David M. Halliday
Intelligent Systems Research (United States), University of York
Papers
7
Total Citations
192
H-Index
6
About
David M. Halliday is a pioneering researcher at the intersection of neuromorphic engineering, fault-tolerant systems, and swarm robotics. His primary research areas include bio-inspired neural networks, self-repairing robotic systems, and hardware implementation of spiking neural networks. Halliday’s major contributions center on developing homeostatic fault tolerance mechanisms in spiking neural networks, drawing inspiration from astrocyte-neuron interactions in the brain. His work demonstrates how biological self-repair capabilities can be translated into electronic systems, with key papers achieving 58 and 50 citations respectively. Notably, his 2017 paper on “Homeostatic Fault Tolerance in Spiking Neural Networks” established foundational principles for dynamic hardware resilience. Halliday also made significant practical contributions to robotics, including the Pi-puck extension board (28 citations) that interfaces Raspberry Pi with e-puck robots, and ARDebug (23 citations), an augmented reality tool for debugging swarm robotic systems. His 2016 work on self-repairing mobile robotic cars using astrocyte-neuron networks (21 citations) exemplifies his ability to translate biological principles into functional robotic systems. Through his research on FPGA implementations of fault-tolerant learning and hippocampal-inspired navigation, Halliday continues to advance the frontier of robust, biologically-plausible neural systems for real-world applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Exploring Self-Repair in a Coupled Spiking Astrocyte Neural Network50 citations · 2018
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- 5Self-repairing mobile robotic car using astrocyte-neuron networks21 citations · 2016
- 6Fault-Tolerant Learning in Spiking Astrocyte-Neural Networks on FPGAs8 citations · 2018
- 7