Dr. Romain Brasselet (Research Engineer Sorbonne University)
CNRS – INSERM – Sorbonne University
17, rue Moreau F75012 Paris, France email:

Research statement: As a mathematical neuroscientist, I am in general interested in applying statistical and mathematical methods to various fields of neuroscience and psychology (as well as other fields, opportunity pending). I nod thoughtfully at John Tukey’s famous quote: “the best thing about being a statistician is that you get to play in everyone’s backyard”. A former advisor of mine wrote that I try to teach that particular branch of statistics called HowPsychologyandNeuroscienceMistreatStatistics. He might be onto something. When I am not nurturing/torturing my colleagues with stats, I dedicate myself to various topics in neuroscience. I currently work on the cohort data recorded at the AVA. One of my primary goals is to try to find the underlying dimensionality of the manifold the data lives on, using cuttingedge dimensionality reduction techniques. This would provide insight the deeper structure of the data and allow me to carry further statistical techniques to understand how the various measurements interact with each other. I am also, among others, interested in spatiotemporal neural coding, or how populations of neurons manage to encode temporallyresolved signals. More precisely, I try to understand how the mathematical relations betweens neural signals can map relations between external stimulations, and how these relations are altered or preserved as the signals are hierarchically processed. I also study complex networks. On a technical side, I make an extensive use of information theory and machine learning techniques. I give lectures on these topics as well as on statistical inference.
Shortbio: After a degree in Theoretical Physics from University AixMarseille 2, I obtained a PhD in Computational Neuroscience from Université Pierre et Marie Curie (under the supervision of Angelo Arleo). I proceeded with postdocs at the MaxPlanckInstitute for Biological Cybernetics in Tübingen (with Christoph Kayser and Stefano Panzeri), and Universitat Pompeu Fabra in Barcelona (with Gustavo Deco). I was then awarded the Excellence Grant at SISSA (working with Mathew Diamond, Alessandro Treves, Valentina Parma and others) and continued working there in the Language, Learning and Reading Lab (with Davide Crepaldi) and the Time Perception Lab (with Domenica Bueti). I joined the Aging in Vision and Action team in January 2022. This is the least tortuous and convoluted way I found to get back to the starting place.
Keywords: statistics, information theory, mixedeffect models, modeling, neural coding, complex networks.
CV and Publication List (pdf): [ download ]
Publications
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2021
 Dynamics of historydependent perceptual judgment. Nature Communications, 12(1):115. (2021)
2020
 TDP43 realtime quaking induced conversion reaction optimization and detection of seeding activity in CSF of amyotrophic lateral sclerosis and frontotemporal dementia patients. Brain Communications, 2(2):fcaa142. (2020)
2019
 Sizing complex networks. Communications Physics, 2(1):110. (2019)
2018
 The capacity for correlated semantic memories in the cortex. Entropy, 20(11):824. (2018)
 Category structure and categorical perception jointly explained by similaritybased information theory. Entropy, 20(7):527. (2018)
2017
 The origin of human handedness and its role in prebirth motor control. Scientific Reports, 7(1):19. (2017)
2013
 Integration of sensory quanta in cuneate nucleus neurons in vivo. PloS One, 8(2):e56630. (2013)
2012
 Neurons with stereotyped and rapid responses provide a reference frame for relative temporal coding in primate auditory cortex. Journal of Neuroscience, 32(9):29983008. (2012)
2011
 Quantifying neurotransmission reliability through metricsbased information analysis. Neural Computation, 23(4):852881. (2011)
 Encoding/decoding of first and second order tactile afferents in a neurorobotic application. Journal of Physiology P, 105 (13):2535. (2011)
 Neural coding in the early stages of the somatosensory pathway: a metrical information theory analysis of human microneurography data. In Proceedings of the 10th French Society for Neuroscience Meeting, Marseille, France. (2011)
 Isometric coding of spiking haptic signals by peripheral somatosensory neurons. In Cabestany, J. et al., editors, LNCS  Advances on Computational Intelligence, vol. 6691, pages 528536, Springer. (2011)
2010
 Neural coding in the ascending somatosensory pathway: a metrical information theory pproach. Ph.D. Thesis, University Pierre and Marie Curie Paris 6. (2010)
 Quantifying neurotransmission through an entropy measure embedding spike train metrics. In Workshop on Shapes of Brain Dynamics, Paris, France. (2010)
 Quantifying neurotransmission through an entropy measure embedding spike train metrics. In Workshop on Dendrites, Neurones and Networks, Warwick, UK. (2010)
 Local metrical information: application to the perceptual magnet effect. In Gervais, R. et al., editors, Proceedings of the Fifth french conference on computational neuroscience (Neurocomp), pages 132136. (2010)
 Effective encoding/decoding of spiking signals from an artificial touch sensor. In Gervais, R. et al., editors, Proceedings of the Fifth french conference on computational neuroscience (Neurocomp), pages 120125. (2010)
 Neuromimetic encoding/decoding of spatiotemporal spiking from an artificial touch sensor. In Proceedings of the International Joint Conference on Neural Networks (IJCNN) (in press). (2010)
 Quantifying neurotransmission by taking into account the metrical properties of spike trains. In SIMAI  Symposium on new trends in scientific computing: computational Biology, Abs. N. 628, Cagliari, Italy. (2010)
 Quantifying neurotransmission through an entropy measure embedding spike train metrics. In Workshop on Spike Train Measures and Their Applications to Neural Coding, Plymouth, UK. (2010)
2009
 Optimal context separation of spiking haptic signals by secondorder somatosensory neurons. Advances in Neural Information Processing Systems, 22. (2009)
 Optimal context separation of spiking haptic signals by secondorder somatosensory neurons. In Bengio, Y. et al., editors, Advances in Neural Information Processing Systems 22, vol. 22, pages 180188. (2009)
 Fast encoding/decoding of haptic microneurography data based on first spike latencies. In Renaud, S. and Saighi, S., editors, Proceedings of the Fourth french conference on computational neuroscience (Neurocomp), vol. 4, pages 5. (2009)
 Fast encoding/decoding of haptic microneurography data based on first spike latencies. In BMC Neuroscience  Eighteenth Annual Computational Neuroscience Meeting, vol. 10 (Suppl1), pages 349. (2009)
 Fast encoding/decoding of haptic microneurography data based on firstspike latency. In Proceedings of the Humanoids Conference, Paris, France. (2009)