7/
@klensj.bsky.social @mariannefyhn.bsky.social @hafting.bsky.social in the Fyhn lab. Paper: doi.org/10.64898/2026.03.31.715401

6/ The "brakes on plasticity" framing captured something real, but PNNs may be more informative as features of within-class PV specialization than as general plasticity regulators. Where a cell sits on this axis likely shapes how it participates in circuit dynamics.

5/ PNN-negative PV neurons look different — expressing neuropeptides and GABA-A subunits more typical of Sst interneurons. Similar across-class continua have been described transcriptomically for broad interneuron populations; the PNN gives us a physical handle on one within a class.

pyDESeq2 (within animal, PV basket cells +PNN vs -PNN, 69 donors). volcano plot, p=0.001 cutoff, l2fc 0.5 indicated with vertical gray line. Genes included in xenium spatial panel indicated in red, non-xenium genes in blue. Genes of interest highlighted by name.

4/ PNN-positive PV neurons are the mature fast-spiking specialists: Kv3 channels, Grin2a (mature NMDA), Gabra1 (fast GABA-A), oxidative phosphorylation, gap junctions (Gjd2). The canonical basket-cell phenotype, molecularly.

3/ We combined Xenium spatial transcriptomics with post-hoc WFA staining in adult mouse cortex. 378,349 cells, same-section PNN quantification. To extend beyond our 297-gene panel, we trained a classifier (AUC = 0.87) and projected onto Allen scRNA-seq: 34,326 PV neurons, genome-wide.

Expansion microscopy image of perineuronal nets (unpublished).

1/ 97% of cortical PNNs are on PV interneurons. But PNN-positive and PNN-negative PV cells don't split into two groups — they sit at different ends of a transcriptional continuum of fast-spiking specialization. New preprint 🧵

The easy fix for me was to change the loading in line 1320 from "if 'meanImg' in self._ops and self._mean_im is None:" to "if 'meanImg' in self._ops:".
Everything else seems great!

I was doing exactly this last night, but hadn't gotten to the gui part yet..! Thanks for sharing! One small issue (in python at least) is that the mean image does not reload when you open a new file, so it overlays Ca2+ spikes on the wrong background.

The reactivation content was way more specific to the experience in controls.
Overall, our data shows that both reactivations and consolidation relies on intact PV+ activity locally in the cortex.

Next we looked at the activity in post-training rest. Reactivations in controls were biased to the rewarded cue and persisted for hours, but with hM4Di activation the reactivations barely happened, despite higher overall activity in the population

We used RiboL1 jGCaMP8 to image large populations of neurons during training and rest. Controls developed a bias to the rewarded cue while the responses in hM4Di mice remained exactly like the naive state

We found no effect on cortico-hippocampal synchrony - ripple and spindle activity was intact and synchronized, showing that the effects only applied to the local network

Using this setup we show that post-training reduction of PV+ activity completely prevented learning. On off-days with saline injections the mice improved, but reverted to chance levels after a single hM4Di activation

Are cue-specific reactivations in the cortex necessary for consolidation and learning? To address this we targeted neural activity in POR after daily training in a Go/NoGo task

Local inhibitory circuits mediate cortical reactivations and memory consolidation Reducing inhibitory activity after training prevents cortical reactivations and memory consolidation.

New paper out with @hafting.bsky.social and @markandermann.bsky.social lab on reactivations and memory consolidation : www.science.org/doi/10.1126/...

New preprint out in eLife! Neurons in medical entorhinal cortex (MEC) develop responses to visual cues and reward as mice learn a visual Go/NoGo task. elifesciences.org/reviewed-pre...

Brainstem sensing of multiple body signals during food consumption Studies of body-to-brain communication often examine one stimulus or organ at a time, yet the brain must integrate many body signals during behavior. For example, food consumption generates diverse or...

I’m so excited to share our preprint on how brainstem neurons sense and integrate multiple body signals during food consumption. We imaged 1000s of neurons across the lateral parabrachial nucleus (LPBN) in behaving mice. www.biorxiv.org/content/10.1...

Task and Behavior-Related Variables Are Encoded by the Postrhinal and Medial Entorhinal Cortex During Non-Spatial Associative Learning The medial entorhinal cortex (MEC) is pivotal in spatial computations and episodic memory. In particular, an animal’s position can be decoded from the activity of entorhinal grid cells. However, it re...

In this preprint we see how activity in entorhinal cortex change during learning a non-spatial visual association task. Neurons in the MEC initially exhibited weak responses to visual cues but gradually developed strong tuning toward the rewarded trials. doi.org/10.1101/2024...