Author | Lightnews

Andreas Tolias @andreastolias.bsky.social · Apr 25

Deeply grateful to the @simonsfoundation.org for launching SCENE and thrilled to join this 10-year journey into ecological neuroscience—unraveling how sensory and motor systems interact. Excited to collaborate with an incredible team of theorists and experimentalists working across species!

Simons Foundation @simonsfoundation.org · Apr 24

We are excited to announce our new Simons Collaboration on Ecological Neuroscience (SCENE)! This program will unite experts in experimental and computational #neuroscience approaches to investigate how the brain represents sensorimotor interactions. www.simonsfoundation.org/2025/04/24/s... #science

Simons Foundation Launches Collaboration on Ecological Neuroscience

Simons Foundation Launches Collaboration on Ecological Neuroscience on Simons Foundation

www.simonsfoundation.org

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Andreas Tolias @andreastolias.bsky.social · Apr 19

Building on Lurz et al., our new Wang et al. studies movie‑data performance vs. training size and compares scaling for Conv‑LSTM vs. CvT(convolutional vision
transformer)‑LSTM. Details: www.nature.com/articles/s41...

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Reposted by Andreas Tolias

Alexander Ecker @aecker.bsky.social · Apr 18

In Lurz et al., ICLR 2021 we did quite some analysis on scaling and generalization across animals in the context of visual response prediction (incl. behavioral modulation) with @sinzlab.bsky.social and @andreastolias.bsky.social: openreview.net/forum?id=Tp7...

Generalization in data-driven models of primary visual cortex

Deep neural networks (DNN) have set new standards at predicting responses of neural populations to visual input. Most such DNNs consist of a convolutional network (core) shared across all neurons...

openreview.net

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Andreas Tolias @andreastolias.bsky.social · Apr 15

A super exciting paper by @aecker.bsky.social and Marissa Weis, part of the #MICrONS package, deriving a set of principles to characterize the morphological diversity of excitatory neurons across cortical layers.
www.nature.com/immersive/d4...

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Andreas Tolias @andreastolias.bsky.social · Apr 13

We didn't know the optimal patterns driving mouse V1 neurons until the deep learning model by Walker et al. (2019). FYI: Unlike mice, Gabors actually describe macaque V1 neurons quite well (Fu et al., Cell Reports).

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Reposted by Andreas Tolias

Sophia Sanborn @naturecomputes.bsky.social · Apr 13

MICrONS represents a huge step forward for the field. Big-data and AI will drive the next wave of discoveries in neuroscience

Andreas Tolias @andreastolias.bsky.social · Apr 10

After 7 years, thrilled to finally share our #MICrONS functional connectomics results!

We recorded activity from ~75K neurons in the visual cortex of a single mouse, then mapped its wiring using electron microscopy.

nature.com/immersive/d42859-025-00001-w/index.html

The MICrONS Project

An unprecedented dataset of high resolution anatomical images of individual cells in mouse visual cortex, mapped on to their responses. This integrated view of function and structure lays a foundation...

nature.com

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Reposted by Andreas Tolias

Sophia Sanborn @naturecomputes.bsky.social · Apr 13

Join me, @andreastolias.bsky.social, and many of the incredible MICrONS team members in an AI-driven approach to neuroscience discovery

Apply here: www.linkedin.com/jobs/view/42...

Or email us at [email protected]

Enigma hiring Research Engineer: Multi-Modal Modeling in Stanford, CA | LinkedIn

Posted 11:42:35 PM. The modeling team at Enigma is seeking ML Research Engineers to build and scale the next generation…See this and similar jobs on LinkedIn.

www.linkedin.com

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Andreas Tolias @andreastolias.bsky.social · Apr 13

3/3 The core strength of our approach—robust prediction of neural responses to novel visual stimulus domains. Dyer's autoregressive approach generates latent embeddings for neural decoding—an entirely different architectural paradigm with different scientific objectives.

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Andreas Tolias @andreastolias.bsky.social · Apr 13

2/3 However this is not the main point, these models serve fundamentally different purposes. Ours explicitly predicts neural responses to visual stimuli (an encoding model), creating functional digital twins.

