Dr. Alexis Dubreuil (Postdoc Sorbonne University)
Aging in Vision and Action Lab
CNRS – INSERM – Sorbonne University
17, rue Moreau F-75012 Paris, France
Research statement: I use tools from statistical physics and machine learning to understand how computing abilities emerge from the interactions of recurrently connected networks of neurons. This understanding leads to the production of mathematical models that allow, in collaboration with experimental neuroscientists to study the functioning of biological neural networks.
Short-bio: In 2011 I have received the master « theoretical physics of complex systems » from ENS Cachan. I then worked between Paris (Neurophysics lab) and Chicago (Neurobiology department) as a neuroscience PhD student. From 2014 to 2017 I worked as a post-doctoral fellow in the Laboratory of Theoretical Physics of ENS, and from 2018 to 2020 in the cognitive science department of ENS.
Keywords: theoretical neuroscience – machine learning – recurrent neural networks – cortical processing.
CV and Publication List (pdf): [ download ]
- Dynamics of random recurrent networks with correlated low-rank structure. Phys. Rev. Research, 2:013111. (2020)
- Shaping dynamics with multiple populations in low-rank recurrent networks. arXiv, 2007.02062:q-bio.NC. (2020)
- The interplay between randomness and structure during learning in RNNs. NeurIPS. (2020)
- Complementary roles of dimensionality and population structure in neural computations. bioRxiv. (2020)
- Short term memory properties of sensory neural architectures. Journal of Computational Neuroscience, 46(3):321?332. (2019)
- Can grid cell ensembles represent multiple spaces?. Neural Computation, 31(12):2324-2347. (2019)
- Sensorimotor computation underlying phototaxis in zebrafish. Nature Communications, 8(1). (2017)
- Storing structured sparse memories in a multi-modular cortical network model. Journal of Computational Neuroscience, 40(2):157?175. (2016)
- Rheotaxis of larval zebrafish: behavioral study of a multi-sensory process. Frontiers in Systems Neuroscience, 10:14. (2016)
- Memory capacity of networks with stochastic binary synapses. PLoS computational biology, 10(8):e1003727. (2014)