@article {68414, title = {High-throughput automated methods for classical and operant conditioning of larvae.}, journal = {eLife}, volume = {11}, year = {2022}, month = {2022 Oct 28}, abstract = {

Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The Drosophila melanogaster larva is a tractable model system for studying the circuit basis of behaviour, but many forms of associative learning have not yet been demonstrated in this animal. Here, we developed a high-throughput (i. e. multi-larva) training system that combines real-time behaviour detection of freely moving larvae with targeted opto- and thermogenetic stimulation of tracked animals. Both stimuli are controlled in either open- or closed-loop, and delivered with high temporal and spatial precision. Using this tracker, we show for the first time that Drosophila larvae can perform classical conditioning with no overlap between sensory stimuli (i. e. trace conditioning). We also demonstrate that larvae are capable of operant conditioning by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Our results extend the known associative learning capacities of Drosophila larvae. Our automated training rig will facilitate the study of many different forms of associative learning and the identification of the neural circuits that underpin them.

}, issn = {2050-084X}, doi = {10.7554/eLife.70015}, author = {Croteau-Chonka, Elise C and Clayton, Michael S and Venkatasubramanian, Lalanti and Harris, Samuel N and Jones, Benjamin M W and Narayan, Lakshmi and Winding, Michael and Masson, Jean-Baptiste and Zlatic, Marta and Klein, Kristina T} } @article {67793, title = {Circuits for integrating learned and innate valences in the insect brain.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Nov 10}, abstract = {

Animal behavior is shaped both by evolution and by individual experience. Parallel brain pathways encode innate and learned valences of cues, but the way in which they are integrated during action-selection is not well understood. We used electron microscopy to comprehensively map with synaptic resolution all neurons downstream of all Mushroom Body output neurons (encoding learned valences) and characterized their patterns of interaction with Lateral Horn neurons (encoding innate valences) in larva. The connectome revealed multiple types that receive convergent Mushroom Body and Lateral Horn inputs. A subset of these receives excitatory input from positive-valence MB and LH pathways and inhibitory input from negative-valence MB pathways. We confirmed functional connectivity from LH and MB pathways and behavioral roles of two of these neurons. These neurons encode integrated odor value and bidirectionally regulate turning. Based on this we speculate that learning could potentially skew the balance of excitation and inhibition onto these neurons and thereby modulate turning. Together, our study provides insights into the circuits that integrate learned and innate valences to modify behavior.

}, issn = {2050-084X}, doi = {10.7554/eLife.62567}, author = {Eschbach, Claire and Fushiki, Akira and Winding, Michael and Afonso, Bruno and Andrade, Ingrid V and Cocanougher, Benjamin T and Eichler, Katharina and Gepner, Ruben and Si, Guangwei and Valdes-Aleman, Javier and Fetter, Richard D and Gershow, Marc and Jefferis, Gregory Sxe and Samuel, Aravinthan Dt and Truman, James W and Cardona, Albert and Zlatic, Marta} } @article {65373, title = {Recurrent architecture for adaptive regulation of learning in the insect brain.}, journal = {Nature Neuroscience}, volume = {23}, year = {2020}, month = {2020 Mar 23}, pages = {544-55}, abstract = {

Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.

}, issn = {1546-1726}, doi = {10.1038/s41593-020-0607-9}, author = {Eschbach, Claire and Fushiki, Akira and Winding, Michael and Schneider-Mizell, Casey M and Shao, Mei and Arruda, Rebecca and Eichler, Katharina and Valdes-Aleman, Javier and Ohyama, Tomoko and Thum, Andreas S and Gerber, Bertram and Fetter, Richard D and Truman, James W and Litwin-Kumar, Ashok and Cardona, Albert and Zlatic, Marta and Cardona, Albert and Zlatic, Marta} } @article {64785, title = {Neural substrates of Drosophila larval anemotaxis.}, journal = {Current Biology : CB}, volume = {29}, year = {2019}, month = {2019 Jan 29}, pages = {554-66}, abstract = {

Animals use sensory information to move toward more favorable conditions. Drosophila larvae can move up or down gradients of odors (chemotax), light (phototax), and temperature (thermotax) by modulating the probability, direction, and size of turns based on sensory input. Whether larvae can anemotax in gradients of mechanosensory cues is unknown. Further, although many of the sensory neurons that mediate taxis have been described, the central circuits are not well understood. Here, we used high-throughput, quantitative behavioral assays to demonstrate Drosophila larvae anemotax in gradients of wind speeds and to characterize the behavioral strategies involved. We found that larvae modulate the probability, direction, and size of turns to move away from higher wind speeds. This suggests that similar central decision-making mechanisms underlie taxis in somatosensory and other sensory modalities. By silencing the activity of single or very few neuron types in a behavioral screen, we found two sensory (chordotonal and multidendritic class III) and six nerve cord neuron types involved in anemotaxis. We reconstructed the identified neurons in an electron microscopy volume that spans the entire larval nervous system and found they received direct input from the mechanosensory neurons or from each other. In this way, we identified local interneurons and first- and second-order subesophageal zone (SEZ) and brain projection neurons. Finally, silencing a dopaminergic brain neuron type impairs anemotaxis. These findings suggest that anemotaxis involves both nerve cord and brain circuits. The candidate neurons and circuitry identified in our study provide a basis for future detailed mechanistic understanding of the circuit principles of anemotaxis.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2019.01.009}, author = {Jovanic, Tihana and Winding, Michael and Cardona, Albert and Truman, James W and Gershow, Marc and Zlatic, Marta} }