Ever heard of the lottery ticket hypothesis?
Our new paper shows that lottery tickets are not a useful metaphor to explain the success of overparameterized neural networks - and suggests an alternative metaphor: escape dimensions
Unbelievably 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...
🙏
Episode #39 in #TheoreticalNeurosciencePodcast:
On modeling neural population activity with mean-field models – with Tilo Schwalger
theoreticalneuroscience.no/thn39
How can mean‑field models be systematically derived from the underlying microscopic dynamics of individual neurons?
This was a lot of fun! From my side, it started with a technical Q: what's the relation between two-side cavity and path integrals? Turns out it's a fluctuation correction - and amazingly, this also enable the "O(N) rank" theory by @david-g-clark.bsky.social and @omarschall.bsky.social. 🤯
Incredibly grateful to @cmiehl.bsky.social and @gjorjulijana.bsky.social for their thoughtful and spot-on Preview "Novelty beyond counting" of our recent work in Neuron with @modirshanechi.bsky.social and @gerstnerlab.bsky.social! 🧠✨ It's a real honor 🙏 www.cell.com/neuron/fullt...
In 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-...
www.cell.com
Novelty is not just about whats new, but also what feels new given past experience. New study from Sophia Becker in Wulfram Gerstner’s @epfl-brainmind.bsky.social lab posits a model showing how similarity between familiar and novel stimuli shapes exploration and learning - doi.org/10.1016/j.ne...
Presenting 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.
