Self-powered analogue neuromorphic system for multimodal sensing, encoding and learning with diffusive and drift memristors

Biological systems process multimodal analogue sensory inputs with remarkable efficiency, seamlessly integrating sensing, encoding and learning. Replicating this capability in electronics has typically required complementary metal–oxide–semiconductor-based digital architectures with analogue-to-digital conversion, external power and off-chip processing, resulting in high energy consumption and complexity. Here we present a fully analogue, self-powered neuromorphic system that unites multimodal sensing, spike encoding and unsupervised learning entirely in hardware using drift and diffusive memristors. Built solely from sensors and memristors, the platform operates without digital circuitry or external power, enabling autonomous, real-time analogue processing. Diffusive memristors capture temporal correlations from diverse inputs, while drift memristors provide non-volatile synaptic weights. Supporting both homo-synaptic and hetero-synaptic plasticity, the system achieves unsupervised learning across multiple sensory channels. By integrating sensing, computation, memory and learning on a single printed circuit board, this work establishes a compact, energy-efficient hardware paradigm for neuromorphic processing and multimodal sensor fusion. A self-powered, fully analogue neuromorphic platform using drift and diffusive memristors unites multimodal sensing, spike encoding and learning, enabling compact, energy-efficient bio-inspired computing.