Fabrizio Musacchio

My pleasure 🙏 Great work!

🤯

Awesome work by @matteoguardamagna.bsky.social et al: The geometry of the #gridcell system seems to come online before #behavior does. Toroidal #manifolds in #rat #MEC appear at P10, align progressively with real space only after pups begin to explore. Strong case for preconfigured spatial circuits!

Is spatial navigation innate 🧠? Using #NeuroPixels we show that the #torus 🍩 underlying the #GridCell map exists already on day 10 in rats — before pups open eyes and ears and before they start upright walking. 🧵1:4
👇
www.biorxiv.org/content/10.6...

🔬 Registration is open for our pilot Introduction to napari Workshop!

napari is a powerful open-source image viewer for scientific data analysis in Python. This hands-on workshop will get you exploring multi-dimensional datasets fast.

#napari #Python #ImageAnalysis #DataScience #OpenSource

Ruffling some feathers, it's fascinating to ask: What forces drive this plot?

To what degree is our community targeting important challenges and creating impactful solutions vs caught up in the inertia of curiosity and opportunity?

For instance, ... /1

NEST conference June 16-17th 2026

Registration and submission of contributions are now open for the virtual NEST Conference 16-17 June 2026! 🎉
Please register and submit your contribution(s) via the conference website. Deadline for submission of contributions 10 April 2026

nest-simulator.org/conference.

#Hippocampus #learning #Neuroscience 👇

Cool work on conserved #MotorCortex dynamics across species. #Behavior differs mainly through different trajectories on shared #NeuralManifolds.

#NeuralDynamics #CompNeuro #Neuroscience 🧪

Erwartungshaltung

Neural geometry in the human hippocampus enables generalization across spatial position and gaze Hippocampal neurons track positions of self, others, and gaze direction. However, it is unclear how their respective neural codes differ enough to avoid confusion while allowing for abstraction. We re...

Compositional representation of self, others, and gaze direction in *human* hippocampus - super cool.

arxiv.org/abs/2603.04747

Compact deep neural network models of the visual cortex Nature - Parsimonious deep neural network models can be used for prediction of visual neuron responses.

DNN models of the brain are getting bigger. Are we replacing one complicated system in vivo with another in silico?

In new work, we seek the *smallest* DNN models of visual cortex, balancing prediction with parsimony.

It turns out these compact models are surprisingly small!

rdcu.be/e5H8G

𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗺𝗮𝗻𝗶𝗳𝗼𝗹𝗱𝘀 𝗮𝗻𝗱 𝗿𝗲𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗯𝗿𝗮𝗶𝗻?
This looks like a must read (suppl material bursting with goodies).
#neuroskeyence
doi.org/10.1016/j.ne...

Looking forward to being in @bonnbrainconf.bsky.social in less than two weeks. Reach out if you are around and would like to meet up! 🙌

Honored, a little nervous, and mostly very excited to be included in such a cool lineup 🤩 on #learning and #memory 🐀🐾🧠

Linking neural manifolds to circuit structure in recurrent networks Dimensionality reduction methods are widely used in neuroscience to investigate two complementary aspects of neural activity: the distribution of sing…

Excited to share our new paper to be published in Neuron!

With Valentin Schmutz @bio-emergent.bsky.social and Wulfram Gerstner @gerstnerlab.bsky.social, we explore how circuit structure in RNNs shapes network computation and single-neuron responses.
www.sciencedirect.com/science/arti...

fMRI connectivity is dominated by recurring high-amplitude BOLD events (CAPs, QPPs)
But what originates these events? Many think neuromodulation is the culprit. Our new work offers a simpler take: cortico-cortical dynamics alone can generate CAP-like events! doi.org/10.1371/journal.pcbi.1013995
🧵1/n

How does the brain generalize past experiences without confusing memories? 🧠

Our lab's newest preprint reveals a fundamental division of labor between the cortex and cerebellum that solves this problem. (Check out the cortex-cerebellum video below! 👇) 🧵

I am totally pumped about this new work . "Task-trained RNNs" are a powerful and influential framework in neuroscience, but have lacked a firm theoretical footing. This work provides one, and makes direct contact with the classical theory of random RNNs:
www.biorxiv.org/content/10.6...

