Neural networks are characterized by dense connectivity at multiple scales that makes challenging to assess their organization and function. We develop machine learning and statistical tools to study the functional organization of these systems at multiple scales.
In the case of the mamalian brain, strong recurrent and bidirectional connectivity characterizes the circuits,leading to a complex dynamics where various modules cooperate at different levels to give rise, for example, to coherent behaviors and percepts. This complexity manifests itself as collective oscillations as well as more complex dymamical patterns such as Sharp-wave ripple complexes, that can be observed in electrical brain activity. These neural events are believed to play a key role in information processing, learning and behavior. Our research aims at designing better techniques to detect these events and understand their underlying mechanisms and computational role.
In order to address these questions, we put an emphasis on developing new Machine learning tools with strong theoretical foundations that are particularly well suited to capture the complexity of Biological signals. These include unsupervised learning algorithms to identify relevant patterns in large neural recording datasets [ ], non-parametric statistical tools (based on kernel methods) to identify the complex statistical dependencies of biological signals [ ], as well as causal inference methods to infer the underlying mechanisms generating the data [ ].
Importantly, our work also leads us to use models to investigate the principles underlying learning and plasticity in biological and artificial networks [ ].
In parallel, the current intensive development of new artificial deep neural networks has lead to impressive successes, but the functioning of these architectures remains largely elusive due to their high dimensional connectivity. This provides us an opportunity to use our network analysis tools to uncover fundamental principles for such systems, and possibly relate them to biology. We are currently investigating causality and invariance principles [ ] to understand the structure of deep generative models and in particular assess their modularity [ ].
Be it during wakefulness or sleep, our brains are able to implement the numerous functions key to our survival with an extraordinary reliability. This implies precise coordination of transient mechanisms at multiple spatio-temporal scales ensuring both the synergy between modules contributing to a same task, and the non-interference between network activities in charge of different functions. Such fine coordination seems at odds with the widespread and largely recurrent anatomical connectivity of the central nervous system, and with the seemingly random fluctuations observed in ongoing brain signals. We postulate that this paradox can be resolved by carefully telling apart the myriad neural activities routed dynamically through this network, each implementing a very specific function. The concurrent study of brain activity at multiple scales using simultaneous recording of action potentials, multi-site Local Field Potential (LFP), and functional Magnetic Resonance Imaging (fMRI) signals offers an exceptional opportunity to investigate the properties and functions of these neural events at a system level. This requires advanced data analysis techniques that fully capture the complexity of these signals including highly transient and non-linear phenomena.
Generative models encompass most unsupervised learning techniques that aim at building a probabilistic model from data. While the typical measure of success of such techniques is how well the distribution of the model fits the empirical data distribution, we propose that additional causal assumptions could enforce that characteristics of the model should reflect the data generating mechanism. Being able to capture properties of the true causal mechanism allows in principle better generalization and better interpretability of the models. We introduced a general group invariance framework to allow a quantitative assessment of this idea , which connects to several causal inference methods, and currently apply this approach to classical unsupervised learning methods such as clustering, as well as more recent techniques relying on deep generative models.
Besserve, M., Sun, R., Schölkopf, B.
Intrinsic disentanglement: an invariance view for deep generative models
Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML, July 2018 (conference)
Besserve, M., Shajarisales, N., Schölkopf, B., Janzing, D.
Group invariance principles for causal generative models
Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 84, pages: 557-565, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, 2018 (conference)
Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N. K., Besserve, M.
Generalized phase locking analysis of electrophysiology data
7th AREADNE Conference on Research in Encoding and Decoding of Neural Ensembles, 2018 (poster)
Kapoor, V., Besserve, M., Logothetis, N. K., Panagiotaropoulos, T. I.
Parallel processing of task phase and conscious content in functionally segregated prefrontal populations during a no report task of perceptual suppression
Communications Biology, 2018 (article) Accepted
Ramirez-Villegas, J. F., Willeke, K. F., Logothetis, N. K., Besserve, M.
Dissecting the synapse- and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples
Neuron, 2018 (article) Accepted
Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N. K., Besserve, M.
Generalized phase locking analysis of electrophysiology data
ESI Systems Neuroscience Conference (ESI-SyNC 2017): Principles of Structural and Functional Connectivity, 2017 (poster)
Logothetis, N. K., Murayama, Y., Ramirez-Villegas, J. F., Besserve, M., Evrard, H.
PGO wave-triggered functional MRI: mapping the networks underlying synaptic consolidation
47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)
Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.
Statistical source separation of rhythmic LFP patterns during sharp wave ripples in the macaque hippocampus
47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)
Besserve, M., Logothetis, N. K.
Hippocampal neural events predict ongoing brain-wide BOLD activity
47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)
Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.
Diversity of sharp wave-ripple LFP signatures reveals differentiated brain-wide dynamical events
Proceedings of the National Academy of Sciences U.S.A, 112(46):E6379-E6387, November 2015 (article)
Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.
Diversity of sharp wave-ripples in the CA1 of the macaque hippocampus and their brain wide signatures
45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), October 2015 (poster)
Besserve, M.
Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism
53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)
Besserve, M., Lowe, S. C., Logothetis, N. K., Schölkopf, B., Panzeri, S.
Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer
PLOS Biology, 13(9):e1002257, September 2015 (article)
Besserve, M.
Independence of cause and mechanism in brain networks
DALI workshop on Networks: Processes and Causality, April 2015 (talk)
Shajarisales, N., Janzing, D., Schölkopf, B., Besserve, M.
Telling cause from effect in deterministic linear dynamical systems
In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 285–294, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)
Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.
Dynamical source analysis of hippocampal sharp-wave ripple episodes
Bernstein Conference, 2014 (poster)
Besserve, M., Schölkopf, B., Logothetis, N. K.
Unsupervised identification of neural events in local field potentials
44th Annual Meeting of the Society for Neuroscience (Neuroscience), 2014 (talk)
Besserve, M.
Quantifying statistical dependency
Research Network on Learning Systems Summer School, 2014 (talk)
Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.
Cluster analysis of sharp-wave ripple field potential signatures in the macaque hippocampus
Computational and Systems Neuroscience Meeting (COSYNE), 2014 (poster)
Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.
Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI
Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience},
year = {2013},
month = {7},
volume = {14},
number = {Supplement 1},
pages = {A1}, (talk)
Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N., Besserve, M.
Coupling between spiking activity and beta band spatio-temporal patterns in the macaque PFC
43rd Annual Meeting of the Society for Neuroscience (Neuroscience), 2013 (poster)
Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N., Besserve, M.
Analyzing locking of spikes to spatio-temporal patterns in the macaque prefrontal cortex
Bernstein Conference, 2013 (poster)
Ramirez-Villegas, J., Logothetis, N., Besserve, M.
Characterization of different types of sharp-wave ripple signatures in the CA1 of the macaque hippocampus
4th German Neurophysiology PhD Meeting Networks, 2013 (poster)
Balduzzi, D., Ortega, P., Besserve, M.
Metabolic cost as an organizing principle for cooperative learning
Advances in Complex Systems, 16(02n03):1350012, 2013 (article)
Besserve, M., Logothetis, N., Schölkopf, B.
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators
In Advances in Neural Information Processing Systems 26, pages: 2535-2543, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)
Laurent, F., Valderrama, M., Besserve, M., Guillard, M., Lachaux, J., Martinerie, J., Florence, G.
Multimodal information improves the rapid detection of mental fatigue
Biomedical Signal Processing and Control, 8(4):400 - 408, 2013 (article)
Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.
Hippocampal-Cortical Interaction during Periods of Subcortical Silence
Nature, 491, pages: 547-553, November 2012 (article)
Besserve, M., Panagiotaropoulos, T., Crocker, B., Kapoor, V., Tolias, A., Panzeri, S., Logothetis, N.
Identifying endogenous rhythmic spatio-temporal patterns in micro-electrode array recordings
9th annual Computational and Systems Neuroscience meeting (Cosyne), 2012 (poster)
Besserve, M., Bartels, A., Murayama, Y., Logothetis, N.
Centrality of the Mammalian Functional Brain Network
42nd Annual Meeting of the Society for Neuroscience (Neuroscience), 2012 (poster)
Balduzzi, D., Besserve, M.
Towards a learning-theoretic analysis of spike-timing dependent plasticity
In Advances in Neural Information Processing Systems 25, pages: 2465-2473, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)
Panagiotaropoulos, T., Besserve, M., Logothetis, N.
Beta oscillations propagate as traveling waves in the macaque prefrontal cortex
42nd Annual Meeting of the Society for Neuroscience (Neuroscience), 2012 (talk)
Panagiotaropoulos, T., Besserve, M., Crocker, B., Kapoor, V., Tolias, A., Panzeri, S., Logothetis, N.
Spatiotemporal mapping of rhythmic activity in the inferior convexity of the macaque prefrontal cortex
41(239.15), 41st Annual Meeting of the Society for Neuroscience (Neuroscience), November 2011 (poster)
Besserve, M., Janzing, D., Logothetis, N., Schölkopf, B.
Finding dependencies between frequencies with the kernel cross-spectral density
In pages: 2080-2083 , IEEE, Piscataway, NJ, USA, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , May 2011 (inproceedings)
Besserve, M., Martinerie, J., Garnero, L.
Improving quantification of functional networks with EEG inverse problem: Evidence from a decoding point of view
NeuroImage, 55(4):1536-1547, April 2011 (article)
Chavez, M., Besserve, M., Le Van Quyen, M.
Dynamics of excitable neural networks with heterogeneous connectivity
Progress in Biophysics and Molecular Biology, 105(1-2):29-33, March 2011 (article)
Besserve, M., Martinerie, J.
Extraction of functional information from ongoing brain electrical activity: Extraction en temps-réel d’informations fonctionnelles à partir de l’activité électrique cérébrale
IRBM, 32(1):27-34, February 2011 (article)
Besserve, M., Schölkopf, B., Logothetis, N., Panzeri, S.
Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis
Journal of Computational Neuroscience, 29(3):547-566, December 2010 (article)
Besserve, M., Murayama, Y., Schölkopf, B., Logothetis, N., Panzeri, S.
High frequency phase-spike synchronization of extracellular signals modulates causal interactions in monkey primary visual cortex
40(616.2), 40th Annual Meeting of the Society for Neuroscience (Neuroscience), November 2010 (poster)
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