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Mora Ogando

@moraogando.bsky.social

110 followers 200 following 16 posts

Postdoctoral researcher at Hillel Adesnik's lab in UC Berkeley. Interested in causally understanding learning and memory

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Mora Ogando @moraogando.bsky.social · Aug 5

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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Reposted by Mora Ogando

Marla Feller @mfeller.bsky.social · Aug 20

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

Assistant Professor - Molecular, Cellular, and Developmental Neuroscience - Department of Neuroscience

University of California, Berkeley is hiring. Apply now!

aprecruit.berkeley.edu

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Reposted by Mora Ogando

Marlene Cohen @marlenecohen.bsky.social · Aug 15

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...

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...

www.biorxiv.org

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Mora Ogando @moraogando.bsky.social · Aug 6

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

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Mora Ogando @moraogando.bsky.social · Aug 5

100% agreed! Thanks so much for sharing :)

Mora Ogando @moraogando.bsky.social · Aug 5

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 💙

Mora Ogando @moraogando.bsky.social · Aug 5

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.

Mora Ogando @moraogando.bsky.social · Aug 5

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!!

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Mora Ogando @moraogando.bsky.social · Aug 5

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...

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Mora Ogando @moraogando.bsky.social · Aug 5

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

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Mora Ogando @moraogando.bsky.social · Aug 5

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

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Mora Ogando @moraogando.bsky.social · Aug 5

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

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Mora Ogando @moraogando.bsky.social · Aug 5

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

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Mora Ogando @moraogando.bsky.social · Aug 5

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

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Mora Ogando @moraogando.bsky.social · Aug 5

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

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Mora Ogando @moraogando.bsky.social · Aug 5

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.

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Mora Ogando @moraogando.bsky.social · Aug 5

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)

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Mora Ogando @moraogando.bsky.social · Aug 5

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

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Mora Ogando @moraogando.bsky.social · Aug 5

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