Louis Pezon @lpezon.bsky.social · 28/09/2026How can RNNs re-use the same dynamics across different tasks to implement compositional computation? What are the consequences for network connectivity & activity? If you're interested, come and find out at the Bernstein Conference, poster session III-#48! 0172
Reposted by Louis PezonGerstnerLab @gerstnerlab.bsky.social · 29/06/2026How can the brain learn the hidden hierarchical structure from high dimensional data? In our latest work, we use synthetic datasets to analyze two classes of bio-plausible learning rules: variants of Direct Feedback Alignment, and local self-supervised learning. We find only the latter succeeds. 12612
Louis Pezon @lpezon.bsky.social · 30/06/2026Both brains and RNNs can re-use components of computation across similar tasks or contexts. But what exactly are those “shared components”? How can they be used to solve several tasks? We address these questions in a new preprint with @avm.bsky.social! Link: www.biorxiv.org/content/10.6...biorxiv.orgInterpretable compositional computation with recurrent neural networksFlexible cognition utilizes reusable components to enable rapid adaptation of behavior to different contexts or tasks. Analysis of artificial neural networks trained on multiple tasks suggested that t... 16425
Reposted by Louis PezonFlavio Martinelli @flavioh.bsky.social · 10/06/2026NEW PAPER. Why do larger networks train better? "Because they contain more candidate *sub*networks that can learn the task" → lottery tickets This popular explanation uses an appealing but misleading metaphor🧵 We propose an intuitive alternative grounded in theory: escape dimensions 519953
Reposted by Louis PezonValentin Schmutz @bio-emergent.bsky.social · 08/05/2026Unbelievably honoured to read Tatiana Engel's (@engeltatiana.bsky.social) wonderfully written Preview on our work "Linking neural manifolds to circtuit structure in recurrent networks" (with @lpezon.bsky.social & @gerstnerlab.bsky.social) in this issue of Neuron www.cell.com/neuron/fullt... 🙏cell.comFinding clues to circuit structure in population dynamics and single-neuron selectivityIn this issue of Neuron, Pezon et al. introduce neural circuit models with flexible connectivity structure that can generate low-dimensional population dynamics with different distributions of single-... 05617
Louis Pezon @lpezon.bsky.social · 13/03/2026Presenting a poster tomorrow at Cosyne 26: [3-033] Compositional computation via shared latent dynamics in low-rank RNNs. With @avm.bsky.social, we explore how RNNs can re-use the same dynamics across different tasks, and what it implies for their connectivity and neural activity. 0102
Reposted by Louis PezonAlexander van Meegen @avm.bsky.social · 12/03/2026Travelling to COSYNE seems to be the perfect opportunity to announce that I started my own lab at RWTH Aachen University earlier this year, funded by NRW's Ministry of Culture and Science through its Return Program. If you are at COSYNE and want to chat please reach out! 5335
Louis Pezon @lpezon.bsky.social · 06/03/2026Excited to share our new paper to be published in Neuron! With Valentin Schmutz @bio-emergent.bsky.social and Wulfram Gerstner @gerstnerlab.bsky.social, we explore how circuit structure in RNNs shapes network computation and single-neuron responses. www.sciencedirect.com/science/arti...sciencedirect.comLinking neural manifolds to circuit structure in recurrent networksDimensionality reduction methods are widely used in neuroscience to investigate two complementary aspects of neural activity: the distribution of sing… 14211