Harnessing Biological Nanopores for Sustainable Neuromorphic Computing: Toward Scalable, Biocompatible, and Energy-Efficient Neural Networks

Objective

Artificial intelligence is reshaping our world, but its progress carries an escalating energy crisis. Inspired by the brain’s unmatched efficiency, ionic neuromorphic computing offers a path to sustainable hardware, uniting biocompatibility, energy efficiency, multi-charge carriers, and scalability. Recent demonstrations of ionic memristors and reservoir computing show promise, yet devices remain difficult to reproduce and poorly integrated. Biological nanopores offer a compelling solution: they are nanometer-scale, self-assembling, atomically uniform, and tunable by mutation. Their ability to display memristive or volatile rectifying behaviour positions them as natural candidates for neuromorphic hardware. However, the unresolved physical basis of their stochastic conductance gating remains a barrier for computation integration. To date, neither nanopores nor other ionic platforms have achieved true in-hardware learning or network-level computation. NeuroPORE will address these gaps by two objectives: (1) combining advanced optical imaging with electrical and label-free-optical readouts to map nanopore assembly and gating with unprecedented spatio-temporal resolution. These insights will guide the design of nanopore-based neuromorphic nanofluidic devices, culminating in (2) the first proof-of-concept demonstrations of: a memristive crossbar, a minimal ionic ‘neuron’, and in-hardware learning of an ionic neural-network. My expertise in single-molecule fluorescence microscopy, biophysics, and machine-learning analyses, provides the foundation to realise this vision. This fellowship will expand my skills into protein engineering, nanofluidics, and neuromorphic hardware design, while strengthening leadership, project management, and international collaborations. NeuroPORE will advance sustainable AI and open pathways to diagnostic and bioelectronic applications, positioning me to lead future interdisciplinary research at the physics–biology–computing interface.