As promised (and only slightly late 😅): here's a figure-by-figure walk-through of our preprint "Random network structure stabilizes neural manifolds". This one is a bit more for the nerds. For an overview version 👉 bsky.app/profile/j-b-... Preprint: doi.org/10.64898/202... 1/8 🧠🧪
Random network structure stabilizes neural manifolds
Neuronal activity patterns change continuously over days and weeks, a phenomenon known as representational drift. Despite this, the geometric structure of population representations, namely the pairwise similarities between stimulus-evoked activity patterns, remains remarkably stable. How can ongoing changes in activity be consistent with stable representational similarity? We show that this is a generic consequence of random connectivity: in networks with random connectivity, output similarity is a monotonically increasing function of input similarity, independent of the specific connectivity pattern. Drift, whether driven by random synaptic turnover or Hebbian plasticity, merely transitions the network between random instantiations, leaving similarity intact. This extends to recurrent architectures and to deep neural networks, where continued training beyond performance saturation produces activity drift while preserving representational similarity. Although connectivity in the brain is not random, networks trained on high-dimensional inputs acquire connectivity that behaves statistically like a random projection, making these results broadly applicable to biological neural circuits. ### Competing Interest Statement The authors have declared no competing interest. Spanish Ministry of Science and Innovation, PCI2023-145967-2, PID2021-124702OB-I00 Spanish State Research Agency (AEI) – Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D, CEX2020-001084-M German Research Foundation (DFG), FOR 5368 ARENA
doi.org
Jens-Bastian Eppler@j-b-eppler.bsky.social · 2mo agoNew preprint: Random network structure stabilizes neural manifolds We’re excited to share our new work on representational drift. doi.org/10.64898/202... Representational drift poses a puzzle. 👇 A short thread below. In the next days a figure by figure walk through will follow. 1/5 🧪🧠
