Catrina Hacker

When insects lose their minds: The fascinating world of parasitic fungi You might think mind control is the stuff of science fiction, but for many fungi it’s just what they do. Explore how parasitic fungi control the insects they infect.

You might think mind control is science fiction, but for some fungi it’s just what they do.

Explore the fascinating world of parasitic fungi and how they control infected insects in this week's post by @catrinahacker.bsky.social: pennneuroknow.com/2026/01/27/w...

#PsychSciSky #SciComm 🧠🟦 🧪

Our new paper (with @biotay.bsky.social) is out and on the cover story of @currentbiology.bsky.social !!!! Veronika, a Carinthian mountain cow flexibly uses a “multi-purpose tool” to scratch herself. A video and more information will follow in the comments.
www.cell.com/current-biol...

Writing is thinking

Outsourcing the entire task of writing to LLMs will deprive us of the essential creative task of interpreting our findings and generating a deeper theoretical understanding of the world.

Mysterious ‘Little Red Dots’ in Space Are Likely Cloaked Black Holes Puzzling red spots in photos from the James Webb Space Telescope are probably young supermassive black holes obscured by dense cocoons of gas

Puzzling red spots in photos from the James Webb Space Telescope are probably young supermassive black holes obscured by dense cocoons of gas

A unifying account of replay as context-driven memory reactivation A context-driven memory model simulates a wide range of characteristics of waking and sleeping hippocampal replay, providing a new account of how and why replay occurs.

Really thrilled that this paper led by @neurozz.bsky.social is now published in its final version in @elife.bsky.social!!

This is a memory-focused (as opposed to RL-focused) account of the detailed characteristics of forward and backward awake and sleep replay!

elifesciences.org/articles/99931

super excited to share my preprint with @meganakpeters.bsky.social stimulus familiarity shapes hierarchical structure learning and metacognitive dynamics🚀😊!!

osf.io/preprints/ps...

String Theory May Have a New Neuroscientific Niche Mathematical tools from string theory are giving scientists a new way to study the networking of neurons

Now on @sciam.bsky.social: A new study uses tools from string theory to improve models of branching neurons, blood vessels, and more. But was string theory's arcane math actually needed for the job?

www.scientificamerican.com/article/does...

Finally out! We studied the retinas of the longest-living vertebrate, the Greenland shark, and found that the retinas remain remarkably healthy in animals around 150 years old. What is the mechanism? It may be a highly efficient DNA repair system. Enjoy!
www.nature.com/articles/s41...

Scientists Catch Jellyfish and Sea Anemones Behaving in Surprisingly Human Ways Sea anemones and jellyfish don’t have brains, but the way their neurons behave during sleep shows some surprising similarities to humans

Sea anemones and jellyfish don’t have brains, but the way their neurons behave during sleep shows some surprising similarities to humans

Happy to see the final version of our article out! Brain–computer interfaces as a causal probe for scientific inquiry: Trends in Cognitive Sciences doi.org/10.1016/j.tics.2025.06.017

Our new paper in @sfnjournals.bsky.social shows different neural systems for integrating views into places--PPA integrates views *of* a location (e.g., views of a landmark), while RSC integrates views *from* a location (e.g., views of a panorama). Work by the bluesky-less Linfeng Tony Han.

Thank you! We don't extrapolate our results to consider fMRI, but I've been curious about it and would love to hear if you have any thoughts about the implications.

This work wouldn’t have been possible without the support, expertise, and patience of @nicolecrust.bsky.social and @brettlfoster.bsky.social, the generosity and helpfulness of @simonbohn.bsky.social, and the support of countless others.

(10/10)

A bridge connecting the left side labeled "Animal models, spikes, neural coding mechanisms" with pictures of a mouse, rat, and monkey, to the right side labeled "humans, field potentials, clinical application" with pictures of human patients and someone with a deep brain stimulator. A circle of arrows pointing both directions labeled "basic translational neuroscience" is over the bridge.

These results provide a framework for translating between spikes and LFPs, highlighting the scenarios likely to be fruitful for translation.

I call this “basic translational neuroscience” and I’m excited to continue with this approach in my research moving forward!

(9/10)

Table showing several previous studies that have and have not found alignment between spikes and LFPs. The table shows that variables encoded as magnitude codes and clustered pattern-of-spikes codes have consistently been shown to be aligned between spikes and LFPs, whereas those encoded as salt and pepper pattern-of-spikes codes have not.

And this rule generalizes beyond visual memory!

Sorting previous studies by whether they examined magnitude or pattern-of-spikes codes demonstrates that magnitude codes have consistently been found to be aligned between spikes and LFPs, while heterogenous pattern-of-spikes codes have not.

(8/10)

Schematic showing neural representations as vectors in high-dimensional space. Variables that change the vector magnitude are magnitude codes, whereas those that change the relative angles between vectors are pattern-of-spikes codes. The visual compares these two types of representations in spikes to field potentials in two cases. One where pattern-of-spikes codes are encoded by neurons where those with similar tuning are anatomically clustered (clustered) and another where they are not (salt and pepper). Magnitude codes and clustered pattern-of-spikes codes are aligned between spikes and HGA, whereas salt and pepper pattern-of-spikes codes are not.

We propose that it’s the neural coding scheme of the underlying spiking representation. HGA captures an average of local spikes. This increases signal for variables encoded as overall changes in local magnitude and “washes out” signals encoded as a pattern of heterogeneous responses.