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Andreas Tolias @andreastolias.bsky.social · Apr 13

1/3 Just for clarification our foundation model was introduced on March 21st, 2023—predating Dyer et al. by over six months.
www.biorxiv.org/content/bior...

www.biorxiv.org

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Andreas Tolias @andreastolias.bsky.social · Apr 13

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Andreas Tolias @andreastolias.bsky.social · Apr 13

8/8 Deep learning simulation enables systematic representational-level characterization, though detailed circuit-cell-type-level mechanistic comprehension remains beyond current capabilities in the cortex.

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Andreas Tolias @andreastolias.bsky.social · Apr 13

7/8 and characterization of the feature landscape of mouse visual cortex (Tong et al., bioRxiv 2023)—just a few examples of their applications. Most importantly, they yield in silico predictions which are subsequently verified through experimental testing.

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Andreas Tolias @andreastolias.bsky.social · Apr 13

6/8 Predictive models also enabled to systematically characterize single neuron invariance properties (Ding et al., bioRxiv 2023), center-surround interactions (Fu et al., bioRxiv 2023), color-opponency mechanisms (Hofling et al., Elife 2024),

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Andreas Tolias @andreastolias.bsky.social · Apr 13

5/8 Our models also revealed that mouse V1 neurons shift their selectivity toward UV when pupil dilation or running begins, despite maintaining stable spatial stimulus structure—discovered in the digital twin and validated experimentally in closed-loop studies (Franke et al., Nature 2022).

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Andreas Tolias @andreastolias.bsky.social · Apr 13

4/8 For example, these simulations revealed that mouse V1 neurons exhibit complex spatial features deviating from the common notion that Gabor-like stimuli are optimal (Walker, Sinz et al., Nature Neuroscience 2019).

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Andreas Tolias @andreastolias.bsky.social · Apr 13

3/8 When ANNs accurately simulate neural function, they facilitate 'mechanistic interpretability' (to borrow the AI term)—enabling rigorous representational-level analysis of neuronal tuning.

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Andreas Tolias @andreastolias.bsky.social · Apr 13

2/8 Moreover, both task- and data-driven neural predictive models are powerful tools to gain neuroscientific insights as we and others have demonstrated repeatedly.

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Andreas Tolias @andreastolias.bsky.social · Apr 13

1/8: This quote from our abstract refers to task-driven modeling approaches (e.g., Yamins, DiCarlo, et al.) which define computational objectives and reveal hidden representations closely matching brain activity—widely recognized for deepening insights into brain computations.

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Andreas Tolias @andreastolias.bsky.social · Apr 13

3/3 The core strength of our approach—robust prediction of neural responses to novel visual stimulus domains. Dyer's autoregressive approach generates latent embeddings for neural decoding—an entirely different architectural paradigm with different scientific objectives.

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Andreas Tolias @andreastolias.bsky.social · Apr 13

2/3 However this is not the main point, these models serve fundamentally different purposes. Ours explicitly predicts neural responses to visual stimuli (an encoding model), creating functional digital twins.

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Andreas Tolias @andreastolias.bsky.social · Apr 13

1/3 Just for clarification our foundation model was introduced on March 21st, 2023—predating Dyer et al. by over six months.
www.biorxiv.org/content/bior...

www.biorxiv.org

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Andreas Tolias @andreastolias.bsky.social · Apr 10

Huge thanks to @IARPAnews for funding this groundbreaking effort through the @BRAINinitiative, and to our amazing team at
@stanforduniversity.bsky.social @stanfordmedicine.bsky.social @BCM @Allen @Princeton @unigoettingen.bsky.social
#MICrONS #NeuroAI #Connectomics #FoundationModels #AI

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Andreas Tolias @andreastolias.bsky.social · Apr 10

Foundation models offer a powerful way to systematically decode the neural code of natural intelligence, bridging the gap between brain structure and function.

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Andreas Tolias @andreastolias.bsky.social · Apr 10

Instead, they preferentially connect based on shared functional tuning, choosing partners with similar feature selectivity (“what”) rather than merely receptive field overlap (“where”).

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