arXiv subjects

Anuj K. Sharma

Publications and source records attributed to Anuj K. Sharma.

3 recordsLinked to original sources

arXiv2024

Olfactory learning alters navigation strategies and behavioral variability in C. elegans

Animals adjust their behavioral response to sensory input adaptively depending on past experiences. The flexible brain computation is crucial for survival and is of great interest in neuroscience. The nematode C. elegans modulates its navigation behavior depending on the association of odor butanone with food (appetitive training) or starvation (aversive training), and will then climb up the butanone gradient or ignore it, respectively. However, the exact change in navigation strategy in response to learning is still unknown. Here we study the learned odor navigation in worms by combining precise experimental measurement and a novel descriptive model of navigation. Our model consists of two known navigation strategies in worms: biased random walk and weathervaning. We infer weights on these strategies by applying the model to worm navigation trajectories and the exact odor concentration it experiences. Compared to naive worms, appetitive trained worms up-regulate the biased random walk strategy, and aversive trained worms down-regulate the weathervaning strategy. The statistical model provides prediction with $>90 \%$ accuracy of the past training condition given navigation data, which outperforms the classical chemotaxis metric. We find that the behavioral variability is altered by learning, such that worms are less variable after training compared to naive ones. The model further predicts the learning-dependent response and variability under optogenetic perturbation of the olfactory neuron AWC$^\mathrm{ON}$. Lastly, we investigate neural circuits downstream from AWC$^\mathrm{ON}$ that are differentially recruited for learned odor-guided navigation. Together, we provide a new paradigm to quantify flexible navigation algorithms and pinpoint the underlying neural substrates.

q-bio.NC↗

arXiv2023

Neural signal propagation atlas of C. elegans

A fundamental problem in neuroscience is understanding how a network's properties dictate its function. Connectomics provides one avenue to predict nervous system function. To test this explicitly, we systematically measure signal propagation in 23,427 pairs of neurons across the head of the nematode Caenorhabditis elegans by direct optogenetic activation and simultaneous whole-brain calcium imaging. We measure the sign (excitatory or inhibitory), strength, temporal properties, and causal direction of signal propagation between these neurons to create a functional atlas. We find that signal propagation differs from predictions based on anatomy. Using mutants, we show that extrasynaptic signaling not visible from anatomy contributes to this difference. We identify many instances of dense-core-vesicle dependent signaling on seconds-or-less timescales that evoke acute calcium transients often where no direct wired connection exists but where relevant neuropeptides and receptors are expressed. We propose that here extrasynaptically released neuropeptides serve a similar function as that of classical neurotransmitters. Finally, our measured signal propagation atlas better predicts neural dynamics of spontaneous activity than does anatomy. We conclude that both synaptic and extrasynaptic signaling drive neural dynamics on short timescales and that measurement of evoked signal propagation are critical for interpreting neural function.

q-bio.NC↗

arXiv2021

Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training

We present an automated method to track and identify neurons in C. elegans, called "fast Deep Learning Correspondence" or fDLC, based on the transformer network architecture. The model is trained once on empirically derived synthetic data and then predicts neural correspondence across held-out real animals via transfer learning. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL [1]. Using only position information, the method achieves 80.0% accuracy at tracking neurons within an individual and 65.8% accuracy at identifying neurons across individuals. Accuracy is even higher on a published dataset [2]. Accuracy reaches 76.5% when using color information from NeuroPAL. Unlike previous methods, fDLC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10 ms making it suitable for future real-time applications.

q-bio.QM↗