Stéphane Deny

Neuroscience & AI

318 Campus Drive, office S245, Stanford, CA

Welcome!

I am a postdoc working at the interface of neuroscience and artificial intelligence, in the lab of Surya Ganguli at Stanford University. To understand the inner workings of the brain, I apply tools from mathematics, physics and machine learning. In particular, I study visual representations in the retina and the brain and show how they can be derived from first principles, such as optimizing the use of limited neural or energy resources. Moreover, I study how similar representations can emerge in artificial neural networks trained with adequate objectives and constraints. Inspired by biology, I also work on the design of compact and energy-efficient neural networks for perception.

I also applied my expertise in machine learning and data science as a consultant for two French startups, BuilData and Spinergie.

I am also interested in pedagogy, teaching and mentoring. During summer, I am a teaching assistant for the Methods in Computational Neuroscience course at the Marine Biology Lab (Woods Hole, MA), where I co-created a Deep Learning tutorial for Neuroscientists.


News

Brief CV (full CV)

Now - Postdoc in the department of Applied Physics at Stanford University, with Surya Ganguli.

2017 - PhD in computational neuroscience at Paris VI University and the Vision Institute, advised by Serge Picaud and Olivier Marre.

2012 - MS in computer science from Supelec (France).

2009 - BS in mathematics and physics (MPSI/MP) from Lycée Hoche, Versailles (France).

Featured Publications

A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs

Jack Lindsey*, Samuel A. Ocko*, Surya Ganguli, Stéphane Deny

International Conference on Learning Representations (ICLR 2019) / Code / PDF

Visual representations differ drastically between the retina and primary visual cortex, and retinal representations also differ across species. We reproduce these biological properties by varying neural resource constraints in a deep convolutional model of the visual system. more...

The emergence of multiple retinal cell types through efficient coding of natural movies

Samuel A. Ocko*, Jack Lindsey*, Surya Ganguli, Stéphane Deny

Advances in Neural Information Processing Systems (NeurIPS 2018) / Code / PDF

Why are there so many parallel pathways at the output of the retina? Convolutional autoencoders, that optimally encode natural movies with low firing rates, use exactly the same channels that primates use in their retina. more...

Multiplexed computations in retinal ganglion cells of a single type

Stéphane Deny, Ulisse Ferrari, Emilie Macé, Pierre Yger, Romain Caplette, Serge Picaud, Gašper Tkačik & Olivier Marre

Nature Communications 2017 / PDF

By fitting convolutional neural neworks to retinal responses, we find that certain cell types multiplex linear and non-linear computations with the help of a sophisticated gain control mechanism.

Optogenetic vision restoration with high resolution

Ulisse Ferrari*, Stéphane Deny*, Abhishek Sengupta*, Romain Caplette, José-Alain Sahel, Deniz Dalkara, Serge Picaud, Jens Duebel, Olivier Marre

BiorXiv 2018 / PDF

Using Bayesian methods, we estimate an upper bound on the acuity that blind people could recover from optogenetic therapy.

Learning stable representations in a changing world with on-line t-SNE: proof of concept in the songbird

Stéphane Deny*, Emily Mackevicius*, Tatsuo Okubo, Gordon Berman, Joshua Shaevitz, Michale Fee

International Conference on Learning Representations Workshop Track (ICLR 2016) / PDF

By adapting t-SNE to streaming time series, we track the evolution of syllables in the developing songbird.

All Publications


A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs

Lindsey J. *, Ocko S. *, Ganguli S., Deny S. International Conference on Learning Representations (ICLR 2019) / Code / PDF


The emergence of multiple retinal cell types through efficient coding of natural movies

Ocko S. *, Lindsey J. *, Ganguli S., Deny S. Advances in Neural Information Processing Systems (NeurIPS 2018) / Code / PDF


Optogenetic vision restoration with high resolution

Ulisse Ferrari*, Deny S. *, Sengupta A.*, Dalkara D., Duebel J., Marre O. BiorXiv 2018 / PDF


Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons

Ferrari U., Deny S., Chalk M., Tkačik G., Marre O., Mora T. Physical Review E 2018 / PDF


A simple model for low variability in neural spike trains

Ferrari U., Deny S., Marre O., Mora T. Neural Computation 2018 / PDF


Nonlinear decoding of a complex movie from the mammalian retina

Botella-Soler V., Deny S., Marre O., Tkacik G. PLoS Computational Biology 2018 / PDF


A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo

Yger P., Spampinato G.L.B, Esposito E., Lefebvre B., Deny S., Gardella C., Stimberg M., Jetter F., Zeck G., Picaud S., Duebel J., Marre O. eLife 2018 / PDF


Multiplexed computations in retinal ganglion cells of a single type

Deny S., Ferrari U., Botella-Soller V., Caplette R., Yger P., Tkacik G., Marre O. Nature Communications 2017 / PDF


Learning stable representations in a changing world with on-line t-SNE: proof of concept in the songbird

Deny S.*, Mackevicius E.*, Okubo T., Berman G., Shaevitz J., Fee M. International Conference on Learning Representations Workshop Track (ICLR 2016) / PDF


Dynamical criticality in the collective activity of a population of retinal neurons

Mora T., Deny S., Marre O. Physical Review Letters 2015 / PDF


*equal contribution.

Theses


Local and Non-local Processing in the Retina (PhD thesis)

Deny S. Paris-Sorbonne University VI (2017) / PDF


Surprise Decoding in the Retinal Activity (MS neuroscience thesis)

Deny S. Pierre and Marie Curie University (2013) / PDF


Implementation of a bio-inspired recurrent convolutional neural network for image recognition and threat detection (MS computer science thesis, in french)

Deny S. Ecole Superieure d'Electricite (Supelec) (2012) / PDF