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...

I am thrilled to share that this winter I will be starting my lab in the GIGA Institute of @universitedeliege.bsky.social!

The lab will study early brain development at the intersection of systems and computational neuroscience.

🌐 You can find out more on the lab website: www.chinilab.com

1/2

7/7
We implemented iSTTC to tackle challenges in our own sparse, developmental datasets, and it’s working beautifully. We hope it helps others working with tricky spiking data. Try it yourself github.com/iinnpp/isttc!

6/7
iSTTC doesn’t just perform well in simulations; it works in the mess of real neural data, too. Using 30 min of Neuropixels recordings from the Visual Coding @alleninstitute.org dataset, iSTTC gave more stable, more accurate, and more inclusive IT estimates than other methods.

5/7
We didn’t set out to show this, but... 🫢ITs estimated from epoched spike data are dramatically less reliable, with up to 10x more estimation error than continuous data. Regardless of the method, this instability is real. iSTTC helps, but long and uninterrupted recordings still matter a lot.

4/7
It also beats PearsonR on epoched data by a wide margin. iSTTC yields ~17% lower estimation error and ~10% fewer failed fits: more accurate and representative IT estimates!

3/7
iSTTC outperforms classic autocorrelation (ACF) on synthetic continuous data, especially under low firing rates and high burstiness.

2/7
ITs are estimated both on continuous and epoched data, but with inconsistent methods (ACF vs Pearson’s R). iSTTC fixes this: the same algorithm works on both data types!

1/7
Why does this matter? ITs tell us how neurons integrate information over time, a critical link between neural dynamics and cognition. But current methods suffer from bias and limited applicability, especially in biologically realistic conditions (low firing rates and high burstiness).

New preprint is out! We present iSTTC, a robust method for estimating intrinsic neural timescales (ITs) from single-unit activity. It’s accurate, stable, and works even when data is sparse.
doi.org/10.1101/2025...

We are super happy to announce the third Workshop of Ideas in Neuroscience! We will once again look critically at assumptions of modern neuroscience: what does it mean that the brain encodes information? Is this a useful approach, or a metaphor that blurs our vision?