Functional diversity of hippocampal fast oscillations for memory processing
The hippocampal brain region is essential for episodic memory, which relies on the coordinated
discharge of cell assemblies within transient events termed Sharp Wave Ripples (SPW-Rs). The
electrographic signature of SPW-Rs is considered evidence for the involvement of specific neural
subcircuits in memory formation. We recently made the experimental observation that sharp wave
ripples can present distinct profiles, suggesting the involvement of previously overlooked subcircuits
in their generation. We hypothesize that there are several classes of SPW-Rs, associating the discharge
of distinct neuronal networks and, therefore distinct functional processing. In order to evaluate this
hypothesis, we propose to develop signal processing techniques based on recent machine learning
advances (attentional networks and non-linear dimensionality reduction) to comprehend the diversity
of SPW-Rs in terms of electrographic signature and participating neural circuits.
memory, neural processing, signal analysis, machine learning, classifiers
This project will consist of the development of a classification framework based on attentional
networks for SPW-Rs. Our goal is to challenge and extend our current knowledge about Sharp Wave
Ripples, a hallmark of hippocampal neural circuit processing. Contrary to the canonical view, we have
observed CA1 ripples with a diversity of electrographic signatures, suggesting that distinct categories
of ripples may be related to the local integrative processing of distinct sets of inputs. Thanks to stateof-
the-art analysis techniques, we expect to demonstrate the functional importance of the yet
overlooked diversity of SPW-Rs.
Proposed approach (experimental / theoretical / computational)
This project combines computational and experimental approaches. Simultaneous recordings from
Entorhinal Cortex, CA3 and CA1 hippocampal regions performed in adult mice during rest and wheel
running in the head-fixed configuration (multisite silicon probes and spike sorting analysis to reach
single cell resolution) will be analyzed using machine learning techniques, such as variational
autoencoders (VAEs) with transformers networks, to classify experimentally recorded CA1 SPW-Rs
from their electrographic signature. The cell assemblies and sequences associated with CA1 SPW-Rs
will then be analyzed using non-linear dimensionality reduction techniques such as UMAP to identify
distinct cell assemblies preferentially associated with distinct SPW-R profiles, suggesting functional
segregation of hippocampal processing during SPW-Rs.
This project is highly interdisciplinary because it combines the conceptual and experimental expertise
of X. Leinekugel in neuroscience with the theoretical and computational expertise in neural networks
and information processing of Hervé Rouault. The recent development of computational approaches,
sustained by the rapid development of computing power, is now allowing to address fundamental
questions in the field of neuroscience. In this project, we propose to take advantage of both the power
of machine learning classifier algorithms and of non-linear dimensionality reduction techniques to
address the question of neural processing underlying episodic memory in the mammalian brain.
The PhD student is expected to have a strong background in physics and/or computational approaches
(signal analysis, machine learning), and to be strongly motivated in addressing fundamental questions
in the field of neuroscience. Previous experience with experimental electrophysiological recordings
can be appreciated but is not required.
Is this project the continuation of an existing project or an entirely new one? In the case of an existing project, please explain the links between the two projects
This project is an entirely new project, although based on experimental observations made in former
2 to 5 references related to the project
1. Buzsáki, G. (2015). Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning. Hippocampus 25, 1073-1188.
2. Gedankien, T., Gotman, J., et al. (2022). A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations. Nat Commun 13, 6000.
3. Girardeau, G., Benchenane, K., Wiener, S.I., Buzsaki, G., and Zugaro, M.B. (2009). Selective suppression of hippocampal ripples impairs spatial memory. Nature Neuroscience 12, 1222-1223.
4. Valeeva, G., Janackova, S., Nasretdinov, A., Rychkova, V., Makarov, R., Holmes, G.L., Khazipov, R., and Lenck-Santini, P.-P. (2018). Emergence of Coordinated Activity in the Developing Entorhinal–Hippocampal Network. Cerebral Cortex 29, 906-920.
5. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I.
(2017). Attention is all you need. Advances in neural information processing systems, 30.
3 main publications from each PI over the last 5 years
1. Dubanet, O., Ferreira Gomes Da Silva, A., Frick, A., Hirase, H., Beyeler, A., and Leinekugel, X. (2021). Probing the polarity of spontaneous perisomatic GABAergic synaptic transmission in the mouse CA3 circuit in vivo. Cell reports 36.
2. Carreno-Munoz, M.I., Martins, F., Medrano, M.C., Aloisi, E., Pietropaolo, S., Dechaud, C., Subashi, E., Bony, G., Ginger, M., Moujahid, A., et al. (2018). Potential Involvement of Impaired BKCa Channel Function in Sensory Defensiveness and Some Behavioral Disturbances Induced by Unfamiliar Environment in a Mouse Model of Fragile X Syndrome. Neuropsychopharmacology 43, 492-502.
3. Carreño-Muñoz, M.I., Medrano, M.C., Ferreira Gomes Da Silva, A., Gestreau, C., Menuet, C., Leinekugel, T., Bompart, M., Martins, F., Subashi, E., Aby, F., et al. (2022). Detecting fine and elaborate movements with piezo sensors provides non-invasive access to overlooked behavioral components. Neuropsychopharmacology 47, 933-943.
1. Dard RF, Leprince E, Denis J, Rao Balappa S, Suchkov D, Boyce R, Lopez C, Giorgi-Kurz M, Szwagier T, Dumont T, Rouault H, Minlebaev M, Baude A, Cossart R, Picardo MA. (2022). The rapid developmental rise of somatic inhibition disengages hippocampal dynamics from self-motion. Elife 11:e78116
2. Kim SS, Rouault H, Druckmann S, Jayaraman V. (2017). Ring attractor dynamics in the Drosophila central brain. Science 356(6340):849-853
3. Corson F, Couturier L, Rouault H, Mazouni K, Schweisguth F. (2017). Self-organized Notch dynamics generate stereotyped sensory organ patterns in Drosophila. Science 356(6337):eaai7407