Richard Naud

Richard Naud

@neuronaud.bsky.social

just trying to say true and-or kind things

264 followers281 following47 posts

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How are the fluctuations in electric field organized across the whole brain? We analyzed the 3.4 M samples from the international brain lab using a combination of deep learning and graph theory. We found a surprising structure made of communities and landmarks. doi.org/10.1101/2025...

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The Ferrier Medal and Lecture 2027 is awarded to Professor Kenneth Daniel Harris for his contributions to neuroscience by unifying experimental neurophysiology with advanced analytical methods and for pioneering methods that allow recording from thousands of neurons. #RSMedals https://bit.ly/4qCJZRo

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These images of First Nations community members evacuating Collins, Ontario on motorboats are absolutely surreal www.facebook.com/reel/1548244...

Luke LeBrun@lukelebrun.ca

These photos of FNs community members fleeing their homes by boat are genuinely apocalyptic (Photo via Wayne Wastaken of Collins, Ontario, who says his “hometown is gone”) www.facebook.com/share/r/1ArN...

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So excited to be part of this one! Now officially out in Annual Review of Neuroscience 🎉 www.annualreviews.org/content/jour...

annualreviews.orgEcological Visual Processing in the MouseVisual systems evolved to extract behaviorally relevant information while animals move through and interact with their world. Such ecological vision differs fundamentally from standard laboratory paradigms in many key aspects, making this a much harder problem for the brain to solve, and for the neuroscientist to study. However, emerging technologies and experimental approaches have enabled investigation of visual computations under these ecological conditions. These approaches are particularly powerful in the mouse, combining well-developed genetic tools, high-throughput recordings, and quantifiable ethological tasks. Here we review computations that are engaged in ecological contexts, including active sensing, motion processing, scene analysis, distance estimation, and spatial perception. We delineate experimental approaches that engage these computations and synthesize current understanding of their neural implementations based on mouse research. These studies reveal how ecological vision engages distinct processing strategies and novel neural circuitry, while highlighting the vast territory that remains unexplored in understanding real-world visual computation.
Spencer LaVere Smith@spencerlaveresmith.bsky.social

Ecological Visual Processing in the Mouse - with @michaelgoard.bsky.social @crisniell.bsky.social @mbeyeler.bsky.social Visual computations that are engaged in ecological contexts: active sensing, motion processing, scene analysis, distance estimation, and spatial perception. doi.org/10.1146/annu...

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When reality surpasses fiction! Top: fictional forecast for August 2050, broadcast by French TV in 2014 to warn about the consequences of global warming. Bottom: Real French forecast for yesterday, June 22, 2026.

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Recent work from the lab: big contrast between the synaptic dynamics of excitatory and inhibitory synapses!

bioRxiv Neuroscience@biorxiv-neursci.bsky.social

Functional Continuum of GABAergic Synaptic Dynamics Reflects Genetic Identities https://www.biorxiv.org/content/10.64898/2026.06.09.731181v1

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Going from neural activity to blood flow just became easier! Two brainwide populations, each with its neurovascular coupling. (But going backwards... is now a tad more complicated.) By @agnesland.bsky.social & team. Thanks @intlbrainlab.bsky.social @wellcometrust.bsky.social @simonsfoundation.org

Agnès Landemard@agnesland.bsky.social

How does blood flow relate to brain activity? We discovered that it reflects two neural populations affected oppositely by arousal. Together, they explain neurovascular coupling in all brain regions and brain states! Out today in Nature: rdcu.be/fdC2A @uclbrainscience.bsky.social

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Stoked this is finally out! We ask: how can we simulate the brain from the bottom up? It's not sufficient to grab the connectome and wire it up in silico! We need 1) ultrastructure 2) (causal) calibration data 3) functional data. Then we can build a simulation compiler. 1/

Konrad Kording@kordinglab.bsky.social

Preprint out arguing that we should build the techology to translate (compile) molecularly annotated connectomes into dynamics. I think this is incredibly important. arxiv.org/abs/2603.25713

An important piece on error-based learning in biological networks

Aaron Milstein@neurosutras.bsky.social

Our latest publication grapples with how the brain could implement gradient descent by sending learning targets top-down, gating plasticity with dendritic inhibition, and updating synaptic weights with biologically observed learning rules like BTSP. www.cell.com/cell-reports...

