Marmaduke Woodman

The paper develops ADAM, a model linking dopamine clearance and structural connectivity to cognitive aging, finding elevated clearance timescales and a structure-age-dependent association with cognitive performance.

The paper proposes a computational framework where cortico-subcortical circuits implement multi-head self-attention, aligning with human brain recordings during speech perception.

BRIDGE is a Python pipeline that reconstructs and validates mean-field models from single neuron dynamics, enabling reproducible and reusable large-scale brain simulations.

This paper identifies a deformable attractor manifold that organizes human resting-state brain dynamics, showing it relates to cognitive variability and pathology.

This paper introduces Virtual Brain Twins (VBTs) as personalized digital brain models that integrate structural and functional data to improve understanding of brain function and support personalized treatment.

This paper introduces VBI, a toolkit for Bayesian inference on virtual brain models, demonstrating its accuracy and reliability through in-silico testing.

This paper develops Bayesian inference methods for Dynamic Causal Modelling using probabilistic programming languages to improve neural dynamics prediction.

This paper introduces SBI-VBMs, a method using deep neural networks to estimate generative parameters in brain disorders through probabilistic inference on virtual brain models.

This study uses deep learning to improve inference on spiking neuron networks, showing better performance in handling noise and missing data compared to traditional methods.

The paper introduces The Virtual Mouse Brain, a platform for simulating whole mouse brain dynamics using connectome data to study health and disease.