仿生神经网络实现热刺激下的快速缩手反射，提升假肢安全反馈。
A Bio-mimetic Neuromorphic Model for Heat-evoked Nociceptive Withdrawal Reflex in Upper Limb
- 模仿人体反射弧结构与编码方式，用脉冲神经网络建模
- 能模拟人类的时空累积效应，响应延迟随刺激强度变化
- 适合用于假肢或机器人的人体级触觉反馈系统
痛觉退缩反射（NWR）是机体在危险环境中自我保护的重要机制。为使假肢或自主机器人能及时感知并响应热刺激，需建立生物可解释的温度信息处理模型。本文提出一种仿生脉冲神经网络，模拟反射弧的结构与编码方式，并采用生物合理化的奖励调制尖峰时序依赖可塑性算法进行训练。在辐射热刺激实验中，仅该神经形态模型表现出与人类相似的空间累积效应（SS）和时间累积效应（TS），且在线编码的相对脉冲延迟可匹配刺激强度。该模型更高的生物合理性，有望提升神经假体中的感觉反馈性能。
原文摘要 · Abstract (English)
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.
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