Sridhar R. Jagannathan

The paper uses machine learning to classify sleep stages in fruit flies based on long-term electrophysiology data, identifying distinct temporal stages and their associated brain activity.

Annotating the 127,978-neuron FlyWire fly connectome yields 4,552 cell types, 3,094 cross-validated against the hemibrain; cell-type edges above 10 synapses or 1.1% of input recur over 90% across hemispheres.

The paper shows how coherent neural oscillations in Drosophila create filters that promote quiescence over navigation by hierarchically overriding locomotion-promoting networks.

This study examines how decreasing alertness affects perceptual decision-making by analyzing neural and behavioral dynamics in humans using EEG and computational models.

This study examines fluctuations in neural complexity during wakefulness using MEG and fMRI, finding links to conscious level and cognitive performance.

The paper investigates how high and low arousal levels affect cognitive flexibility during probabilistic learning in humans, finding distinct decision-making patterns under each condition.

This study shows that postsynaptic plasticity at cholinergic synapses in Drosophila mushroom bodies is essential for appetitive memory induction but not aversive memories.

The paper describes the Drosophila olfactory system using a connectome, identifies information flow patterns, and quantifies stereotypy across individuals.

The natverse provides an open-source R package suite for analyzing neuroanatomical data, enabling analysis, visualization, and comparison across neuronal datasets.

The paper develops automated methods to detect and control for micro variations in alertness during EEG experiments, validated on datasets where participants fall asleep.