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Fabian Schneider

@fabianschneider.bsky.social

Doctoral researcher at the University Medical Centre Hamburg-Eppendorf (UKE). Interested in memory, audition, semantics, neural coding, spiking networks. http://mvpy.tools

23 Posts171 Follower150 Folgt
Daniel Anthes@anthesdaniel.bsky.social · 4mo ago

Excited about our new preprint: “The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream” arxiv.org/abs/2604.12825 Work with Sushrut Thorat, Anna Mitola, Paolo Papale, Peter König & Tim Kietzmann 🧵 thread below

The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream

In studying primate vision, a large body of work focuses on the first feedforward sweep. During this initial time window, information is thought to pass through ventral stream regions in a stage-like ...

arxiv.org

The Transmitter @thetransmitter.bsky.social · 5mo ago

Prediction without understanding sustained astronomy through a thousand years of epicycles, writes @tonyzador.bsky.social. AI is now offering neuroscience the same deal. #neuroskyence www.thetransmitter.org/machine-lear...

Can AI do neuroscience without understanding?

Prediction without understanding sustained astronomy through a thousand years of epicycles. Artificial intelligence is now offering neuroscience the same deal.

thetransmitter.org

Anna Vasilevskaya@loghyr.bsky.social · 7mo ago

Our work with @georgkeller.bsky.social on testing predictive processing (PP) models in cortex is out on biorvix now! www.biorxiv.org/content/10.6... A short thread on our findings and thoughts on where we should move on from PP below.

A functional influence based circuit motif that constrains the set of plausible algorithms of cortical function

There are several plausible algorithms for cortical function that are specific enough to make testable predictions of the interactions between functionally identified cell types. Many of these algorithms are based on some variant of predictive processing. Here we set out to experimentally distinguish between two such predictive processing variants. A central point of variability between them lies in the proposed vertical communication between layer 2/3 and layer 5, which stems from the diverging assumptions about the computational role of layer 5. One assumes a hierarchically organized architecture and proposes that, within a given node of the network, layer 5 conveys unexplained bottom-up input to prediction error neurons of layer 2/3. The other proposes a non-hierarchical architecture in which internal representation neurons of layer 5 provide predictions for the local prediction error neurons of layer 2/3. We show that the functional influence of layer 2/3 cell types on layer 5 is incompatible with the hierarchical variant, while the functional influence of layer 5 cell types on prediction error neurons of layer 2/3 is incompatible with the non-hierarchical variant. Given these data, we can constrain the space of plausible algorithms of cortical function. We propose a model for cortical function based on a combination of a joint embedding predictive architecture (JEPA) and predictive processing that makes experimentally testable predictions. ### Competing Interest Statement The authors have declared no competing interest. Swiss National Science Foundation, https://ror.org/00yjd3n13 Novartis Foundation, https://ror.org/04f9t1x17 European Research Council, https://ror.org/0472cxd90, 865617

biorxiv.org

Fabian Schneider@fabianschneider.bsky.social · 8mo ago

Same sound, different perception: Do expectations change what you hear?👂🧠 We paired faces w topics and played the same ambiguous speech w different faces. The brain sharpened sensory signals toward predictions and showed gated prediction errors at higher levels. Read @plosbiology.org. Blueprint👇

Sensory sharpening and semantic prediction errors unify competing models of predictive processing in human speech comprehension

Speech comprehension relies on predictive mechanisms, but models disagree on whether the brain prioritizes expected or unexpected information. This study shows that sharpening of sensory representatio...

dx.plos.org

Fabian Schneider@fabianschneider.bsky.social · 8mo ago

Same sound, different perception: Do expectations change what you hear?👂🧠 We paired faces w topics and played the same ambiguous speech w different faces. The brain sharpened sensory signals toward predictions and showed gated prediction errors at higher levels. Read @plosbiology.org. Blueprint👇

Sensory sharpening and semantic prediction errors unify competing models of predictive processing in human speech comprehension

Speech comprehension relies on predictive mechanisms, but models disagree on whether the brain prioritizes expected or unexpected information. This study shows that sharpening of sensory representatio...

dx.plos.org

Eiko Fried@eikofried.bsky.social · 8mo ago

After 5 years of data collection, our WARN-D machine learning competition to forecast depression onset is now LIVE! We hope many of you will participate—we have incredibly rich data. If you share a single thing of my lab this year, please make it this competition. eiko-fried.com/warn-d-machi...

WARN-D machine learning competition is live » Eiko Fried

If you share one single thing of our team in 2026—on social media or per email with your colleagues—please let it be this machine learning competition. It was half a decade of work to get here, especi...

eiko-fried.com

Victoria Bosch@initself.bsky.social · 10mo ago

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

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Eelke Spaak@eelkespaak.bsky.social · 11mo ago

🧠 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-...)

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

Andrew Lampinen@lampinen.bsky.social · last yr.

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
Decision, Action, and Neural Computation (DANC) lab@danclab.bsky.social · last yr.

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! 🧠📈

Maciek Szul@maciekszul.bsky.social · last yr.

🚨🚨🚨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!

Overview of the simulation strategy and analysis. a) Pial and white matter boundaries surfaces are extracted from anatomical MRI volumes. b) Intermediate equidistant surfaces are generated between the pial and white matter surfaces (labeled as superficial (S) and deep (D) respectively). c) Surfaces are downsampled together, maintaining vertex correspondence across layers. Dipole orientations are constrained using vectors linking corresponding vertices (link vectors). d) The thickness of cortical laminae varies across the cortical depth (70–72), which is evenly sampled by the equidistant source surface layers. e) Each colored line represents the model evidence (relative to the worst model, ΔF) over source layer models, for a signal simulated at a particular layer (the simulated layer is indicated by the line color). The source layer model with the maximal ΔF is indicated by “˄”. f) Result matrix summarizing ΔF across simulated source locations, with peak relative model evidence marked with “˄”. g) Error is calculated from the result matrix as the absolute distance in mm or layers from the simulated source (*) to the peak ΔF (˄). h) Bias is calculated as the relative position of a peak ΔF(˄) to a simulated source (*) in layers or mm.