Daniel Anthes

Daniel Anthes

@anthesdaniel.bsky.social

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Daniel Anthes
@anthesdaniel.bsky.social

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

arxiv.orgThe illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral streamIn 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 ...

Daniel Anthes reposted

Philip Sulewski
@psulewski.bsky.social

Now out in Nature Neuroscience: "Fixation duration on natural scenes is explained by memory encoding not processing demand". www.nature.com/articles/s41... Our eyes don't linger because recognition is hard; they linger to remember. Let me take you on a quick tour. 🧵

nature.comFixation duration on natural scenes is explained by memory encoding not processing demand - Nature NeuroscienceBy combining magnetoencephalography and eye tracking, this study sheds light on why people fixate on some parts of natural scenes longer than others. Rather than visual complexity, fixation durations ...

Daniel Anthes reposted

Martin Hebart
@martinhebart.bsky.social

I'm proud to say we are releasing LAION-fMRI, a densely sampled 7T fMRI dataset of natural images, with very broad stimulus sampling for testing countless hypotheses and for deeply exploring brain representations. The dataset is now available at laion-fmri.hebartlab.com What does LAION-fMRI offer? 🧵

laion-fmri.hebartlab.comLAION-fMRI - a 7T fMRI dataset of human visionLAION-fMRI (LfMRI / LAION MRI dataset): 5 subjects, 25,052 launch-release natural images, 165 acquired 7T fMRI sessions with single-trial GLMsingle betas, retinotopy, localizers, and diffusion.
Daniel Anthes
@anthesdaniel.bsky.social

All our results point into a new direction: even the earliest phase of visual processing is governed by informative spatiotemporal trajectories, rather than a spatial feedforward transfer of information. This has implications for our core understanding of primate vision, modelling, and NeuroAI. /7

Daniel Anthes
@anthesdaniel.bsky.social

This is the result of a fantastic collaboration with @sushrutthorat.bsky.social, Anna Mitola, Paolo Papale, @peterkonig.bsky.social & @timkietzmann.bsky.social /fin arxiv.org/abs/2604.12825

arxiv.orgThe illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral streamIn 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 ...
Daniel Anthes
@anthesdaniel.bsky.social

We then turned to task-trained ANNs to characterise the representations underlying these communication phases. Models for ‘early’ neural interactions predict representational geometries characterized well by low-level features, ‘late’ models are predicted by ‘high-level’ features geometries. /5

Daniel Anthes
@anthesdaniel.bsky.social

But can we find a single time-point that offers a high-accuracy stable categorical readout from IT? No. Category information in IT can be decoded much better using a recurrent neural network with access to the whole spatiotemporal trajectory, compared to a pure ‘spatial’ code. /6

Daniel Anthes
@anthesdaniel.bsky.social

The basis of our claims: neural responses to multiple repetitions of 1000 natural images from up to 1024 sites across V1, V4 & IT of two macaques. This allowed for time-resolved analyses of visual processing across multiple regions within single trials. /3

Daniel Anthes
@anthesdaniel.bsky.social

We modeled information transfer from V4 to IT using linear models across a large range of time/delay combinations. This revealed, for all arrays tested, multiple distinct interaction windows within ~150ms of processing (not one, as would be expected from a stage-like ‘feedforward’ process). /4

Daniel Anthes
@anthesdaniel.bsky.social

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

arxiv.orgThe illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral streamIn 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 ...
Daniel Anthes
@anthesdaniel.bsky.social

In studying primate vision, much attention has been paid to the first “feedforward sweep”: a stage-like progression across the ventral stream hierarchy. But is this view too simplistic? We here turn to modelling multi-array recordings and inter-area communication to find answers. /2

Daniel Anthes reposted

Philip Sulewski
@psulewski.bsky.social

What happens if you hook up an energy-efficiency optimising RNN on active vision input? It learns predictive remapping and path integration into allocentric scene coordinates. Now out in patterns: www.cell.com/patterns/ful...

cell.comPredictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active visionThis study gives an example of how complex computations in neural networks can emerge from simple physical principles. Training a model to optimize internal energy efficiency while performing eye move...
Tim Kietzmann @timkietzmann.bsky.social

Can seemingly complex multi-area computations in the brain emerge from the need for energy efficient computation? In our new preprint on predictive remapping in active vision, we report on such a case. Let us take you for a spin. 1/6 www.biorxiv.org/content/10.1...

Daniel Anthes reposted

Guido Meijer
@guidomeijer.com

Want to make publication-ready figures come straight from Python without having to do any manual editing? Are you fed up with axes labels being unreadable during your presentations? Follow this short tutorial including code examples! 👇🧵

Daniel Anthes reposted

Marlene Cohen
@marlenecohen.bsky.social

New results for a new year! “Linking neural population formatting to function” describes our modern take on an old question: how can we understand the contribution of a brain area to behavior? www.biorxiv.org/content/10.1... 🧠👩🏻‍🔬🧪🧵 #neuroskyence 1/

biorxiv.orgLinking neural population formatting to functionAnimals capable of complex behaviors tend to have more distinct brain areas than simpler organisms, and artificial networks that perform many tasks tend to self-organize into modules (1-3). This sugge...

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