Neuropixels Opto is out! 🎉🥳🍾🚨 Congratulations to a fantastic team! nature.com/articles/s41592-0…

If you’re at Cosyne, catch @maxime_beau at the workshop on “Bridging the gap between cell types and spike trains"! Monday 16th March:   celltypestospikesworkshop.gi…

The culmination of 8 years of teamwork: the IBL brain-wide map and prior papers have been published in @Nature today! We are so happy to be part of this great collaboration 🎉🎉🎉 doi.org/10.1038/s41586-025-0… doi.org/10.1038/s41586-025-0…

This is real team science: 5 labs expert in recordings from mouse and monkey cerebellum joined forces to make the project happen. Thanks to a great team, led by @1DanaCohen1, @Steve_Lisberger, @dkneurolab , @theHullLab, @Neuralcomplab, and Javier Medina.

We created a carefully validated “ground truth” library of over 200 optogenetically tagged cerebellar neurons and used this to train a classifier with semi-supervised generative modelling to recognize neuron types based on their electrophysiological signatures.

We tackled a decades-old problem in systems neuroscience: the identification of neurons in extracellular recordings in vivo.

Our “Neuro-AI” paper has just been published in Cell. It describes a new deep learning-based classifier for identifying different cell types in Neuropixels recordings: doi.org/10.1016/j.cell.2025.…

Congratulations to the team! @omgauld, @paqio, @llerussell, @hwpdalgleish, Maya Iuga, @Fran_Sacadura, @ArndRoth & @BeverleyAClark

This result suggests that non-coding neurons can still convey important information, which may have significance for builders of brain-machine interfaces as it widens the pool of neurons available to enhance behavioral performance.

Second, the recruitment of this non-coding pool by optogenetic stimulation can enhance perception. This means that this latent pool of neurons (the “dark matter”) can actually be useful for sensory processing!

We therefore provide two key new insights into the mechanism and significance of sparse coding. First, the sparse code is enforced by selective targeting of powerful inhibition, which predominantly suppresses non-coding neurons.

The all-optical experiments also revealed that these non-coding (i.e. normally silent) neurons are selectively targeted by inhibition recruited during sensory stimulation – in other words, they are preferentially suppressed by sensory stimulation.

All-optical interrogation revealed that we can produce a behavioral bias which depends on the number of neurons activated. Moreover, what is fascinating is that the behavioral effect is dominated by activation of non-coding neurons.

We then performed two-photon imaging during the task and confirmed that coding of whisker input is sparse – in other words, most neurons are silent! This sparse code aligns with work from many groups (e.g. Olshausen & Field, Brecht, Helmchen, Svoboda, Barth & Poulet).

We used one-photon optogenetics to show that barrel cortex is required for this task and that the processing window is very brief – less than 100 ms!

Most neurons in the cortex remain silent, even during sensory stimulation and behavior. What are these silent neurons good for? This is known as the “dark matter problem” of the brain. We address this problem in our @NeuroCellPress study led by @omgauld doi.org/10.1016/j.neuron.202…

Fantastic work, especially by Christina Buetfering and Zihui ‘Zoe’ Zhang! Congratulations to all of the authors, and thanks to the funders, the referees for their helpful feedback, and the whole lab for their support. nature.com/articles/s41593-0…

We developed a new texture discrimination task and combined two-photon imaging and two-photon targeted optogenetics (“all-optical interrogation”) to show that neurons encoding decision signals can be detected in S1 – and that their activity can drive behavior.

Where do decisions get made? Decisions about sensory information are thought to arise high in the cortical processing hierarchy. Our @NatureNeuro study challenges this, showing that decision signals consequential for behaviour are found at the earliest cortical processing stages.

Thanks also to our friends, collaborators, and reviewers that helped us with this project and provided feedback on the protocol!