NEW: #Kempner researchers develop a mean-field theory of task-trained RNNs that bridges random and learned connectivity—and find macaque motor cortex is best captured by an intermediate, task-specific recurrent structure.
Read the blog post 👇
🔗 bit.ly/47f3Ldl

Dissociable dynamic effects of expectation during statistical learning Neural responses during statistical learning reveal dissociable dynamic effects of expectation, supporting the opposing process theory within trials while demonstrating contrasting effects across tria...

🚨New paper alert 🚨

Using EEG and decoding analyses we find dynamic effects of expectation during statistical learning, both within and across trials 🧠

@auksz.bsky.social @fedemar.bsky.social
Caspar Schwiedrzik

elifesciences.org/articles/103...

Recurrent cortical networks encode natural sensory statistics via sequence filtering The visual cortex receives a stream of high-dimensional sensory input. The role of dense local, recurrent cortical connections in shaping responses to these inputs has been unclear. Here, we show that...

Recurrent cortical networks encode natural sensory statistics via sequence filtering

www.cell.com/neuron/fullt...

#compneuro #neuroskyence

The generalized Hierarchical Gaussian Filter

elifesciences.org/reviewed-pre...

#stats #cogpsy

🚨Preprint! “Bayesian surprise tracks the strength of perceptual insight” - Work with @lindedomingo.bsky.social & @gonzalezgarcia.bsky.social

Ever wondered what factors influence the subjective experience of suddenly understanding a previously unclear input?

Click below:
doi.org/10.64898/202...

A sepia toned, seated portrait of mathematician Georg Cantor in his early twenties. His wavy hair is swept back and he has a curly beard and thin mustache. Cantor wears a dark jacket and white shirt, with a tie in a bow at this neck.

Mathematician Georg Cantor was born #OTD in 1845. He is the founder of set theory and developed the concepts of cardinal and transfinite numbers.

These shocking results led his former professor Kronecker to call him a "corrupter of youth."

Image: Oberwolfach Photo Collection

#DynamicsClub in March: 𝗥𝗶𝗰𝗵𝗮𝗿𝗱 𝗚𝗮𝘀𝘁 @rgast.bsky.social will share his work on neural heterogeneity and network function (PMID: 38198531, 41475350) pubmed.ncbi.nlm.nih.gov/41475350/

March 6 (Friday) at 11am PT / 2pm ET
Join us in person at UCLA or on Zoom: lingyunxiong.github.io/DynamicsClub/

📢 iBehave Seminar: Dr. Tommaso Patriarchi (Univ. of Zürich)

🧠 Next-gen tools to visualize neuromodulators in the brain

📅 Mar 4, 16:30 | 📍 Univ. of Cologne @unicologne.bsky.social | 💻 Zoom: iBehave@uni-bonn.de

Don’t miss this insight into cutting-edge neuroscience!
#Neuroscience

Finally getting on top of new papers and god there are so many good ones. This from Lykken, Kanter, @edvardmoser.bsky.social & @m-bmoser.bsky.social is brilliant. Neuropixels showing differential phase shifts across grid modules drive place cell remapping. Elegant grid-to-place transformation story.

Experimenting with an AI-curated neuroscience feed. It scans the entire firehose in real time, prefilters for neuro/cogsci keywords, then a local LLM scores each post for scientific substance. A ranking algorithm blends that quality score with engagement to surface the best discussion. Try it out!

These reviews on neural manifolds are super relevant to today’s world of large-scale population recordings. How do we link circuits, population geometry, dynamics, and function?

“A neural manifold view of the brain” from Perich, Narain, and Gallego 2025

www.nature.com/articles/s41...

(1/2)

This paper has now been published in PRX Life! journals.aps.org/prxlife/abst...

In this work I developed a scaling theory for spiking neural populations using a renormalization group approach -- the 1st, to my knowledge, applied to models of spiking neurons. See the thread for a brief summary.