Fabian Schneider
@fabianschneider.bsky.social
Doctoral researcher. Interested in memory, audition, semantics, predictive coding, spiking networks.
Pinned
🚨 Fresh preprint w/ @helenblank.bsky.social!
How does the brain acquire expectations about a conversational partner, and how are priors integrated w/ sensory inputs?
Current evidence diverges. Is it prediction error? Sharpening?
Spoiler: It's both.👀
🧵1/16
www.biorxiv.org/content/10.1...
How does the brain acquire expectations about a conversational partner, and how are priors integrated w/ sensory inputs?
Current evidence diverges. Is it prediction error? Sharpening?
Spoiler: It's both.👀
🧵1/16
www.biorxiv.org/content/10.1...
Reposted by Fabian Schneider
Introducing CorText: a framework that fuses brain data directly into a large language model, allowing for interactive neural readout using natural language.
tl;dr: you can now chat with a brain scan 🧠💬
1/n
tl;dr: you can now chat with a brain scan 🧠💬
1/n
November 3, 2025 at 3:17 PM
Introducing CorText: a framework that fuses brain data directly into a large language model, allowing for interactive neural readout using natural language.
tl;dr: you can now chat with a brain scan 🧠💬
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tl;dr: you can now chat with a brain scan 🧠💬
1/n
Reposted by Fabian Schneider
How well do classifiers trained on visual activity actually transfer to non-visual reactivation?
#Decoding studies often rely on training in one (visual) condition and applying it to another (e.g. rest-reactivation). However: How well does this work? Show us what makes it work and win up to 1000$!
#Decoding studies often rely on training in one (visual) condition and applying it to another (e.g. rest-reactivation). However: How well does this work? Show us what makes it work and win up to 1000$!
IMAGINE-decoding-challenge
Predict which words participants were hearing, based upon brain activity recordings of visually seeing these items?
www.kaggle.com
October 24, 2025 at 6:55 AM
How well do classifiers trained on visual activity actually transfer to non-visual reactivation?
#Decoding studies often rely on training in one (visual) condition and applying it to another (e.g. rest-reactivation). However: How well does this work? Show us what makes it work and win up to 1000$!
#Decoding studies often rely on training in one (visual) condition and applying it to another (e.g. rest-reactivation). However: How well does this work? Show us what makes it work and win up to 1000$!
Reposted by Fabian Schneider
🧠 Regularization, Action, and Attractors in the Dynamical “Bayesian” Brain
direct.mit.edu/jocn/article...
(still uncorrected proofs, but they should post the corrected one soon--also OA is forthcoming, for now PDF at brainandexperience.org/pdf/10.1162-...)
direct.mit.edu/jocn/article...
(still uncorrected proofs, but they should post the corrected one soon--also OA is forthcoming, for now PDF at brainandexperience.org/pdf/10.1162-...)
Regularization, Action, and Attractors in the Dynamical “Bayesian” Brain
Abstract. The idea that the brain is a probabilistic (Bayesian) inference machine, continuously trying to figure out the hidden causes of its inputs, has become very influential in cognitive (neuro)sc...
direct.mit.edu
October 22, 2025 at 8:59 AM
🧠 Regularization, Action, and Attractors in the Dynamical “Bayesian” Brain
direct.mit.edu/jocn/article...
(still uncorrected proofs, but they should post the corrected one soon--also OA is forthcoming, for now PDF at brainandexperience.org/pdf/10.1162-...)
direct.mit.edu/jocn/article...
(still uncorrected proofs, but they should post the corrected one soon--also OA is forthcoming, for now PDF at brainandexperience.org/pdf/10.1162-...)
Reposted by Fabian Schneider
In neuroscience, we often try to understand systems by analyzing their representations — using tools like regression or RSA. But are these analyses biased towards discovering a subset of what a system represents? If you're interested in this question, check out our new commentary! Thread:
![What do representations tell us about a system? Image of a mouse with a scope showing a vector of activity patterns, and a neural network with a vector of unit activity patterns
Common analyses of neural representations: Encoding models (relating activity to task features) drawing of an arrow from a trace saying [on_____on____] to a neuron and spike train. Comparing models via neural predictivity: comparing two neural networks by their R^2 to mouse brain activity. RSA: assessing brain-brain or model-brain correspondence using representational dissimilarity matrices](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:e6ewzleebkdi2y2bxhjxoknt/bafkreiav2io2ska33o4kizf57co5bboqyyfdpnozo2gxsicrfr5l7qzjcq@jpeg)
August 5, 2025 at 2:36 PM
In neuroscience, we often try to understand systems by analyzing their representations — using tools like regression or RSA. But are these analyses biased towards discovering a subset of what a system represents? If you're interested in this question, check out our new commentary! Thread:
🚨 Fresh preprint w/ @helenblank.bsky.social!
How does the brain acquire expectations about a conversational partner, and how are priors integrated w/ sensory inputs?
Current evidence diverges. Is it prediction error? Sharpening?
Spoiler: It's both.👀
🧵1/16
www.biorxiv.org/content/10.1...
How does the brain acquire expectations about a conversational partner, and how are priors integrated w/ sensory inputs?
Current evidence diverges. Is it prediction error? Sharpening?
Spoiler: It's both.👀
🧵1/16
www.biorxiv.org/content/10.1...
August 1, 2025 at 11:24 AM
🚨 Fresh preprint w/ @helenblank.bsky.social!
How does the brain acquire expectations about a conversational partner, and how are priors integrated w/ sensory inputs?
Current evidence diverges. Is it prediction error? Sharpening?
Spoiler: It's both.👀
🧵1/16
www.biorxiv.org/content/10.1...
How does the brain acquire expectations about a conversational partner, and how are priors integrated w/ sensory inputs?
Current evidence diverges. Is it prediction error? Sharpening?
Spoiler: It's both.👀
🧵1/16
www.biorxiv.org/content/10.1...
Reposted by Fabian Schneider
It's been a while since our last laminar MEG paper, but we're back! This time we push beyond deep versus superficial distinctions and go whole hog. Check it out- lots more exciting stuff to come! 🧠📈
🚨🚨🚨PREPRINT ALERT🚨🚨🚨
Neural dynamics across cortical layers are key to brain computations - but non-invasively, we’ve been limited to rough "deep vs. superficial" distinctions. What if we told you that it is possible to achieve full (TRUE!) laminar (I, II, III, IV, V, VI) precision with MEG!
Neural dynamics across cortical layers are key to brain computations - but non-invasively, we’ve been limited to rough "deep vs. superficial" distinctions. What if we told you that it is possible to achieve full (TRUE!) laminar (I, II, III, IV, V, VI) precision with MEG!
June 2, 2025 at 12:31 PM
It's been a while since our last laminar MEG paper, but we're back! This time we push beyond deep versus superficial distinctions and go whole hog. Check it out- lots more exciting stuff to come! 🧠📈