Cortical Architecture Theory
Principled theories of cortical hierarchy in artificial neural networks — bridging neuroscience and deep learning architecture design.
Calibration & Uncertainty
Theoretical and empirical advances in neural calibration — making cortical AI confidence scores reliable for high-stakes decision support.
Certified Robustness
Provably robust cortical networks — verified resistance to adversarial perturbations with tight, computationally efficient certification bounds.
Neuroimaging AI
Deep learning methods for fMRI, EEG, and MEG analysis — cortical connectivity mapping, biomarker discovery, and diagnostic AI.
Efficient Cortical Inference
Hardware-aware compression of cortical architectures — structured pruning, quantisation, and dynamic computation inspired by cortical sparsity.
Continual Cortical Learning
Neural systems that learn continuously without catastrophic forgetting — inspired by cortical synaptic consolidation and hippocampal replay.
Research Output
Cortical AI research with real-world precision deployment impact.
Research Collaboration
We partner with neuroscience labs and AI research groups on joint publications and funded programmes.
Collaborate