Thank you so much! From the Allen Brain Cell atlas, we found cell types expressing Fezf1, Tfap2b, Nr5a1, Tph2, Hmx2, Grin2c, Foxa2, Six6, and Vgll2 have relatively larger contributions in predicting timescales, while multiple other genes also contribute. Still need to explore their functionality.

Two flagship papers from the International Brain Laboratory, now out in ‪@Nature.com‬:
🧠 Brain-wide map of neural activity during complex behaviour: doi.org/10.1038/s41586-025-09235-0
🧠 Brain-wide representations of prior information in mouse decision-making: doi.org/10.1038/s41586-025-09226-1 +

A complete brain-wide activity map at single-cell resolution has been revealed for the first time.

Researchers recorded from 650,000+ neurons across 279 brain areas to track decision-making in mice.

Read the story:
🔗 www.sainsburywellcome.org/web/research...

The two key studies of the International Brain Laboratory @intlbrainlab.bsky.social are out today!

A brain-wide map of neural activity during complex behaviour
www.nature.com/articles/s41...

Brain-wide representations of prior information in mouse decision-making
www.nature.com/articles/s41...

8/8 We identified two potential network mechanisms for the universal scaling of intrinsic timescales across the mouse brain: fixed point dynamics operating near the edge of instability in linear networks, or chaotic dynamics in nonlinear networks with heavy-tailed connectivity.

7/8 Across neurons, the diversity of timescales revealed a multiscale architecture, in which fast timescales determined regional differences in medians, while slow timescales universally followed a power-law distribution with an exponent near 2.

6/8 We tested the relationship between timescales and gene expression profiles across the whole brain at fine spatial resolution. Spatial patterns of gene expression predicted timescale variation at a resolution finer than brain-area boundaries.

5/8 Consistent with prior findings, median effective timescales were positively correlated with anatomical hierarchy scores in the cortex, but not in the thalamus.

4/8 We compared effective timescales between selective and non-selective neurons in the IBL decision-making task. Neurons selective for choice or reward exhibited significantly longer timescales compared to their non-selective counterparts, but not for the visual stimulus.

3/8 We generated a map of intrinsic timescales across the mouse brain. Median effective timescales varied widely across 223 brain areas, from tens of milliseconds to several seconds. They were up to fivefold longer in the midbrain and hindbrain than in the forebrain.

2/8 To capture multiple timescales in single-neuron dynamics, we fitted their autocorrelations with linear mixtures of exponential decay functions, one for each timescale. We then defined an effective timescale to facilitate comparison across neurons with varying numbers of timescales.

1/8 By analyzing brain-wide Neuropixels recordings from @intlbrainlab.bsky.social, we found that individual neurons exhibited diverse autocorrelation shapes both within and across brain areas, indicating diverse timescales across the brain.

Excited to share our new preprint on the brain-wide organization of intrinsic timescales at single neuron resolution. Work w/ @roxana-zeraati.bsky.social, @intlbrainlab.bsky.social, Anna Levina, @engeltatiana.bsky.social : www.biorxiv.org/content/10.1...

Our new paper with @chrismlangdon is just out in @natureneuro.bsky.social! We show that high-dimensional RNNs use low-dimensional circuit mechanisms for cognitive tasks and identify a latent inhibitory mechanism for context-dependent decisions in PFC data.
www.nature.com/articles/s41...