A Habituation Sensory Nervous System with Memristors

TLDR

The sensory nervous system forms associations between external stimuli and organism responses, and habituation filters irrelevant repetitive information to adapt to the environment. The authors aim to emulate habituation in electronic devices by developing a LiₓSiOᵧ‑based memristor whose temporal response to repetitive stimulation mimics biological habituation. They connect the memristor to a leaky integrate‑and‑fire neuron using an Ag/SiO₂:Ag/Au memristor, creating a fully memristive SNS that demonstrates habituation, and build a habituation spiking neural network for obstacle‑avoidance robot navigation. The results show that directly emulating biologically inspired habituation with memristors is viable and offers a promising approach for neuromorphic hardware.

Abstract

Abstract 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 Li x SiO y ‐based memristor (TiN/Li x SiO y /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/SiO 2 :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.