Jones, B. M. W. | bioRxiv Science

bioRxiv2025

Descending neurons integrate learnt information from mushroom body with context to promote escape behaviour

To behave adaptively in the environment and select appropriate responses to sensory stimuli, animals integrate learnt information about the valences of stimuli with contextual information. While significant progress has been made in understanding how animals learn which stimuli predict positive and negative outcomes, how the outputs of associative learning circuits flexibly promote different behaviours, depending on context, is not well understood. Addressing this question requires mapping the pathways from learning circuits to nerve cord command neurons that promote specific actions, and understanding where and how contextual information converges with these pathways to modulate their activity. These are daunting tasks in larger brains where synaptic-resolution connectivity maps are not available. We, therefore, addressed this question in the tractable Drosophila larva using a combination of connectomic analyses, imaging and manipulation of neural activity and behavioural analysis. We characterised, with synaptic-resolution, the pathways from the higher-order learning circuit (the mushroom body, MB) all the way to a specific cord command neuron (Goro) in the nerve cord that promotes the most vigorous escape response, rolling. Rolling is the fastest, but also the most energetically costly escape response and is activated by multisensory cues in the context of predator attack. We identify a pair of brain descending neurons, Ipsigoro, that integrate learnt information (via input from MBONs) and nociceptive context (via input from ascending neurons) to facilitate rolling via direct inputs to Goro. Our study reveals the circuit mechanism by which context and learnt information are integrated by brain descending neurons to activate specific nerve cord command neurons and promote specific actions.

neuroscience↗

bioRxiv2024

Statistical signature of subtle behavioural changes inlarge-scale behavioural assays

The central nervous system can generate various behaviours, including motor responses, which we can observe through video recordings. Recent advancements in genetics, automated behavioural acquisition at scale, and machine learning enable us to link behaviours to their underlying neural mechanisms causally. Moreover, in some animals, such as the Drosophila larva, this mapping is possible at unprecedented scales of millions of animals and single neurons, allowing us to identify the neural circuits generating particular behaviours. These high-throughput screening efforts are invaluable, linking the activation or suppression of specific neurons to behavioural patterns in millions of animals. This provides a rich dataset to explore how diverse nervous system responses can be to the same stimuli. However, challenges remain in identifying subtle behaviours from these large datasets, including immediate and delayed responses to neural activation or suppression, and understanding these behaviours on a large scale. We introduce several statistically robust methods for analyzing behavioural data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioural spaces aimed at detecting subtle deviations in behaviour. 3) A generative model for larval behavioural sequences, providing a benchmark for identifying complex behavioural changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioural screen focused on responses to an air puff, analyzing data from 280,716 larvae across 568 genetic lines. Author SummaryThere is a significant gap in understanding between the architecture of neural circuits and the mechanisms of action selection and behaviour generation.Drosophila larvae have emerged as an ideal platform for simultaneously probing behaviour and the underlying neuronal computation [1]. Modern genetic tools allow efficient activation or silencing of individual and small groups of neurons. Combining these techniques with standardized stimuli over thousands of individuals makes it possible to relate neurons to behaviour causally. However, extracting these relationships from massive and noisy recordings requires the development of new statistically robust approaches. We introduce a suite of statistical methods that utilize individual behavioural data and the overarching structure of the behavioural screen to deduce subtle behavioural changes from raw data. Given our studys extensive number of larvae, addressing and preempting potential challenges in body shape recognition is critical for enhancing behaviour detection. To this end, we have adopted a physics-informed inference model. Our first group of techniques enables robust statistical analysis within a learned continuous behaviour latent space, facilitating the detection of subtle behavioural shifts relative to reference genetic lines. A second array of methods probes for subtle variations in action sequences by comparing them to a bespoke generative model. Together, these strategies have enabled us to construct representations of behavioural patterns specific to a lineage and identify a roster of "hit" neurons with the potential to influence behaviour subtly.

neuroscience↗

bioRxiv2024

LarvaTagger: Manual and automatic tagging of Drosophila larval behaviour

MotivationAs more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data. We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth. ResultsWe reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data. AvailabilityAll the code is free and open source. Docker images are also available. See git-lab.pasteur.fr/nyx/LarvaTagger.jl.

bioinformatics↗