Artificial visual neuron based on threshold switching memristors

Abstract

The human visual system encodes optical information perceived by photoreceptors in the retina into neural spikes and then processes them by the visual cortex, with high efficiency and low energy consumption. Inspired by this information processing mode, an universal artificial neuron constructed with a resistor (Rs) and a threshold switching memristor can realize rate coding by modulating pulse parameters and the resistance of Rs. Owing to the absence of an external parallel capacitor, the artificial neuron has minimized chip area. In addition, an artificial visual neuron is proposed by replacing Rs in the artificial neuron with a photo-resistor. The oscillation frequency of the artificial visual neuron depends on the distance between the photo-resistor and light, which is fundamental to acquiring depth perception for precise recognition and learning. A visual perception system with the artificial visual neuron can accurately and conceptually emulate the self-regulation process of the speed control system in driverless automobiles. Therefore, the artificial visual neuron can process efficiently sensory data, reduce or eliminate data transfer and conversion at sensor/processor interfaces, and expand its application in the field of artificial intelligence.

Artificial Touch Neurons Based on Starch Memristors and Hydrogel Sensors

“…In recent years, several major breakthroughs have been made in combining advanced biosensing and neuromorphic engineering techniques. − In particular, different neuromorphic systems based on field-effect transistor synaptic devices have been developed for tactile simulation . For example, Zhu et al used piezoresistive sensors to convert a pressure stimulus into an electrical signal that is transmitted through a soft ion conductor to an indium tungsten oxide synaptic transistor.…”