Grateful to the organizing team: @mariusschneider.bsky.social, @jingpeng.bsky.social, Y Hou, L Herbelin, J Canzano, @spencerlaveresmith.bsky.social.

👏🙏🙌 Special thanks to MS, YH, JP for daily work behind the scenes (at the expense of their own research). The challenge would not exist without them!

Next: Join our NeurIPS workshop on Dec 7, 2025, 11 to 2 PT on Zoom!

Hear from top competitors and our 3 keynote speakers:
- @sinzlab.bsky.social
- @ninamiolane.bsky.social
- @crisniell.bsky.social

More info: robustforaging.github.io/workshop

#NeurIPS2025 #Neuroscience #AI

Top teams:

🥇 371333_HCMUS_TheFangs (ASR 0.968, MSR 0.940, Score 0.954)
🥈 417856_alluding123 (ASR 0.864, MSR 0.650, Score 0.757)
🥉 366999_pingsheng-li (ASR 0.802, MSR 0.670, Score 0.736)

Full leaderboard: robustforaging.github.io/leaderboard/

#NeurIPS2025 #Neuroscience #AI

Thank you so much for this tip! Infuriating change

Good eye! You’re right, my spicy summary skipped over the nuance. Color was a free-form response, which we later binned into 4 categories for modeling. Chance level isn’t 25% but adjusted for class imbalance (majority class frequency). Definitely preliminary re:“perception”, but beats stimulus-only!

Thanks! I hear you, that thought has crossed my mind, too. But IP & money have already held this field back too long... This work was funded by public grants, and our philosophy is to keep data + code open so others can build on it. Still, watch us get no credit & me eat my words in 5-10 years 😅

Together, this argues for closed-loop visual prostheses:

📡 Record neural responses
⚡ Adapt stimulation in real-time
👁️ Optimize for perceptual outcomes

This work was only possible through a tight collaboration between 3 labs across @ethz.ch, @umh.es, and @ucsantabarbara.bsky.social!

And here’s the kicker: 🚨

If you try to predict perception from stimulation parameters alone, you’re basically at chance.

But if you use neural responses, suddenly you can decode detection, brightness, and color with high accuracy.

We pushed further: Could we make V1 produce new, arbitrary activity patterns?

Yes ... but control breaks down the farther you stray from the brain’s natural manifold.

Still, our methods required lower currents and evoked more stable percepts.

Prediction is only step 1. We then inverted the forward model with 2 strategies:

1️⃣ Gradient-based optimizer (precise, but slow)
2️⃣ Inverse neural net (fast, real-time)

Both shaped neural responses far better than conventional 1-to-1 mapping

We trained a deep neural network (“forward model”) to predict neural responses from stimulation and baseline brain state.

💡 Key insight: accounting for pre-stimulus activity drastically improved predictions across sessions.

This makes the model robust to day-to-day drift.

Many in #BionicVision have tried to map stimulation → perception, but cortical responses are nonlinear and drift day to day.

So we turned to 🧠 data: >6,000 stim-response pairs over 4 months in a blind volunteer, letting a model learn the rules from the data.

Curious though - many of the orgs leading this effort don’t seem to be on @bsky.app yet… Would love to see more #Blind, #Accessibility, and #DisabilityJustice voices here!