Maxwell Shinn

This study shows the superior colliculus contributes additively to logistic decision-making by promoting competing actions independently of stimuli.

This study identifies a low-dimensional subspace in resting state fMRI that captures reliable individual differences and can accurately identify participants.

This paper introduces coppaFISH 3D and CASTalign to enable transcriptomic identification of thousands of in vivo recorded neurons at scale.

The paper introduces Zebra noise and a wavelet-based model to efficiently map visual preferences of thousands of neurons in the mouse visual cortex.

This study applies a drift diffusion model to analyze decision-making dynamics in a transitive inference task, showing stable reaction times despite improved accuracy during learning.

The paper quantifies age-related changes in temporal and spatial scales of resting-state fMRI signals across the lifespan and links these changes to cortical myelination.

This paper identifies phantom oscillations in PCA caused by data smoothness and spatial/temporal shifts, showing how they affect analysis and proposing detection methods.

The paper shows spatial and temporal autocorrelation explain network topology measures in rs-fMRI data and how they change with aging and drug treatment.

The paper identifies a transcriptomic axis that predicts how cortical interneurons modulate activity across brain states.

The paper shows that animals briefly suppress motor output when new sensory evidence becomes available, and neurons in the frontal eye field show a corresponding activity dip.

This study investigates how temporal and reward contexts influence perceptual decision-making by training monkeys and modeling their behavior with generalized drift-diffusion models.

This paper introduces a framework for simulating and fitting generalized drift-diffusion models, showing improved accuracy and efficiency over traditional methods.

The paper introduces a generative null model to create spatially autocorrelated surrogate brain maps for hypothesis testing in brain organization studies.

The paper introduces morphometric similarity networks to map cortical organization and shows they predict cognitive variation across individuals.

The paper constructs structural covariance networks from MRI data and links them to transcriptomic networks, finding coupling between network topology and gene expression in supragranular layers.

The paper examines age-related changes in structural brain networks during adolescence, finding decreased structural correlation and changes in nodal degree in association cortex.

This paper derives mean-field equations for neuronal networks with arbitrary degree distributions and shows they exhibit richer dynamics than Erdos-Renyi networks.