Bidirectional Phosphorescent Neuroplasticity for All-Optical Neurovision.

Area of Science:

• Optoelectronics
• Materials Science
• Computational Neuroscience

Background:

• All-optical neuromorphic systems promise advancements in optical computing and imaging.
• Current all-optical hardware often lacks bidirectional neuroplasticity, limiting training and inference capabilities.
• Bidirectional synaptic plasticity is crucial for mimicking biological neural networks.

Purpose of the Study:

• To explore bidirectional neuroplasticity in carbon dot phosphorescence (CDP) for optical neuromorphic applications.
• To demonstrate CDP's capability for neuroregulation of photonic intensity.
• To integrate CDP into an optical neural network for real-time motion tracking.

Main Methods:

• Investigated potentiation and depression synaptic behaviors in CDP.
• Utilized delayed exciton release and superimposition dynamics for light signal conversion.
• Integrated CDP with an optical neural network for experimental validation.

Main Results:

• Achieved bidirectional neuroplasticity in CDP, enabling photonic intensity neuroregulation.
• Demonstrated CDP's function as a neuroconverter for converting pulse light into excitatory and inhibitory outputs.
• Real-time motion tracking of light spots with 96% accuracy was achieved using the integrated system.

Conclusions:

• Carbon dot phosphorescence exhibits essential bidirectional synaptic behaviors for all-optical neuromorphic computing.
• The developed phosphor-based neuromorphic device enables efficient neuromorphic vision and real-time optical signal processing.
• This technology holds potential for broader applications in all-optical imaging and computing using phosphorescent architectures.