An artificial visual neuron with multiplexed rate and time-to-first-spike coding
Neurons
0301 basic medicine
03 medical and health sciences
Science
Q
Models, Neurological
Visual Perception
Humans
Action Potentials
Neural Networks, Computer
Article
DOI:
10.1038/s41467-024-48103-9
Publication Date:
2024-05-01T14:48:33Z
AUTHORS (15)
ABSTRACT
AbstractHuman visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (54)
CITATIONS (26)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....