Biology subjects

Madhusudhanan, J.

Publications and source records attributed to Madhusudhanan, J..

2 recordsLinked to original sources

bioRxiv2026

Flexible modulation of neuronal population dynamics drives variable decision-making in C. elegans

Behavior arises from the interplay between spontaneous brain dynamics and sensory-driven responses, yet how spontaneous neural activity shapes variability in decision-making remains unclear. We leverage the tractable C. elegans nervous system to address this question. During oxygen avoidance, we observe binary trial-to-trial variability in behavioral responses. Whole-brain calcium imaging reveals a brain-wide sensory-to-motor transformation in which sensory neurons faithfully encode the stimulus but do not predict choice. Instead, decision-related information is distributed across interneurons and motor neurons, encoded through a neuronal subspace. This decision-biasing state evolves slowly during the pre-stimulus period, resembling preparatory dynamics for spontaneous behavioral transitions but receives neuromodulatory contributions. Optogenetic manipulations reveal that only a subset of neurons within this distributed representation are causally connected to choice. This reveals a dissociation between broad information sharing and control via dedicated localized nodes. Thus, response variability arises from slowly evolving modulation of brain states rather than stochastic circuit noise.

neuroscience↗

bioRxiv2025

BarlowTrack: A Self-Supervised Framework for Zero-Shot Multi-Object Cell Tracking

Recent advances in neuroscience have made it possible to image large brain regions at single-cell resolution. However, classic methods for processing these videos into neuronal time series fail in the presence of large and nonrigid deformations, in particular for freely moving animals. Several successful algorithms have been proposed to solve this problem in moving C. elegans, but they are highly specific to the conditions of a single research setting. We propose a tracking pipeline based on self-supervised learning that achieves a high level of zero-shot accuracy across conditions, and, for the first time, independent laboratories. We contribute a novel term in the Barlow Twins loss function to encourage decorrelation of features across detected instances at the same time point. To encourage broad adoption, we use the standardized Neurodata Without Borders (NWB) format and we provide a GUI for visualization of the final results and neuronal time series. Finally, we provide a benchmark of datasets with ground truth annotations in the NWB format for further algorithmic development.

bioinformatics↗