iiinova — where your digital self keeps evolving

Reposted by Irina Pochinok

Mattia Chini @mattiachini.bsky.social · 07/04/2026

Final stop for Con2Phys: Barcelona! After two amazing editions at COSYNE and Bernstein, we’re doing one last collaborative team-based hackathon. Open to all, but spots are limited. Registration opens on April 9, put it in your calendar! 👇😱 #Brainhack #OpenScience #Neuroscience #Fens2026

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Reposted by Irina Pochinok

Mattia Chini @mattiachini.bsky.social · 09/01/2026

📣 Pre-COSYNE Brainhack 2026 – Join Us in Lisbon! 🧠✨ March 10–11, 2026 • Lisbon, Portugal pre-cosyne-brainhack.github.io/hackathon2026 Kick off COSYNE week with two days of hands-on, team-based hacking around real electrophysiology data! ✅ Apply now via the event website (limited spots).

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Reposted by Irina Pochinok

COSYNE @cosynemeeting.bsky.social · 05/01/2026

We're excited for the Pre-COSYNE 2026 Brainhack! March 10–11 in Lisbon 🇵🇹 We'll be using CON²PHYS: same dataset, same questions, many truths? 🔹2 days 🔹team-based 🔹real ephys data 🔹open tools 🔹mini-presentations 🔹all skill levels Apply now! 👇👇👇 pre-cosyne-brainhack.github.io/hackathon202...

pre-cosyne-brainhack.github.io

Registration

Deadline: Sunday, January 18 at 5:00 PM (London time). Notification: We will notify applicants of the outcome by the end of January.

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Yanliang Shi @shiyanliang.bsky.social · 02/09/2025

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...

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Mattia Chini @mattiachini.bsky.social · 27/08/2025

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

chinilab.com

Chini Lab

Chini Lab at GIGA–ULiège. We study how neural activity emerges in early development from a systems neuroscience perspective.

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Irina Pochinok @irinapochinok.bsky.social · 05/08/2025

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!

github.com

GitHub - iinnpp/isttc: iSTTC: intrinsic neural timescales estimation

iSTTC: intrinsic neural timescales estimation. Contribute to iinnpp/isttc development by creating an account on GitHub.

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Irina Pochinok @irinapochinok.bsky.social · 05/08/2025

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.

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Irina Pochinok @irinapochinok.bsky.social · 05/08/2025

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.

image showing the relative estimation error versus signal length and versus number of trials

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Irina Pochinok @irinapochinok.bsky.social · 05/08/2025

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!

image shoing the difference in relative estimation error between iSTTC and PearsonR (left) and percenatge of rejected units for 4 methods (right)

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Irina Pochinok @irinapochinok.bsky.social · 05/08/2025

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

image showing the difference in realtive estimation error between iSTTC and ACF

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Irina Pochinok @irinapochinok.bsky.social · 05/08/2025

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!

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Irina Pochinok @irinapochinok.bsky.social · 05/08/2025

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).

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Nencki Open Lab @openlab.bsky.social · 24/04/2025

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?

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