A bioinspired in-materia analog photoelectronic reservoir computing for human action processing.

ABSTRACT: Current computer vision is data-intensive and faces bottlenecks in shrinking computational costs. Incorporating physics into a bioinspired visual system is promising to offer unprecedented energy efficiency, while the mismatch between physical dynamics and bioinspired algorithms makes the processing of real-world samples rather challenging. Here, we report a bioinspired in-materia analogue photoelectronic reservoir computing for dynamic vision processing. Such system is built based on InGaZnO photoelectronic synaptic transistors as the reservoir and a TaO_X-based memristor array as the output layer. A receptive field inspired encoding scheme is implemented, simplifying the feature extraction process. High recognition accuracies (>90%) on four motion recognition datasets are achie

SUBMITTER: Cui H 

PROVIDER: S-EPMC11885466 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature