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Catrina Hacker @catrinahacker.bsky.social · 05/01/2026

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)

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.

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Catrina Hacker @catrinahacker.bsky.social · 05/01/2026

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)

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.

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Catrina Hacker @catrinahacker.bsky.social · 05/01/2026

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)

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.

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Catrina Hacker @catrinahacker.bsky.social · 05/01/2026

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)

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.

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Catrina Hacker @catrinahacker.bsky.social · 05/01/2026

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)

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.

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Catrina Hacker @catrinahacker.bsky.social · 05/01/2026

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)

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

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Catrina Hacker @catrinahacker.bsky.social · 05/01/2026

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)

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.

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Catrina Hacker @catrinahacker.bsky.social · 14/08/2023

🎶 How does your brain's symphony stay in sync? Is there one conductor leading the symphony or do nearby neurons listen to each other to coordinate their activity? Learn more in my recent post for Penn's Brains in Briefs series. www.upennglia.com/briefs/bib-brain-… #neuroskyence

Image of a brain made of music notes next to the words "Keeping Your Brain's Symphony in Sync".

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