Bichan Wu (@bichanw.bsky.social) & I wrote a tutorial paper on Reduced Rank Regression (RRR) — the statistical method underlying "communication subspaces" from Semedo et al 2019 — aimed at neuroscientists.
arxiv.org/abs/2512.12467
Reduced rank regression (RRR) is a statistical method for finding a low-dimensional linear mapping between a set of high-dimensional inputs and outputs. In recent years, RRR has found numerous applica...
We'd be grateful for any comments about points we overlooked, additional citations, as well as any corrections, clarifications, or suggestions for improvement! 🙏
Great opportunity to learn to use fancy neural data analysis tools developed at the @flatironinstitute.org. Sign up for this workshop at SFN 2025!
Interesting in how animals learn a sensory decision-making task from scratch? Come check out Helena Liu's (@helenaliu24.bsky.social) poster in the Saturday poster session at #cosyne2026 !
Proud to be a collaborator on this paper on compact deep neural network models of V4, with Ben Cowley (@benjocowley.bsky.social), Pati Stan, & Matt Smith, now finally out in print (by which I mean online).
We also derive some useful (known) extensions, such as adding a ridge penalty ("ridge RRR") and non-spherical noise (accounting for correlated response noise), both of which preserve a closed-form solution.
Part of our motivation was our own difficulty understanding RRR and its mathematical origins (e.g., Why is this an eigenvector problem?). We thought others might benefit from a simple derivation and some figures and comparisons to build intuition.
New paper with @deanpospisil.bsky.social , in which we introduce a new estimator for the "signal eigenspectrum" (i.e., the eigenvalues of the noiseless population responses). We re-analyze data from Stringer et al 2019 and show eigenvalues of mouse V1 are well explained by a broken power.
We introduce metrics for quantifying the degree of alignment between the communication subspace and the dominant modes of input and output population activity. (e.g., Are the dominant modes of the input population the same ones driving communication?)
Excited to be co-organising a #cosyne2026 workshop with Alison Comrie on 'algorithms for learning from scratch'! With a great line-up of speakers, we'll be tackling the question of what processes enable naive biological & artificial agents to adapt to new situations. Info here: tinyurl.com/4u8enf7k