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I’ll be presenting this work (plus some exciting new follow-up analyses!) tonight at poster [1-019], “A unifying theory of receptive field heterogeneity predicts hippocampal spatial tuning.” Come by and say hi! #cosyne26

Zach Cohen@zachcohen1.bsky.social

Finally up on bioRxiv! A new study with @jdrugowitsch.bsky.social where we do a deep dive into the coding benefits of receptive field heterogeneity and come up with new ways to measure receptive fields! www.biorxiv.org/content/10.1... [1/n]

"neuro AI reckoning: can we move beyond the metaphor?"https://groundedneuro.substack.com/p/22-years-of-brain-science-what-cosyne?r=6l8iaw&utm_campaign=post&utm_medium=web&triedRedirect=true&_src_ref=go.bsky.app

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I recommend this school to anyone interested in computation in the brain - it receives excellent reviews year after year, it is highly interactive and social, and the setting is remarkable.

Soledad Gonzalo Cogno@soledad-gcogno.bsky.social

Applications are now open for the summer school: 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐚𝐥 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐢𝐧 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐍𝐞𝐮𝐫𝐨𝐬𝐜𝐢𝐞𝐧𝐜𝐞 🧠 Apply before March 15: www.compneuronrsn.org 📍 Located in beautiful Eresfjord 🇳🇴 🗓️ Between July 6-24 Supported by the @kavlifoundation.org In collaboration with @kavlintnu.bsky.social

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New paper alert! 🚨 We found that the brain's compass is remarkably stable at two scales 1️⃣ the system maintains its internal organization for weeks 2️⃣ It "remembers" its orientation for weeks, even after a single visit This may be key to how the brain aligns its other maps. Paper: rdcu.be/e3waP

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There are approximately 1000 research groups on the #NIH campuses, including Steve Rosenberg's research group, which develops groundbreaking T-cell therapies for cancer. Effectively all of those groups were suddenly prohibited from buying any (!) supplies last year. That is on the leadership.

Aaron Rupar@atrupar.com

BHATTACHARYA: I do not believe patients' care was disrupted. If there were disruptions, it's the responsibility of the researchers, not the NHS HASSAN: That is an unacceptable and outrageous response. You can make an edict - 'Don't disrupt care!' - but that can be a very complicated thing

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We think cortex might function like a JEPA. It looks like prediction errors in layer 2/3 are not computed against input (as is the idea in predictive processing), but against a representation in latent space (i.e. like in a JEPA arxiv.org/abs/2301.08243 or RPL doi.org/10.1101/2025...).

arxiv.orgSelf-Supervised Learning from Images with a Joint-Embedding Predictive ArchitectureThis paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Archi...
Anna Vasilevskaya@loghyr.bsky.social

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.

biorxiv.orgA functional influence based circuit motif that constrains the set of plausible algorithms of cortical functionThere 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

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The hippocampal map has its own attentional control signal! Our new study reveals that theta #sweeps can be instantly biased towards behaviourally relevant locations. See 📹 in post 4/6 and preprint here 👉 www.biorxiv.org/content/10.6... 🧵(1/6)

biorxiv.orgAttention-like regulation of theta sweeps in the brain's spatial navigation circuitSpatial attention supports navigation by prioritizing information from selected locations. A candidate neural mechanism is provided by theta-paced sweeps in grid- and place-cell population activity, which sample nearby space in a left-right-alternating pattern coordinated by parasubicular direction signals. During exploration, this alternation promotes uniform spatial coverage, but whether sweeps can be flexibly tuned to locations of particular interest remains unclear. Using large-scale Neuropixels recordings in freely-behaving rats, we show that sweeps and direction signals are rapidly and dynamically modulated: they track moving targets during pursuit, precede orienting responses during immobility, and reverse during backward locomotion — without prior spatial learning. Similar modulation occurs during REM sleep. Canonical head-direction signals remain head-aligned. These findings identify sweeps as a flexible, attention-like mechanism for selectively sampling allocentric cognitive maps. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, Synergy Grant 951319 (EIM) The Research Council of Norway, Centre of Neural Computation 223262 (EIM, MBM), Centre for Algorithms in the Cortex 332640 (EIM, MBM), National Infrastructure grant (NORBRAIN, 295721 and 350201) The Kavli Foundation, https://ror.org/00kztt736 Ministry of Science and Education, Norway (EIM, MBM) Faculty of Medicine and Health Sciences; NTNU, Norway (AZV)

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