Dan Levenstein

Liftoff: New lab alerts Learn about early-career scientists starting their own labs.

In this month’s “Liftoff,” @qlu.bsky.social talks about how he will use a “slide stack” to keep up with his students’ projects, and @dlevenstein.bsky.social shares how he will continue a time-honored tradition he learned in György Buzsáki’s lab.

By @franciscorr25.bsky.social

bit.ly/4apOZm9

🦆🦆🦆😍

one of the active states is definitely bursting ;) (check e.g. CA1 during NREM... biggest density of burst-timescale ISIs).

most regions/brain states have multiple AS modes, there are others that are slower than bursting (e.g. in the 5Hz-100Hz range). bursts are one animal in the modal zoo 🦁🐯🐻😱

Yes absolutely! Love Chai's paper. We actually met at that 2020 Cosyne and hit it off. I think it's highly relevant to GS spiking ;)

I shall not attempt further to define the kinds of material I understand to be embraced within that shorthand description (“representation”), and perhaps I could never succeed in intelligibly doing so. But I know it when I see it and also you better not tax me unless I have it.

Thanks Julie! Hope everything is going well down at Princeton :)

Just wait for whiteboard meetings with students days 👍

IMO there's quite a bit of follow up work to do re: AS/GS modes on a spike-by-spike basis (notice everything in the paper is statistics over conditional ISI distributions), and how they relate between cells. We tried a few different approaches and I wasn't satisfied with any of them/ran out of gas⛽️⌛️

Thanks Jay! We tried to answer this question and from what I remember (it's been a while), it looked like yes - GS spikes are more correlated than you would expect from asynchronous spiking. However, I remember it being challenging to say anything confidently and this didn't make it to the paper...

What about GS mode? We think the brain uses GS mode to maintain its own dynamics. A constant buzz of GS spikes keeps cells near-threshold, and maintains a diverse population of spike sequences to select from and reinforce when associated with salient behaviors or stimuli.

Why might the brain work like this? Our idea is that having discrete modes supports functional multiplexing. Spiking at specific timescales could resonate with different downstream readers and engage intracellular/synaptic mechanisms with different functional implications.

As a proof of principle, we built a spiking model in which cells used iSTDP to balance at a cell-specific rate. In the standard balanced condition, cells only had GS mode. When we adjusted network parms to other activity regimes, AS modes emerged but notably, GS was preserved.

So what do we think is going on here? TL;DR: AS modes reflect a cell's engagement with various region/state-specific activity patterns. GS mode reflects E/I balanced inputs, and each neuron has a different balance point that sets its GS rate.

(see also: pubmed.ncbi.nlm.nih.gov/36086892/)

On the other hand, neurons spent the most time in GS mode and their mean rate mainly reflected of GS rate (rather than AS rate/weight). GS rate was conserved across WAKE and NREM, suggesting it's characteristic feature of a cell.

We reproduced this observation in the thalamic head direction system, and even found that a “modal” encoding model, in which probability of AS mode varied with HD, fit HD cells better than a continuous rate model.

What happens when a cell encounters its favorite stimulus? We conditioned the ISI distribution of CA1 cells on position: an ISI “tuning curve”. While the rate tuning curve is smooth, the ISI tuning curve is discontinuous. Occupancy of AS modes is what increases in the place field

When we fit ISI distributions from 6 different regions, many AS modes corresponded to known activity patterns: θ in CA1, δ in V1, ɣ-timescale modes that were associated with local interneuron activity. Everywhere had a GS mode: low rate irregular spiking at a cell-specific rate.

To formalize this, we fit the ISI distributions to a mixture model, in which each mode is parameterized by the timescale of spiking (the mean ISI), variability (CV), and weight (how many ISIs come from that mode). This decomposition gives a modal “fingerprint” for each cell.

This led us to consider the following picture: spiking can be attributed to a ‘ground state’ (GS) mode of irregular spiking at a cell-specific low rate, and a repertoire of ‘activated state’ (AS) modes which are common to neurons in a given brain region/state.

thanks Adrian!

Cells from each region in our dataset had ISI distributions with qualitatively different shapes. But basically they all had this property (right tail slides in with mean firing rate. Characteristic shape otherwise stays the same but can change occupancy).

What’s so interesting about this plot? Notice: vertical bands of ISI density at the same timescales across cells (⬛️). But at long ISIs, density at a cell-specific timescale (🟫). In high rate cells the long ISIs are shifted to the left, but the shape at shorter ISIs is ~the same

Which brings me to this plot. It shows the inter-spike interval distributions from a CA1 dataset. Each row is the ISI distribution from a single cell (in wake and NREM sleep). When I showed this to GB (now 10 years ago😬) he basically said "stop everything, work on this" (I didn’t😅)

😂😂😭😭

TFW you do all the things they say not to: “don’t leave things unfinished when you start a postdoc”, “don’t get discouraged by tough reviews”, “don’t put off revisions”, “don’t lose easy access to your dataset”

I admire GB’s patience/persistence. Could have easily been a biorxiv-4-life paper.

Another mystery: cells have widely different rates (you may have heard of this lognormal thing). But what does this mean? Maybe the dynamics are similar but shifted. Maybe a cell responds to stimuli with higher rate, or to more stimuli. Maybe… they just have different baselines?

But there are some issues with this. e.g. Cells in across the brain and in different contexts have different spiking statistics. e.g. The brain doesn't know or care about smooth trial-averaged rate.

There’s a pretty standard model in neuroscience that goes something like this: cells have an underlying value called a rate, which changes smoothly over time to encode/compute relevant variables, and is communicated to downstream cells with Poisson-like irregular spiking.

Neuronal spiking in the mammalian forebrain is dominated by a heterogeneous ground state Neuronal firing patterns have significant spatiotemporal variability with no agreed-upon theoretical framework. Using a combined experimental and mode…

With this one in print, I think I finally earned that PhD... 😅
Presented for the first time at the cosyne when the world ended (March 2020). I'll bring over a summary thread from twitter when it was still twitter...

www.sciencedirect.com/science/arti...

The Problem with Counterfeit People

But what about foxes and hedgehogs? 🤔