Ulysse Rançon

Ulysse Rançon

Postdoc in computational neuroscience at Sinz Lab

Ulysse Rancon - Homepage

I am a postdoctoral researcher in computational neuroscience at the Sinz Lab, University of Göttingen, Germany. My research is interdisciplinary and at the frontiers between neuroscience, deep learning, computer science and neuromorphic computing. I like to take high-level descriptive/quantitative approaches that fit real data well (as deep neural networks usually do), and analyze the latter rather than the other way around.

Background :zap:

Originally from an electrical engineering and signal processing background, I have always been fascinated by the brain, among other complex systems. After a very fruitful experience in an academic research lab at the University of Tokyo which made me discover neuromorphic electronics, I honed my deep learning skills in the industry at Bull. From my Master’s thesis on event-based sensors and AI at the Brain and Cognition Research Center (CERCO) in Toulouse, I went on to do a PhD in computational neuroscience there, which I completed in 2025. During my PhD, I used deep neural networks trained with backpropagation as models of sensory neurons, predicting single-unit responses to natural sounds and videos in the auditory and visual systems.

Current Work and Projects :microscope:

As a postdoc in the Sinz Lab, I work on active vision: how the visual system processes the world when the animal itself is moving and shaping its own sensory input. My main focus is building deep predictive models of primary visual cortex (V1) in freely moving mice, where neural responses depend not only on the visual stimulus but also on the behavior of the animal (eye, head and body movements, locomotion, …). Using these models as in-silico replacements of real neurons, I study visuomotor tuning, i.e. how V1 neurons jointly encode visual and motor/behavioral signals, and what this tells us about vision in natural conditions.

Ethics / Animal Experimentation Statement :octopus:

I cannot thank enough the electrophysiologists that share their precious and extremely expensive data to the community on public repositories. When we think about it, everybody is winning: by doing so, they give precious work material to theorists who in turn cite them back. Most importantly, new animals don’t get to die “unnecessarily” to get some data that has been already acquired but never shared. Contrary to what one would think, lots of neurophysiological data is readily available on websites such as the excellent CRCNS. So let’s wonder what quantities are sleeping in local hard drives in labs around the globe. In this current era of deep learning and data-driven approaches, I am certain that great progress in neuroscience is yet to come, supposing the community shares its fruits and normalizes its practices. Let’s do it for the sake of science, or at least that of our animal friends, without which any of that would not be possible.

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Future Research Interests :mag:

As passionate as I may be about the subject of my PhD thesis, I have many other ideas inside my bucket list! Here are a few:

  • a data-driven deep spike sorter
  • towards BCIs (closed loop electrophysiology)
  • a compact AI based model of C. Elegans and Drosophiliae (both have a completely mapped connectome now)
  • …

And in quite distant fields:

Other interests :jp:

Interculturality is as important in my life outside the lab than interdisciplinarity is for my research. This is why I am always very curious about other cultures, with a bias towards Asia so far. I can’t help but love far-East Asia’s aesthetics, cuisine, and teas ! Aside from traveling, languages are a great and fun way to dive into other countries, and they never fail to fascinate me.