
