Andreas Tolias Lab @ Stanford University

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@AToliasLab

to understand intelligence and develop technologies by combining neuroscience and AI

Palo Alto, CA toliaslab.org Joined May 2017

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Andreas Tolias Lab @ Stanford University @AToliasLab Oct 22

Help us build the next frontier of AI, inspired by the brain. @naturecomputes and I are hiring a Chief of Staff at @metamorphiclabs ! If you’re high energy, super ambitious, and know how to get things done, apply. Please repost

Sophia Sanborn Verified account @naturecomputes Oct 21

Andreas Tolias & I are hiring a Chief of Staff to work closely with us at @metamorphiclabs.

Seeking a high-agency generalist with strong judgment and exceptional execution who thrives in a dynamic environment and has a track record of owning complex projects from ambiguity to results.

You’ll work directly with us across strategic projects, fundraising, partnerships, and business operations, with substantial autonomy and room to grow.

If this sounds like you or someone you know, we’d love to connect.

About Metamorphic: metamorphic.com
Role & Application: lnkd.in/gYp-b3c3
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Andreas Tolias Lab @ Stanford University @AToliasLab Jun 17

A hundred years ago, Edgar Adrian recorded from a single nerve fibre and showed the stimulus out in the world is carried in the rate of its firing — he started calling the impulses a "code." We've been trying to read that code ever since.

For parts of the brain like V1 we've had a good answer for more than fifty years, and it was written in math: a Gabor filter. Orientation, spatial frequency, phase — compact, and you could measure how well it fit. Deeper in the hierarchy, in areas like V4, we never had the equivalent. We mostly had qualitative descriptions — they fire to shape, color and texture — but couldn't write down a more precise one for a single neuron, let alone put a number on how well it captured what the neuron responds to.

Our new paper from The Enigma Project, led by @vedanglad, uses vision-language models to write that correspondence in ordinary human words. The words don't just give a qualitative description of what each neuron is tuned for; crucially, they let you quantify how well that language captures the tuning. Take a neuron's description, regenerate fresh images from it with a diffusion model, show them to a digital twin, and measure: descriptions written to drive V4 neurons pushed 96.1% of them into the top 5% of their responses to natural images; descriptions written to suppress pushed 97.6% into the bottom 5%.

So we now have an automated way to describe the map between neurons and the world — in human language, in the macaque brain.

That opens a new avenue: describing biological neurons in the same currency we use to interpret artificial ones, and comparing mechanistic interpretability across brains and AI directly.

This work is together with Nikos Karantzas, @kfrankelab @naturecomputes @SuryaGanguli @TamarRottShaham

Many thanks to the rest of the team at the Enigma Project @Stanford and @jamesfickel for generous support

Vedang Lad Verified account @vedanglad Jun 16

How well can you describe the feature selectivity of a vision neuron … with words? Interpretability has long borrowed from neuroscience — and maybe it can give back too! 🧵

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Andreas Tolias Lab @ Stanford University @AToliasLab Jun 4

If you're at @CVPR this week, come meet the Metamorphic team. We're hosting a happy hour tomorrow evening with @SpiralDB on NeuroAI, AI for Science, and multimodal modeling — and we're hiring. Find @KonstantinWille and @AdrianoCardace
there. 👇 metamorphic.com

metamorphic.com Metamorphic: From artificial to natural intelligence Metamorphic is a frontier NeuroAI research lab bridging neuroscience and machine learning to build robust, efficient and safe AI.

Sophia Sanborn Verified account @naturecomputes Jun 4

Metamorphic is at @CVPR this week.

Join us tomorrow evening for a kick-off happy hour co-hosted with @SpiralDB to talk AI for Science, multimodal modeling, and what comes next.

We’re growing the team - find @KonstantinWille
and @AdrianoCardace there if you’re curious.

luma.com/i4wjq5lu
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Andreas Tolias Lab @ Stanford University @AToliasLab Mar 31

What are deep neural predictive models actually good for?

We use them as digital twins of visual cortex.

In our inception loop paradigm: large-scale natural data → data-driven #DeepLearning predictive models → in silico experiments → in vivo verification—we characterize center-surround interactions in a fully non-parametric way.

Key result: surrounds can be excitatory when they complete the center in ways consistent with natural scene statistics, and suppressive when they disrupt it.

These results generalize across mouse and macaque V1

We then formalize this as a Bayesian normative model where neuronal activity for preferred center features reflects posterior beliefs about likely center-surround configurations.

A step toward using #AI + large-scale neural data not just to predict the brain, but to discover its principles.

Katrin Franke Verified account @kfrankelab Mar 31

New paper out in @NeuroCellPress 🎉

What determines contextual modulation in primary visual cortex (V1)?

The key result ⚡ V1 neurons are facilitated by surrounds that complete their optimal center feature according to natural scene statistics, and suppressed by surrounds that disrupt it — a principle explained by hierarchical Bayesian inference and conserved across mouse and macaque.

These results converge with Deveau et al. in @NeuroCellPress (cell.com/neuron/fulltex…) from the lab of @HistedLab, who show that recurrent circuits in V1 filter temporal input sequences to selectively boost natural dynamics, and Lange et al. in @ScienceMagazine (science.org/doi/10.1126/sc…) from the lab of @haefnerlab, who show that perceptual learning increases population redundancy as predicted by generative inference. A consistent picture is emerging: (early) visual cortex actively infers the statistical structure of the natural world.

Amazing collaboration with @AToliasLab @haefnerlab @sinzlab Antolik Lab & many more — led by @jiakunfu, with co-authors @suhas_shrinivas & @LuchinoBaroni & many more

The paper is open-access and available here: doi.org/10.1016/j.neur…

More detailed approach:

We trained CNN digital twins on large-scale two-photon recordings from mouse V1 and used them to synthesize, for each neuron individually, the surrounds that most strongly facilitate or suppress its response to its optimal center stimulus. Closed-loop in vivo inception loop experiments confirmed the predictions.

Key qualitative finding:
Surrounds that complete the optimal center feature under natural scene statistics → facilitation
Surrounds that disrupt it → suppression

We verified this with an independent generative diffusion model (blind to our CNN): statistically likely continuations of the optimal center feature were significantly more similar to facilitatory surrounds in V1 representational space. The same principle holds in macaque V1 despite major differences in receptive field organization.

We formalize these results in a hierarchical Bayesian inference model — V1 neurons represent posterior beliefs about local features, with feedback from higher areas encoding global scene structure — and find like-to-like excitatory connectivity in the MICrONS dataset as a candidate circuit mechanism.

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Andreas Tolias Lab @ Stanford University @AToliasLab Mar 18

Excited that to we have contributed to this work !

allen institute @AllenInstitute Mar 17

The brain as it's never been seen before.

Last year, scientists created the largest wiring diagram and functional map of a mammal brain to date.

#BrainAwarenessWeek @dana_fdn

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