(7/10)

Representational similarity matrix for five categories in spiking activity (left) and high gamma activity (right). The categorical representations (blocks along the diagonal) are clear and strong in spikes but not in high gamma activity.

But when we looked at the neural representations of object category, which are very strong in spiking activity, we found much weaker representations in HGA.

Why is alignment so striking for novelty, recency, and memorability, but not for category? 🤔

(6/10)

A plot of the performance of a decoder trained to distinguish novel from repeated images as a function of how many channels of high-gamma activity or neurons' spiking activity are included in the analysis. Performance grows noticeably faster for high-gamma activity than for spikes.

Not only were the signals well aligned, but we found that novelty signals were STRONGER in HGA than in spikes, requiring at least 4-fold less data to reached matched discriminability of novel from repeated images. In this case, you're better off with one channel of HGA than one neuron.

(5/10)

Plots comparing neural representations of novelty, recency, and memorability between spikes (top row) and high-gamma activity (bottom row). The results look very similar.

We started by examining a number of variables for which we’ve previously linked spiking neural representations to visual memory behavior: novelty, recency, and memorability.

For all three variables, we found a strong correspondence between the signals measured in spikes and HGA.

(4/10)

Visualization of spiking activity measured as grand mean firing rate as a function of time relative to stimulus onset next to a similar visualization for high-gamma activity. The two plots look very similar.

Others have suggested that high-gamma activity (HGA) captures a proxy of underlying spiking activity. We found that was true of our datasets as well, where HGA consistently captured spiking activity better than other frequency bands.

(3/10)

We leveraged datasets where we've previously reported on the spiking neural representations that support visual memory to ask a simple question: would we have made the same conclusions if we’d been limited to LFPs (similar to many human intracranial experiments)?

(2/10)

Neural representations of visual memory in inferotemporal cortex reveal a generalizable framework for translating between spikes and field potentials Translating neurophysiological findings requires understanding the relationship between common measures of brain activity in animals (spiking activity) and humans (local field potentials, LFP). Prior ...

🚨 New preprint!

Why do some insights from spikes translate to field potentials while others don't? In this paper we compare visual memory representations in spikes and LFPs to propose a general framework that answers this question.

www.biorxiv.org/content/10.6...

🧵 (1/10)

🧠🟦 🧠💻

Rethinking the centrality of brain areas in understanding functional organization Nature Neuroscience - Parcellation of the cortex into functionally modular brain areas is foundational to neuroscience. Here, Hayden, Heilbronner and Yoo question the central status of brain areas...

New Perspective from myself, Sarah Heilbronner and @myoo.bsky.social . “Rethinking the centrality of brain areas in understanding functional organization” in Nature Neuroscience. 🧵

rdcu.be/eVZ1A

Not playing around: Why neuroscience needs toy models Amid the rise of billion-parameter models, I argue that toy models, with just a few neurons, remain essential—and may be all neuroscience needs.

Amid the rise of billion-parameter models, I argue that toy models, with just a few neurons, remain essential—and may be all neuroscience needs, writes @marcusghosh.bsky.social.

#neuroskyence

www.thetransmitter.org/theoretical-...

The 10 Most Mind-Blowing Discoveries About the Brain in 2025 From glowing neurons to newborn memories, here are the most fascinating brain discoveries of 2025

From glowing neurons to newborn memories, here are the most fascinating brain discoveries of 2025

Reduced rank regression for neural communication: a tutorial for neuroscientists Reduced rank regression (RRR) is a statistical method for finding a low-dimensional linear mapping between a set of high-dimensional inputs and outputs. In recent years, RRR has found numerous applica...

Bichan Wu (@bichanw.bsky.social) & I wrote a tutorial paper on Reduced Rank Regression (RRR) — the statistical method underlying "communication subspaces" from Semedo et al 2019 — aimed at neuroscientists.

arxiv.org/abs/2512.12467

How basic neuroscience has paved the path to new drugs A growing list of medications—such as zuranolone for postpartum depression, suzetrigine for pain, and the gepants class of migraine medicines—exist because of insights from basic research.

“Basic neuroscience hasn’t produced new drugs.” 💊

Not true - zuranolone (PPD), suzetrigine (pain), gepants (migraine), and more... were born out of a long arc of studies in the lab.

I wrote a Perspective on why this matters. @thetransmitter.bsky.social

www.thetransmitter.org/drug-develop...

Last week, we published the surprising result below (visual cortex == hippocampus).

Today, we've posted a revised preprint showing something that's actually different between the two structures. Evidence for a novel medial temporal lobe computation!

www.biorxiv.org/content/10.1...

Excited to see this work led by Barnes Jannuzi published!

With a hard-won dataset, Barnes shares the surprising result that visual cortex looks more like the hippocampus than you might think!

#neuroskyence

Neural dynamics outside task-coding dimensions drive decision trajectories through transient amplification Most behaviors involve neural dynamics in high-dimensional activity spaces. A common approach is to extract dimensions that capture task-related variability, such as those separating stimuli or choice...

“Our findings challenge the conventional focus on low-dimensional coding subspaces as a sufficient framework for understanding neural computations, demonstrating that dimensions previously considered task-irrelevant and accounting for little variance can have a critical role in driving behavior.”