Mora Ogando

Assistant Professor - Molecular, Cellular, and Developmental Neuroscience - Department of Neuroscience University of California, Berkeley is hiring. Apply now!

Come join our new Department of Neuroscience @ucberkeleyofficial.bsky.social as an Assistant Professor! aprecruit.berkeley.edu/JPF05041

Guided by Noise: Correlated Variability Channels Task-Relevant Information in Sensory Neurons Shared trial-to-trial variability across sensory neurons is reliably reduced when perceptual performance improves, yet this variability is low-dimensional, so it could be ignored by an optimal readout...

One of the joys of being a scientist is the ability to think about a problem for a long time. Our new preprint solves a mystery that has been bugging me since I was a graduate student (which was, ahem, a while ago). 🧪🧠🧵1/
www.biorxiv.org/content/10.1...

So glad this technology is reaching more and more research labs! It's an incredible tool to crack neural codes!!

100% agreed! Thanks so much for sharing :)

Also, huge thanks to the Ho Yin Chau, @apalmigiano.bsky.social & @kenmiller.bsky.social for the many MANY discussions over this complex data and the possible circuit computations! It's been a huge learning opportunity 💙

Also big thanks to Dr. Lucia Rodriguez (x100), @danfeldman.bsky.social @amarinburgin.bsky.social and Dr. Kaeli Vandemark for their super valuable feedback on the manuscript.

Huge thanks to all the authors!!, especially @lamiaeadm.bsky.social who designed and built this powerful all-optical system, and to the great Adesnik Team!!

We think feature-specific recurrent inhibition may be a general cortical strategy to minimize redundancy, suppress ambiguity, and sharpen internal models of the world.
Read the full story: www.biorxiv.org/content/10.1...

Our results:
🔘Identify a feature-specific PC→SST→PC motif
🔘Show how it can switch from completion to cancellation (unifying previous findings)
🔘Demonstrate how feature-tuned recurrent inhibition sharpens cortical codes

So WHY is the brain wired with a like-to-like inhibitory loop?
Stimulating co-tuned SSTs while showing their preferred visual input:
-Reduces evidence for flanking orientations (consistent with explaining away)
-Preserves evidence for correct orientation
-Boosts discriminability

In fact, directly activating co-tuned SST ensembles alone is sufficient to remove input-matching representations in the absence of visual input.

Feature completion can be explained by the well-known like-to-like PC-PC connectivity in V1, but where does the feature-specific suppression come from?
-PCs recruit co-tuned SSTs (not PVs)
-SSTs, in turn, suppress co-tuned PCs → a “like-to-like-to-like” inhibitory loop

Same microcircuit, opposite computations, depending on input sparsity. This partially reconciles previous contradictory findings using similar tools. But how does this happen?

Using all-optical physiology in awake mice we photostimulated orientation-tuned PC ensembles in V1 in the absence of visual input, and we found:
Small PC ensembles → dominant feature suppression
Large PC ensembles → dominant feature completion

This means that both excitatory AND inhibitory connections in the cortex are highly structured: They store information (statistical regularities → “an internal model”) that can be used during sensory processing.

We show that this dual capacity is present in the same circuit with two components:
(1) Like-to-like connections between PCs (for pattern completion)
(2) A newly discovered circuit motif: Reciprocal like-to-like connections between PCs and SSTs (for pattern cancelation)

Brain circuits can use learned statistical regularities to enable completion or cancelation of predictable signals, but how?

Thrilled to share our new Adesnik lab paper!!
Using holography in excitatory & inhibitory neurons, we reveal how a single cortical circuit can both complete and cancel predictable sensory activity, sharpening representations
📄https://www.biorxiv.org/content/10.1101/2025.08.02.668307v1
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