activity
20162024
most citedApproximate Bayesian inference as a gauge theory

140 citations · 157 across the 12 of their papers we have counts for

collaborators
Showing q-bio.NCShow all

7 papers · 1 filter

q-bio.NC2021★ 6 cited

Reconsidering Spatial Priors In EEG Source Estimation: Does White Matter Contribute to EEG Rhythms?

PK Douglas, DB Douglas

Electroencephalogram (EEG) has been a core tool used in functional neuroimaging in humans for nearly a hundred years. Because it is inexpensive, easy to implement, and noninvasive,…

q-bio.NC2021

Diurnal variations of resting-state fMRI data: A graph-based analysis

Farzad V. Farahani, Waldemar Karwowski, Mark D Esposito +5

Circadian rhythms synchronize a variety of physiological processes ranging from neural activity and hormone secretion to sleep cycles and feeding habits. Despite significant diurna…

q-bio.NC2020★ 1 cited

Structural Brain Asymmetries in Youths with Combined and Inattentive Presentations of Attention Deficit Hyperactivity Disorder

Cintya Nirvana Dutta, Pamela K. Douglas, Hernando Ombao

Alterations in structural brain laterality are reported in attention-deficit/hyperactivity disorder (ADHD). However, few studies examined differences within presentations of ADHD.…

q-bio.NC2018★ 3 cited

Interpreting Encoding and Decoding Models

Nikolaus Kriegeskorte, Pamela K. Douglas

Encoding and decoding models are widely used in systems, cognitive, and computational neuroscience to make sense of brain-activity data. However, the interpretation of their result…

q-bio.NC2018★ 1 cited

Cognitive computational neuroscience

Nikolaus Kriegeskorte, Pamela K. Douglas

To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments.…

q-bio.NC2017★ 140 cited

Approximate Bayesian inference as a gauge theory

Biswa Sengupta, Karl Friston

In a published paper [Sengupta, 2016], we have proposed that the brain (and other self-organized biological and artificial systems) can be characterized via the mathematical appara…

q-bio.NC2016★ 2 cited

Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms

Jianwen Xie, Pamela K. Douglas, Ying Nian Wu +2

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide a…