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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Neuromorphic Tactile Edge Orientation Classification in an Unsupervised Spiking Neural Network.

Fraser L A Macdonald^1,2, Nathan F Lepora^1,2, Jörg Conradt³

¹Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TW, UK.

Sensors (Basel, Switzerland)

|September 23, 2022

Summary

This summary is machine-generated.

This study introduces a novel neuromorphic tactile sensor (NeuroTac) for robotic hands. It accurately detects edge orientations using an event-based optical sensor and spiking neural networks, advancing artificial touch capabilities.

Keywords:

neuromorphic spiking neural network tactile robotics

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Area of Science:

Robotics
Neuroscience
Sensor Technology

Background:

Dexterous manipulation in robotic systems necessitates sophisticated artificial touch.
Current tactile sensors often lack the bio-inspired processing capabilities for complex tasks.
Neuromorphic computing offers a promising avenue for advanced sensory processing.

Purpose of the Study:

To investigate neuromorphic tactile sensation for edge orientation detection.
To integrate an event-based optical tactile sensor with spiking neural networks.
To develop a bio-inspired artificial fingertip for enhanced robotic manipulation.

Main Methods:

An event-based vision system (mini-eDVS) was incorporated into a low-form factor artificial fingertip (NeuroTac).
Tactile data was processed using a Spiking Neural Network with unsupervised Spike-Timing-Dependent Plasticity (STDP) learning.
A 3-nearest neighbours classifier was employed for edge orientation classification.

Main Results:

The NeuroTac sensor reliably detected edge orientations in 10-degree increments.
Accurate classification was achieved during both vertical tapping and horizontal sliding motions.
The system demonstrated effective bio-inspired tactile processing.

Conclusions:

The developed neuromorphic tactile sensor shows high reliability in edge orientation detection.
This technology has the potential to significantly improve tactile sensing in robotics and prosthetics.
Further development could lead to more dexterous and adaptable artificial manipulation systems.