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Auditory system dynamics and multisensory processing

BRICE BATHELLIER

AUDITORY SYSTEM DYNAMICS AND MULTISENSORY PROCESSING

 

The team of Brice Bathellier studies the computational principles of the processing of auditory and multisensory information.

It does so by using a combination of advanced analysis and modeling techniques and a large array of experimental approaches, including two-photon calcium imaging, multichannel electrophysiology, optogenetics and behavioral analyses of auditory perception. The principal projects of the team include the large-scale decoding of sound representation in the murine auditory system, the development of optogenetic methods for generating auditory perception through the targeted activation of central neuronal networks, and exploration of the role in perception of the neuronal connections between areas of the brain processing information from different senses.

MEMBERS OF THE TEAM

Sophie BAGUR

Sophie BAGUR

Postdoc
Brice BATHELLIER

Brice BATHELLIER

Principal investigator
Jacques BOURG

Jacques BOURG

Postdoc
Etienne GOSSELIN

Etienne GOSSELIN

PhD Student
Evan HARREL

Evan HARREL

Postdoc
Sara JAMALI

Sara JAMALI

PhD student
Anthony RENARD

Anthony RENARD

PhD student
Joanna SCHWENKGRUP

Joanna SCHWENKGRUP

Postdoc
Antonin VERDIER

Antonin VERDIER

Engineer
Allan MULLER

Allan MULLER

Master Student

MOST IMPORTANT PUBLICATIONS

 

Ceballo S., Piwkowska Z., Bourg J., Daret A., Bathellier B., Targeted Cortical Manipulation of Auditory Perception, Neuron, 2019,104:1168-1179

Ceballo, S., Bourg, J., Kempf, A., Piwkowska, Z., Daret, A., Pinson, P., Deneux, T., Rumpel, S., and Bathellier, B. (2019). Cortical recruitment determines learning dynamics and strategy. Nat Commun 10, 1479.

Deneux, T., Kempf, A., Daret, A., Ponsot, E., and Bathellier, B. (2016). Temporal asymmetries in auditory coding and perception reflect multi-layered nonlinearities. Nat Commun 7, 12682.

Bathellier, B., Tee, S.P., Hrovat, C., and Rumpel, S. (2013). A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice. Proc Natl Acad Sci U S A 110, 19950-19955.

Bathellier, B., Ushakova, L., and Rumpel, S. (2012). Discrete neocortical dynamics predict behavioral categorization of sounds. Neuron 76, 435-449.

 

PROJECTS

Operations structuring auditory perception

The success of deep learning networks in the execution of complex perception tasks, such as the recognition of images or words, has highlighted the importance of non-linear operations for the construction of invariant representations of pertinent objects and signals. One of the projects currently being conducted by this team aims to make use of the power of two-photon imaging to identify the precise the non-linearities implemented in the auditory system. Using a combination of this approach and behavioral tasks, the researchers are trying to identify the key operations in the development of sound perception. For example, they have recently shown, in the auditory cortex of the mouse, that non-linear operations allow the construction of divergent representations of opposite variations of sound intensity, consistent with the divergent perception of these directions of variation in humans. 

 

Manipulation of the neuronal representations of sounds

Over and above the deciphering of models of neuronal activity in response to auditory stimulation, the establishment of causal links between these models and perception is a major challenge. The researchers of the team are trying to achieve this objective, using methods of light formatting to generate models of cortical activity, and to determine whether these artificial “auditory” stimuli can trigger behaviors or interfere with perceptual decisions. 

 

Reinforcement learning models for sensory discrimination tasks

Sensory discrimination tasks are essential for studies of how animals perceive external stimuli. However, surprisingly, each mouse learns each type of task at its own rate and with its own dynamics. The team of Brice Bathellier has developed reinforcement learning models inspired by biology, to describe the changes in the synapses transmitting auditory information to decision centers during the association of a sound with a behavioral decision.

The use of these models to interpret the learning of an association between a sound and a particular behavior will enable us to understand the causes of interindividual variability in learning, and to determine which characteristics of auditory, or, more generally, sensory representations are important for accelerating the acquisition of learning. 

 

Multisensory interactions in the cortex

 The cortex is a vast network of largely interconnected zones, and the role of this recurrent architecture is a fundamental issue for our understanding of sensory perception. Recent studies have shown that the cortical zones dedicated to hearing and vision are highly connected. Researchers have begun to characterize, with a high degree of precision, the information transmitted by this connection and its impact on visual processing. They have shown, for example, that the impact of this connection may be negative or positive, depending on the sensory context: negative in the dark, because vision cannot provide an explanation for sonic information in the absence of light, and positive when visual information is available. The researchers of the team are also exploring the way in which tactile and olfactory information is combined in the cortical circuits to refine and stabilize object recognition. They are also studying the impact of these two senses on auditory perception.

