Bio-Inspired Vision and Neuromorphic Image Processing Using Printable Metal Oxide Photonic Synapses

Abstract

We propose a neuromorphic vision system that uses digitally printed metal oxide photonic synapses with optical tunability and temporally correlated plasticity. The neuromorphic vision system provides the first demonstration of encoding ambient light intensity in the time domain and captures optical images by encoding their pixel intensity into pulsatile signals in real time, analogous to a biological visual retina and optic nerve. The system can then process the information and form memory, emulating the image data processing in the human brain. The system can realize image-preprocessing functions to increase image contrast and reduce image background noise, thereby effectively improving the classification and recognition accuracy. Dynamic stimulation of visual cortical prosthetics shows that the system is capable of dynamic perception and memory to detect motion and mimic coherent visual perception. Furthermore, the photonic synapses with a two-terminal structure using printed semiconducting fiber arrays could also facilitate large-scale integration. The proposed system offers the potential to simplify neuromorphic visual circuits and may have applications in autonomous smart devices, such as driverless cars, smart surveillance systems, and intelligent healthcare.