Dreaming up scale invariance via inverse renormalization group
Rançon, A., Rançon, U., Ivek, T., & Balog, I. (2026). "Dreaming up scale invariance via inverse renormalization group." Physical Review E, 113(5), 055302; doi: 10.1103/3njc-5tlx
CV
Education
- Ph.D in Neuroscience, University of Toulouse, 2025 (expected)
- French Engineering Degree in Electrical Engineering, INSA Lyon, 2021
Work experience
- Spring 2024: Academic Pages Collaborator
- Github University
- Duties includes: Updates and improvements to template
- Supervisor: The Users
- Fall 2015: Research Assistant
- Github University
- Duties included: Merging pull requests
- Supervisor: Professor Hub
- Summer 2015: Research Assistant
- Github University
- Duties included: Tagging issues
- Supervisor: Professor Git
Skills
- Skill 1
- Languages
- French (Native)
- English (Professional)
- German, Japanese, Spanish (Conversational)
- Korean (Beginner)
- Skill 3
Publications
Temporal recurrence as a general mechanism to explain neural responses in the auditory system
Rançon et al. (2025). "Temporal recurrence as a general mechanism to explain neural responses in the auditory system." Communications Biology, 8, 1456; doi: 10.1038/s42003-025-08858-3
DelRec: learning delays in recurrent spiking neural networks
Quéant et al. (2025). "DelRec: learning delays in recurrent spiking neural networks." ArXiv; doi: https://doi.org/10.48550/arXiv.2509.24852
A 23.5 TOPS/W Depthwise Separable Convolution Accelerator for Event-based Depth Estimation
Brito et al. (2025). "A 23.5 TOPS/W Depthwise Separable Convolution Accelerator for Event-based Depth Estimation." ISCAS; doi: 10.1109/ISCAS56072.2025.11044178
A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses
Rançon et al. (2024). "A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses." PLoS Computational Biology; doi: https://doi.org/10.1371/journal.pcbi.1012288
FPGA implementation of a Spiking Neural Network for Real-Time Action Potential and Burst Detection
Cheslet et al. (2023). "FPGA implementation of a Spiking Neural Network for Real-Time Action Potential and Burst Detection." BioCAS; doi: 10.1109/BioCAS58349.2023.10388622
Optical flow estimation from event-based cameras and spiking neural networks
Cuadrado et al. (2023). "Optical flow estimation from event-based cameras and spiking neural networks." Frontiers in Neuroscience; doi: https://doi.org/10.3389/fnins.2023.1160034
StereoSpike: Depth Learning With a Spiking Neural Network
Rançon et al. (2022). "StereoSpike: Depth Learning with a Spiking Neural Network." IEEE Access; doi: 10.1109/ACCESS.2022.3226484
Talks
A SNN with plastic delays could provide a mechanistic understanding of V1 responses to dynamic stimuli.
Online workshop flash talk at Spiking Neural networks as Universal Function Approximators (SNUFA 2024), Online
Recurrent neural networks outperform canonical computational models at fitting auditory brain responses
Conference proceedings talk at 33rd Annual Computational Neuroscience Meeting (CNS 2024), Natal, Brazil
StereoSpike: Depth Learning with a Spiking Neural Network
Online workshop talk at Spiking Neural Networks as Universal Function Approximators (SNUFA 2021), Online