 

PUBLICATIONS BY MEMBERS OF THE TEAM

Bondanelli G, Deneux T., Bathellier B., Ostojic S., Network dynamics underlying OFF responses in the auditory cortex, Elife, 2021, (Accepted)

Schwenkgrub J., Harrell ER, Bathellier B.*, Bouvier J.*, Deep imaging in the brainstem reveals functional heterogeneity in V2a neurons controlling locomotion, Science Advances, 2020, 6:eabc6309

Harrell ER., Goldin MA., Bathellier B., Shulz DE., An elaborate sweep-stick code in rat barrel cortex, Science Advances, 2020, 6:eabb7189

Ceballo S., Piwkowska Z., Bourg J., Daret A., Bathellier B., Targeted Cortical Manipulation of Auditory Perception, Neuron, 2019,104:1168-1179

Ceballo, S., Bourg, J., Kempf, A., Piwkowska, Z., Daret, A., Pinson, P., Deneux, T., Rumpel, S., and Bathellier, B. (2019). Cortical recruitment determines learning dynamics and strategy. Nat Commun 10, 1479.

Deneux, T., Harrell, E.R., Kempf, A., Ceballo, S., Filipchuk, A., and Bathellier, B. (2019). Context-dependent signaling of coincident auditory and visual events in primary visual cortex. Elife 8.

Kuchibhotla, K., and Bathellier, B. (2018). Neural encoding of sensory and behavioral complexity in the auditory cortex. Curr Opin Neurobiol 52, 65-71.

Roland, B., Deneux, T., Franks, K.M., Bathellier, B., and Fleischmann, A. (2017). Odor identity coding by distributed ensembles of neurons in the mouse olfactory cortex. Elife 6.

Deneux, T., Kempf, A., Daret, A., Ponsot, E., and Bathellier, B. (2016). Temporal asymmetries in auditory coding and perception reflect multi-layered nonlinearities. Nat Commun 7, 12682.

Fregnac, Y., and Bathellier, B. (2015). Cortical Correlates of Low-Level Perception: From Neural Circuits to Percepts. Neuron 88, 110-126.

Naud, R., Bathellier, B., and Gerstner, W. (2014). Spike-timing prediction in cortical neurons with active dendrites. Front Comput Neurosci 8, 90.

Bathellier, B., Tee, S.P., Hrovat, C., and Rumpel, S. (2013). A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice. Proc Natl Acad Sci U S A 110, 19950-19955.

Peter, M., Bathellier, B., Fontinha, B., Pliota, P., Haubensak, W., and Rumpel, S. (2013). Transgenic mouse models enabling photolabeling of individual neurons in vivo. PLoS One 8, e62132.

Moczulska, K.E., Tinter-Thiede, J., Peter, M., Ushakova, L., Wernle, T., Bathellier, B., and Rumpel, S. (2013). Dynamics of dendritic spines in the mouse auditory cortex during memory formation and memory recall. Proc Natl Acad Sci U S A 110, 18315-18320.

Bathellier, B., Ushakova, L., and Rumpel, S. (2012). Discrete neocortical dynamics predict behavioral categorization of sounds. Neuron 76, 435-449.

Bathellier, B., Steinmann, T., Barth, F.G., and Casas, J. (2012). Air motion sensing hairs of arthropods detect high frequencies at near-maximal mechanical efficiency. J R Soc Interface 9, 1131-1143.

Bathellier, B., Gschwend, O., and Carleton, A. (2010). Temporal Coding in Olfaction. In The Neurobiology of Olfaction, A. Menini, ed. (Boca Raton (FL)).

Bathellier, B., Margrie, T.W., and Larkum, M.E. (2009). Properties of piriform cortex pyramidal cell dendrites: implications for olfactory circuit design. J Neurosci 29, 12641-12652.

Bathellier, B., Buhl, D.L., Accolla, R., and Carleton, A. (2008). Dynamic ensemble odor coding in the mammalian olfactory bulb: sensory information at different timescales. Neuron 57, 586-598.

Bathellier, B., Carleton, A., and Gerstner, W. (2008). Gamma oscillations in a nonlinear regime: a minimal model approach using heterogeneous integrate-and-fire networks. Neural Comput 20, 2973-3002.

Lagier, S., Panzanelli, P., Russo, R.E., Nissant, A., Bathellier, B., Sassoe-Pognetto, M., Fritschy, J.M., and Lledo, P.M. (2007). GABAergic inhibition at dendrodendritic synapses tunes gamma oscillations in the olfactory bulb. Proc Natl Acad Sci U S A 104, 7259-7264.

Accolla, R., Bathellier, B., Petersen, C.C., and Carleton, A. (2007). Differential spatial representation of taste modalities in the rat gustatory cortex. J Neurosci 27, 1396-1404.

Bathellier, B., Van De Ville, D., Blu, T., Unser, M., and Carleton, A. (2007). Wavelet-based multi-resolution statistics for optical imaging signals: Application to automated detection of odour activated glomeruli in the mouse olfactory bulb. Neuroimage 34, 1020-1035.

Bathellier, B., Lagier, S., Faure, P., and Lledo, P.M. (2006). Circuit properties generating gamma oscillations in a network model of the olfactory bulb. J Neurophysiol 95, 2678-2691.

Bathellier, B., Barth, F.G., Albert, J.T., and Humphrey, J.A. (2005). Viscosity-mediated motion coupling between pairs of trichobothria on the leg of the spider Cupiennius salei. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 191, 733-746.