Christopher W. Lynn

Heavy-tailed connections support robust neural communication

The paper shows heavy-tailed synaptic connections improve neural communication robustness against noise across species and brain regions.

Hierarchical Community Structure of the Adult Drosophila Connectome Reveals Conserved Circuit Archetypes

The paper analyzes the Drosophila connectome to reveal hierarchical community structures and identifies functional circuit archetypes.

Decay in transcriptional information flow is a hallmark of cellular aging

The paper quantifies age-related decline in transcriptional information flow using information theory and single-cell RNA-seq data, finding reduced mutual information between transcription factors and target genes.

LSD flattens the hierarchy of directed information flow in fast whole-brain dynamics

This study shows LSD reduces directed information hierarchy in the brain by balancing signal sending and receiving, as measured by MEG data.

Heavy-tailed neuronal connectivity arises from Hebbian self-organization

The paper shows that heavy-tailed neuronal connectivity and clustering emerge from a minimal model of Hebbian self-organization, confirmed in animal connectomes.

Neurophysiological evidence for cognitive map formation during sequence learning

This study links cognitive map formation during sequence learning to neural activity in the temporal lobe using behavioral modeling and iEEG recordings.

Efficient Coding in the Economics of Human Brain Connectomics

The paper develops a theory of efficient coding in brain networks and shows that structural connectivity balances information fidelity and compression, predicting behavior and explaining biological organization.

A pairwise maximum entropy model uncovers the white matter scaffold underlying emergent dynamics in intracranial EEG

The paper uses a pairwise maximum entropy model to analyze intracranial EEG data and shows it captures neural dynamics and identifies white matter connections.