Heterostimuli-modulated neuromorphic devices were created to emulate chemo-modulated biological associative learning with concurrent volume and wiring transmission. The zinc oxide (ZnO)/polyvinylpyrrolidone nanocomposites sandwiched by silver and indium tin oxide were used to mimic synapse wiring transmission through electrically induced resistivity switching. Broad photostimuli to emulate volume transmission and photoactivated ZnO provided additional modulation of the electric conductive path for synaptic weight adjustment through photo -/electric -associated redox chemistry. The resultant associative learning memristor (ALM) demonstrated rapid learning (1 ms), reliable memory operations, and extended memory retention (>1 day), with a learning efficiency 1,000 times better than the prior ones. A 5 X 5 crossbar of ALM was incorporated into an artificial neural network (ANN) algorithm, demonstrating data -efficient machine learning with 90% accuracy in small training datasets, while conventional ANN shows 76% accuracy. This biomimic network is also >1,000 times more powerefficient than existing models, indicating time -/power -/data -efficient neuromorphic artificial intelligence.

Neuromorphic materials are promising for fabricating artificial synapses for flexible electronics, but they are usually expensive and lack a good combination of electronic and mechanical properties. In this paper, low-cost flexible carbon nanotube/polydimethylsiloxane (CNT/PDMS) nanocomposites were prepared by solution processing. Their neuromorphic properties were studied as a function of PDMS macromolecular network structure. Specifically, the structural defects of the polymer network originating from intermolecular crosslinking reactions were tuned to tailor the electron transfer between carbon nanotubes, resulting in a recorded low switching power consumption (1.40 × 10–10 W) and a high working bending radius of curvature (5 mm) compared to other organic, flexible neuromorphic materials. As-fabricated CNT/PDMS composites demonstrate robust performance for 104 operating cycles under mechanical deformation. Emulation of synaptic functions was also presented, showing long-term potentiation (LTP) and long-term depression (LTD) characteristics. These results lay a foundation for networked polymer-based multifunctional nanocomposites for flexible neuromorphic electronic devices.