Spike trains in PANI-proteinoid nanomaterials with different light pulse rates

Abstract

Bio-inspired computing emulates the brain's information processing power in artificial architectures. We investigate the modification of emergent spike train dynamics in hybrid networks of polyaniline (PANI) nanomaterials hybridised with proteinoids under different optical stimulation frequencies. Hierarchical clustering of photoelectrochemical response data categorises activity patterns for 1 Hz, 5 Hz, and 20 Hz pulsed yellow light inputs. Importantly, the driving frequency controls nanofiber excitability and temporal coordination, with 1 Hz causing erratic spike bursts, 5 Hz rhythmic oscillations, and 20 Hz fast but unsustained responses. Raster plots and peristimulus time histograms show a development from stochastic to deterministic to complicated nonlinear dynamics like neuromorphic systems. Modelling the network as a forest-fire automaton quantifies spatiotemporal activity propagation, where illumination frequency modulates node ignition and recovery probability, shaping spike clusters and coherence. Photonic stimulation can modify information encoding regimes in these PANI-proteinoid composites, suggesting they can be synthetic neural networks. Our findings provide insights for constructing adaptive, brain-inspired computer systems by showing extrinsic control over the network's self-organized dynamics. The ability to delicately tune nanofiber excitation–relaxation cycles using optical pulse trains creates a prototype for biophysically-motivated perception, learning, and cognitive models in empirically accessible material frameworks.