A Bio-mimetic Neuromorphic Model for Heat-evoked Nociceptive Withdrawal Reflex in Upper Limb

TL;DR

This paper introduces a bio-inspired neuromorphic spiking network that models heat-evoked nociceptive withdrawal reflexes, enabling more realistic sensory feedback for prosthetics and robots by mimicking biological heat response mechanisms.

Contribution

It presents a novel neuromorphic model that captures spatial and temporal summation effects of heat stimuli, trained with a biologically plausible learning rule, improving sensory feedback in prosthetic applications.

Findings

The neuromorphic model exhibits spatial and temporal summation effects similar to humans.

It encodes reflex strength proportionally to stimulus intensity in real-time.

The model outperforms other recent methods in simulating heat-evoked NWR.

Abstract

The nociceptive withdrawal reflex (NWR) is a mechanism to mediate interactions and protect the body from damage in a potentially dangerous environment. To better convey warning signals to users of prosthetic arms or autonomous robots and protect them by triggering a proper NWR, it is useful to use a biological representation of temperature information for fast and effective processing. In this work, we present a neuromorphic spiking network for heat-evoked NWR by mimicking the structure and encoding scheme of the reflex arc. The network is trained with the bio-plausible reward modulated spike timing-dependent plasticity learning algorithm. We evaluated the proposed model and three other methods in recent studies that trigger NWR in an experiment with radiant heat. We found that only the neuromorphic model exhibits the spatial summation (SS) effect and temporal summation (TS) effect similar to humans and can encode the reflex strength matching the intensity of the stimulus in the relative spike latency online. The improved bio-plausibility of this neuromorphic model could improve sensory feedback in neural prostheses.