Rebecca Jordan

@beckyjordan.bsky.social

Neuroscientist and painter | group leader at SIDB, University of Edinburgh | SCGB BTI Fellow | Prediction and Plasticity lab

Sadly, not an April Fool & also not surprising. “An analysis conducted by @nature.com in collaboration with Grounded AI suggests that at least tens of thousands of 2025 publications, including journal papers, books, & conference proceedings, probably contain invalid references generated by AI.” 🧪

Hallucinated citations are polluting the scientific literature. What can be done?

Tens of thousands of publications from 2025 might include invalid references generated by AI, a Nature analysis suggests.

nature.com

First preprint from the lab! Using intracellular recordings & analysis of 2-photon imaging data, we show that spiking & neuromodulatory input during experience drive a reorganization of visuomotor inputs in V1 layer 2/3 neurons, consistent with enhanced visuomotor cancellation - bioRxiv link below.

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Peer review would be easier if we stopped treating it like combat with the authors. Results want to be free, and perfect is the enemy of good. Do the data support the conclusions? If so, that’s enough. A paper isn’t a blank slate for projecting your own ideas.

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

Please share: Postdoc position available in a new collaborative SNSF project with @georgkeller.bsky.social at @fmiscience.bsky.social using a cross-species approach in humans and mice to investigate the cortical circuit mechanisms underlying schizophrenia. Apply at karriere.upk.ch/Postdoctoral...

Stellenangebot Postdoctoral Researcher (PostDoc m/f) 100% Translational Psychiatry Lab for 2 Years bei Universitäre Psychiatrische Kliniken Basel UPK

Forschung in Klinik für Erwachsene (UPKE)

karriere.upk.ch

Thrilled to share our new Adesnik lab paper!! Using holography in excitatory & inhibitory neurons, we reveal how a single cortical circuit can both complete and cancel predictable sensory activity, sharpening representations 📄https://www.biorxiv.org/content/10.1101/2025.08.02.668307v1 🧵

Defending science in public we often talk about 'peer reviewed science'. But could this framing contribute to undermining trust in science and holding us back from improving the scientific process? Instead, let's talk about the work that has received the most thorough and transparent scrutiny? 🧪

The construction of the interpretation, however, appears to be built on the assumption that if something is predictable in principle it will be predicted by the brain. I would look to behavioral relevance to make guesses about what the brain predicts.

In the battle against brain disease, researchers can now rely on a new arsenal of genetic tools – The Armamentarium. Together with scientists from across the NIH BRAIN Initiative, we’ve created and published over 1000 new enhancer AAV vectors. 🧠📈

So the tired "preprints shouldn't be cited because they're not peer reviewed" meme is back. Reminder these frequently cited items aren't peer reviewed either: Editorials Books Reviews [some not all] News reports Data Code Websites Social media posts Citations are just links...intent varies 1/2