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

Mougkogiannis, Panagiotis; Adamatzky, Andrew

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Panagiotis Mougkogiannis


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.

Journal Article Type Article
Acceptance Date Jun 5, 2024
Online Publication Date Jun 6, 2024
Deposit Date Jun 11, 2024
Publicly Available Date Jun 12, 2024
Journal Materials Advances
Print ISSN 2633-5409
Electronic ISSN 2633-5409
Publisher Royal Society of Chemistry
Peer Reviewed Peer Reviewed
Public URL


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