Liam McDaid
Papers
10
Total Citations
304
H-Index
8
About
Liam McDaid is a leading researcher in neuromorphic engineering and bio-inspired computing, whose work bridges the gap between biological neural systems and fault-tolerant hardware. His primary research areas include spiking neural networks (SNNs), astrocyte-neuron interactions, self-repairing systems, and swarm robotics. McDaid’s major contributions lie in developing biologically plausible models that endow artificial systems with homeostatic fault tolerance—a property inspired by the self-repair capabilities of biological networks. His 2017 paper on homeostatic fault tolerance in SNNs (58 citations) and his 2018 work on coupled spiking astrocyte neural networks (50 citations) have been foundational, demonstrating how glial cells can modulate synaptic activity to achieve localized self-repair. He has also pioneered SNN-based robot controllers (49 citations) and self-organizing architectures for mobile robot navigation (45 citations). Beyond theoretical models, McDaid has made practical contributions to robotics hardware, including the Pi-puck extension board for the e-puck robot and ARDebug, an augmented reality tool for debugging swarm systems. His work on fault-tolerant learning using STDP and BCM rules on FPGAs further showcases his impact on robust neuromorphic hardware design.
Research Focus
Key Achievements
Top Papers
- 1
- 2Exploring Self-Repair in a Coupled Spiking Astrocyte Neural Network50 citations · 2018
- 3Biologically Inspired SNN for Robot Control49 citations · 2012
- 4CASE STUDY ON A SELF-ORGANIZING SPIKING NEURAL NETWORK FOR ROBOT NAVIGATION45 citations · 2010
- 5
- 6
- 7Self-repairing mobile robotic car using astrocyte-neuron networks21 citations · 2016
- 8
- 9Fault-Tolerant Learning in Spiking Astrocyte-Neural Networks on FPGAs8 citations · 2018
- 10