The sensory nervous system (SNS) builds up the association between external stimuli and the response of organisms. In this system, habituation is a fundamental characteristic that filters out irrelevantly repetitive information and makes the SNS adapt to the external environment. To emulate this critical process in electronic devices, a LixSiOy‐based memristor (TiN/LixSiOy/Pt) is developed where the temporal response under repetitive stimulation is similar to that of habituation. By connecting this synaptic device to a leaky integrate‐and‐fire neuron based on a Ag/SiO2:Ag/Au memristor, a fully memristive SNS with habituation is experimentally demonstrated. Finally, a habituation spiking neural network based on the SNS is built and its application in obstacle avoidance for robot navigation is successfully presented. The results provide that a direct emulation of the biologically inspired learning process by memristors could be a sound choice for neuromorphic hardware implementation.
This work was conducted by researchers in the team led by Prof. LIU Ming at Institute of Microelectronics of Chinese Academy of Sciences, and was recently published in Advanced Materials. PhD students WU Zuheng and LU Jikai at University of Chinese Academy of Sciences (institute: Institute of Microelectronics of Chinese Academy of Sciences) contributed equally to this work; Prof. LIU is the corresponding author, who is also a doctoral supervisor at University of Chinese Academy of Sciences.
