Search Results for author:
Found 7 papers, 3 papers with code
Coordinated Multi-Neighborhood Learning on a Directed Acyclic Graph
Our method facilitates causal discovery without learning the entire DAG structure.
Stochastic Control Barrier Functions with Bayesian Inference for Unknown Stochastic Differential Equations
Control barrier functions are widely used to synthesize safety-critical controls.
Neural correlates of cognitive ability and visuo-motor speed: validation of IDoCT on UK Biobank Data
Previously, we developed a novel modelling approach that leverages continuous performance recordings from large-cohort studies to achieve an iterative decomposition of cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive abilities, and device and visuomotor latencies, whilst recalibrating trial-difficulty scales.
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
Finally, we derive an in-silico implementation of ModProp that could serve as a low-complexity and causal alternative to backpropagation through time.
Challenges for machine learning in clinical translation of big data imaging studies
The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology.
The NonHuman Primate Neuroimaging & Neuroanatomy Project
Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries.
