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

Fengyi Wang, J. Rogelio Guadarrama Olvera, Nitish Thako, Gordon Cheng

TL;DR

This work tackles heat-evoked nociceptive withdrawal reflex (NWR) in the upper limb by presenting a bio-mimetic neuromorphic spiking network that encodes temperature as spike trains. It trains the network with reward-modulated spike-timing-dependent plasticity (R-STDP) to map stimulus intensity to reflex strength and latency, reproducing human-like spatial and temporal summation. The neuromorphic approach yields a graded reflex response tied to stimulus intensity via online spike-latency decoding, outperforming three reference methods that rely on fixed thresholds or binary decisions. The findings suggest potential for low-power, biologically plausible sensory feedback and protective control in neural prostheses and autonomous robots.

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.

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

TL;DR

This work tackles heat-evoked nociceptive withdrawal reflex (NWR) in the upper limb by presenting a bio-mimetic neuromorphic spiking network that encodes temperature as spike trains. It trains the network with reward-modulated spike-timing-dependent plasticity (R-STDP) to map stimulus intensity to reflex strength and latency, reproducing human-like spatial and temporal summation. The neuromorphic approach yields a graded reflex response tied to stimulus intensity via online spike-latency decoding, outperforming three reference methods that rely on fixed thresholds or binary decisions. The findings suggest potential for low-power, biologically plausible sensory feedback and protective control in neural prostheses and autonomous robots.

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.

Paper Structure

This paper contains 17 sections, 13 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Method
  3. Bio-mimetic spiking neural networks
  4. Sensory neurons
  5. Interneuron
  6. Motor neuron
  7. Learning algorithm
  8. Experimental evaluation
  9. Reflex strength
  10. Spatial summation effect
  11. Temporal summation effect
  12. Comparison
  13. Analog reflex system
  14. Neural classifier
  15. Feed-forward spiking neurons
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Illustration of the polysynaptic reflex arc and the proposed bio-mimetic neuromorphic model of NWR.
  • Figure 2: Lines in (a) and (b) shows how $\Delta t$ and reflex strength $g_s$ varies with stimulation intensity, respectively. The dashed line in (b) shows the NWR strength in the human forearm campbell_comparison_1991.
  • Figure 3: (a) and (b) illustrate the stimulation in the SS effect experiment and the corresponding output of the neurons in the model.
  • Figure 4: The simulation of the analog reflex circuit. $V_S$ represents the voltage applied to the motor during the reflex action. $V_c$ represents the motor command from the microcontroller.
  • Figure 5: (a), (b), (c), and (d) illustrate the stimulation and the output of the proposed neuromorphic model, analog system, and feed-forward spiking neurons, respectively.