Day 5 has an exciting lineup 🤩 @smikulovic.bsky.social talks about helping mice @nikolaskaralis.bsky.social neuromodulatory circuits @acnavasolive.bsky.social SWR across species And me 💁🏽‍♀️ about theta & movement #neuroscience #NeuralMechanismsofCognitiveFunction sites.google.com/isd.org.br/i...

Talk 19: Next up is @acnavasolive.bsky.social talking about her impressive research on sharp wave ripple analysis across species in health and disease. Andrea is a phenomenal computational neuroscientist 🤩 and I am so excited to hear her speak for the first time!

Cont.. @acnavasolive.bsky.social discussing important considerations for identifying hippocampal ripple events in a recording session and how a convolutional NNs might help their identification. ✅

This is a great application of our topological analysis of the iEEG waveform space to detect and differentiate interictal discharges, ripples and fast ripples in temporal lobe epilepsy in human. Don’t miss the 📝!! www.biorxiv.org/content/10.1... and the Github 👇🏼

Here is our paper www.nature.com/articles/s41...

New preprint about SWRs, this time in HUMANS! 🤩 With @anna-maslarova.bsky.social and @JiyunShin, from @NYU, we’ve (i) developed a pipeline to analyze SWRs in intracranial EEG, and (ii) created ripmap, an open-source toolbox that allows versatile inspection and curation of events 🧵

2025=(1+2+3+4+5+6+7+8+9)² = 1³+2³+3³+4³+5³+6³+7³+8³+9³. 👉🏼 elpais.com/ciencia/cafe...

More human neurophysiology out today from the Geiger Lab. *Analogue* neuronal output modifies synapses for consolidation during sleep states. With such beautiful data and interpretation it’s easy to forget that the recordings are heroically tough. Fantastic research from @fxmittermaier.bsky.social

With @mojtabart.bsky.social, we applied this to human tissue, and saw that human CA3 cells appear to receive 5 times more DG inputs than mouse cells do! This finding has a lot of potential for powering up our idea of hippocampal computations.

Finally, we saw that dentate gyrus input to human CA3 (the ‘teaching’ signal) seemed very high. Luckily, we have @mojtabart.bsky.social and Hans Danzl as neighbours. Their incredible LICONN technology allows light microscopy based connectomics. See the preprint here: www.biorxiv.org/content/10.1...

Using a Hopfield-like model, @acnavasolive.bsky.social showed that expanding brain size by increasing neuronal number (sparse connectivity) is far better for memory capacity than aiming for dense connectivity (and more inputs per cell). This has some interesting philosophy for brain scaling rules!

In a fantastic collaboration with Prof Karl Rössler (MedUniWien), we applied multicell patch-clamp to human hippocampus resected from epilepsy patients. Some samples show sclerosis (disease-led cell loss), but many are perfectly intact. This is the closest to 'wildtype’ human physiology we can get..

We explored CA3, which in theory stores and retrieves memories from interconnected ensembles of pyramidal neurons. With Victor Vargas-Barroso and Rebecca Morse, we looked for these networks using octopatch. From 8 patients and 56 slices we found just 10 connected pairs! (under 1% connectivity)..

To touch base with better characterised circuits, we recorded in neocortex and saw the same dense connectivity that @yangfanpeng.bsky.social, @alleninstitute.bsky.social and others see. In fact CA3 wasn’t just sparse, but gets sparser from mice to humans - opposite scaling to neocortical circuits!

We had the first view on human hippocampal synaptic pairs, and they look slow and integrating as we would expect (perfect for associations!). However they were also much more reliable and precise than seen in rodent research.

As you may expect, human neurons were larger than mouse cells, but spine density was a lot lower, so the number of inputs from other neurons in the recurrent network doesn’t change so much. Low spine density and reliable synapses have been seen in other brain areas, so may be human circuit features

A bigger difference between human and mouse brains is the number of neurons. This has gone up by about 17 times in CA3! By (very) simple maths, our anatomy data predicts connectivity in a random recurrent network to be pretty much in line with what we record experimentally for CA3 across species.

We think this explains the connectivity and circuit scaling between brain areas - dense local circuits in neocortex, while hippocampal CA3 forms something like one big recurrent network - perfect for associating all the hippocampus’ incoming info. Circuits made to measure!

Is neuroscience research really working to understand the human brain? Or do we get lost in mouse cognition? I’ve asked myself this a lot since starting to work with human tissue. Our first Jonas Lab foray into untangling human hippocampal circuits is now online! www.cell.com/cell/fulltex... 1/a few

Now that we’re on Bluesky, it’s a good time to bring back #badsciencedrawings – a collection of figures that prove that science is more science than art. Before Biorender, all we had was MS Paint and a dream. But ovals, lines, and lightning bolts were all we needed Morales-Botello et al., 2012

So nice to see science sparking again 🦋 Hello everyone!

This paper is incredible. EM level connectomics on a light microscope. www.biorxiv.org/content/10.1...

Following the great migration! Leading the @lmprida.bsky.social lab. Hippocampal circuits, oscillations and memory.

go.bsky.app/LdtUYZS I created a starter pack for the growing community of hippocampus physiologists that have joined the great migration. Also included the physiology-adjacent. Tell me who I haven’t found yet