Group leader: Claire Eschbach

Recurrent Circuits, Learning and Memory

In brief

The formation of a flexible memory is a key function for any individual immersed in a dynamic environment and needing to make choices. Indeed, it allows monitoring and predicting the consequences – positive or negative – resulting from such choices, which will also impact future choices. In many invertebrate and vertebrate animal species, the brain circuits used to assess these consequences are based on recurrent connections and neuromodulation, generally dopaminergic. Studying the detailed functioning of these circuits is essential to understanding how their dysfunction can lead to behavioural disorders such as addiction.
Unresolved questions remain about these computational mechanisms, e.g. on the one hand, the exact role of recurrent connections within the learning circuit and, on the other, the signalling pathways downstream of dopaminergic signals that determine plasticity. At a more integrative level, we also need to understand how localised plasticity impacts future choices. We are studying these questions in the associative circuitry of the larval brain of Drosophila melanogaster using a multi-scale approach combining behaviour, neurogenetics, mathematical modelling and in vivo imaging.

My research considers two main mechanisms:

  • On one hand, the role of recurrent connections in adjusting dopamine-gated plasticity. The hypotheses I focus on are based on reinforcement learning theories as well as on knowledge from the larval brain connectome (entirely recontructed and published in Winding et al., Science 2023). Using advanced neurogenetics approach, I transiently manipulate dopamine reinforcing neurons as well as the neurons projecting feedback onto dopamine neurons, while testing the effect of this manipulation on fine-tuning learned behaviors and on dopamine responses to unpredicted or predicted stimuli.
  • On the other hand, the role of different dopamine receptors and second messengers to implement synaptic plasticity over the course of training. Here, I base my hypotheses on previous findings on the involvement of distinct types of dopamine receptors expressed in the same memory cells in setting associative memories of different kinds. I plan to use RNA interference techniques to knock down specific receptors in specific neurons and set up in vivo imaging of 2nd messengers.

Drosophila larva uniquely permits a combination of sophisticated, multi-scale, approaches. With online tracking, specific genetic targeting of neurons (to e.g. activate, silence, or image neuronal activity), computational modelling of networks, and the use of a detailed connectome as a road map, we can study the way recurrent networks implement reinforcement learning to an unprecedented level of precision.

Grants
  • 2023-2024 Fondation Fyssen
  • 2024-2027 ATIP-Avenir

Team leader

Member

2021

• Claire Eschbach, Akira Fushiki, Michael Winding, Bruno Afonso, Ingrid Andrade, et al. (2021). Circuits for integrating learned and innate valences in the insect brain. eLife, 10, pp.10:e62567. ⟨10.7554/eLife.62567⟩

2020

• Claire Eschbach, Akira Fushiki, Michael Winding, Casey Schneider-Mizell, Mei Shao, et al. (2020). Recurrent architecture for adaptive regulation of learning in the insect brain. Nature Neuroscience, 23 (4), pp.544-555. ⟨10.1038/s41593-020-0607-9⟩
• Claire Eschbach, Marta Zlatic. (2020). Useful road maps: studying Drosophila larva’s central nervous system with the help of connectomics. Current Opinion in Neurobiology, 65, pp.129-137. ⟨10.1016/j.conb.2020.09.008⟩

2018

• Birgit Michels, Hanna Zwaka, Ruth Bartels, Oleh Lushchak, Katrin Franke, Thomas Endres, Markus Fendt, Inseon Song, May Bakr, Tuvshinjargal Budragchaa, Bernhard Westermann, Dushyant Mishra, Claire Eschbach, Stefanie Schreyer, Annika Lingnau, Caroline Vahl, Marike Hilker, Randolf Menzel, Thilo Kähne, Volkmar Leßmann, Alexander Dityatev , Ludger Wessjohann, Bertram Gerber (2018). Memory enhancement by ferulic acid ester across species. Science Advances, 4 (10), pp.eaat6994. ⟨10.1126/sciadv.aat6994⟩
• Timo Saumweber, Astrid Rohwedder, Michael Schleyer, Katharina Eichler, Yi-Chun Chen, Yoshinori Aso, Albert Cardona, Claire Eschbach, Oliver Kobler, Anne Voigt, Archana Durairaja, Nino Mancini, Marta Zlatic, James W Truman, Andreas S Thum, Bertram Gerber (2018). Functional architecture of reward learning in mushroom body extrinsic neurons of larval Drosophila. Nature Communications, 9 (1), pp.1104. ⟨10.1038/s41467-018-03130-1⟩

2017

• Maria J Almeida-Carvalho, Dimitri Berh, Andreas Braun, Yi-Chun Chen, Katharina Eichler, Claire Eschbach, Pauline M J Fritsch, Bertram Gerber, Nina Hoyer, Xiaoyi Jiang, Jörg Kleber, Christian Klämbt, Christian König, Matthieu Louis, Birgit Michels, Anton Miroschnikow, Christen Mirth, Daisuke Miura, Thomas Niewalda, Nils Otto, Emmanouil Paisios, Michael J Pankratz, Meike Petersen, Noel Ramsperger, Nadine Randel, Benjamin Risse, Timo Saumweber, Philipp Schlegel, Michael Schleyer, Peter Soba, Simon G Sprecher, Teiichi Tanimura, Andreas S Thum, Naoko Toshima, Jim W Truman, Ayse Yarali, Marta Zlatic (2017). The Ol1mpiad: concordance of behavioural faculties of stage 1 and stage 3 Drosophila larvae. Journal of Experimental Biology, 220 (13), pp.2452-2475. ⟨10.1242/jeb.156646⟩

• Katharina Eichler, Feng Li, Ashok Litwin-Kumar, Youngser Park, Ingrid Andrade, Casey M Schneider-Mizell, Timo Saumweber, Annina Huser, Claire Eschbach, Bertram Gerber, Richard D Fetter, James W Truman, Carey E Priebe, L F Abbott, Andreas S Thum, Marta Zlatic, Albert Cardona (2017). The complete connectome of a learning and memory centre in an insect brain. Nature, 548 (7666), pp.175-182. ⟨10.1038/nature23455⟩

• Birgit Michels, Timo Saumweber, Roland Biernacki, Jeanette Thum, Rupert D V Glasgow, Michael Schleyer, Yi-Chun Chen, Claire Eschbach, Reinhard F Stocker, Naoko Toshima, Teiichi Tanimura, Matthieu Louis, Gonzalo Arias-Gil, Manuela Marescotti, Fabio Benfenati, Bertram Gerber. (2017). Pavlovian Conditioning of Larval Drosophila: An Illustrated, Multilingual, Hands-On Manual for Odor-Taste Associative Learning in Maggots. Frontiers in Behavioral Neuroscience, 11, pp.45. ⟨10.3389/fnbeh.2017.00045⟩