Examining the interface of neuroscience and robotics, developing methods for control and tactile feedback in prosthetic hands

My research combines developments in two difference fields. In computational neuroscience and computer science, Spiking Neural Networks (SNN) are heralded as the third generation of neural networks due to their utilization of the temporal domain in a feedforward structure. Coupled with a significant reduction in required computational resources relative to previous network structures, they offer a new technique to explore our world. In the field of tactile robotics, the research team here at Bristol have pioneered a 3D printed finger-like structure known as the TacTip. The silicone mould depresses upon contact with a surface and current deep learning techniques are utilised in the lab to detect surface texture.

In a pursuit to explore the feasibility of human robot interaction via prosthesis, my research aims to incorporate the low-computational power of SNN with a scaled-down version of the TacTip on a prosthetic hand. To begin, I am developing a generalizable unsupervised SNN and investigating the success of the network in detecting a unique finger motions such as tapping, edge-detection and slip.

Some ideas for future work throughout my PhD progression involve haptic feedback for the user and incorporation across multi-finger TacTips.