My lab at USC is recruiting!
1) research coordinator: perfect for a recent graduate looking for research experience before applying to PhD programs: usccareers.usc.edu REQ20167829
2) PhD students: see FAQs on lab website dornsife.usc.edu/hklab/faq/

Thrilled to announce our new publication titled 'Decoding predicted future states from the brain's physics engine' with @emiecz.bsky.social, Cyn X. Fang, @nancykanwisher.bsky.social, @joshtenenbaum.bsky.social

www.science.org/doi/full/10....

(1/n)

What shapes the topography of high-level visual cortex?

Excited to share a new pre-print addressing this question with connectivity-constrained interactive topographic networks, titled "Retinotopic scaffolding of high-level vision", w/ Marlene Behrmann & David Plaut.

🧵 ↓ 1/n

Super excited for our #VSS2025 symposium tomorrow, "Model-optimized stimuli: more than just pretty pictures".
Join us to talk about designing and using synthetic stimuli for testing properties of visual perception!

May 16th @ 1-3PM in Talk Room #2

More info: www.visionsciences.org/symposia/?sy...

Whelp. See you later 1.8 Million in NSF research funds -- all designed to better understand learning mechanisms in early childhood so we can develop effective early childhood educational interventions.

Proud of Harvard for standing up to fascism, though.

We will persist.

We are presenting our work “Discriminating image representations with principal distortions” at #ICLR2025 today (4/24) at 3pm! If you are interested in comparing model representations with other models or human perception, stop by poster #63. Highlights in 🧵
openreview.net/forum?id=ugX...

i'll be presenting this as a poster on thursday @ iclr! please come by & chat if any of this sounds interesting to you :)

thanks for reading. link to the paper again: openreview.net/forum?id=IqH...

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in sum, we used dominant components of the neural response to get an **axis-sensitive** measure of similarity.

this work fits into a broader look at (R)epresentational alignment (cf. work by @taliakonkle.bsky.social, @itsneuronal.bsky.social, @sucholutsky.bsky.social, & others)

12/n

we also used connectivity matrices to capture behaviorally-relevant information. a lot like rsa! but with a sparse coding structure

11/n

using sca and a few different pre-trained models, we found markedly higher alignment to the ventral stream.

rotationally invariant methods were less sensitive to this finding, providing an answer to question 2: DNNs are more similar to the ventral stream along a native axis of neural tuning

10/n

the resulting matrices represent the activity of sparse sub-populations of neurons/units and, unlike some methods, are quite sensitive to rotations in neural space

you can thus interpret sca as measuring similarity along a specific set of tuning axes

9/n

we applied the same decomposition to DNN activations and used them in a method we call **sparse component alignment** (sca). sca compares representations at the population level using image x image connectivity matrices.

(see the paper for complete derivation)

8/n

these response profiles gave an answer to question 1: there are interpretable and functionally-distinct representations across the brain's three visual pathways.

nice to see! but also heightens the mystery of question 2: why aren't these differences picked up by standard similarity metrics

7/n

we reproduced well-known category selectivity in the ventral stream (for faces, scenes, bodies, etc).

new in this paper we also found components in the lateral stream (groups of people, implied motion, hand actions, scenes, & reachspaces) and dorsal stream (implied motion & scenes)

6/n

to find out, we used a data-driven method to identify dominant components of the neural response to natural images.

some of the most consistent components had pretty clear selectivities, which we cross-validated with behavioral saliency ratings

5/n

this left us with two big questions:

1. what distinguishes visual representations in the dorsal, ventral, & lateral streams?
2. why does alignment to DNNs often fail to reflect these differences?

4/n

yet neural networks trained to perform a single task seem to model all three pathways pretty well. so perhaps the representations in these streams are not so different after all?

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it's commonly thought that the brain processes distinct visual information along separate functional pathways (the dorsal, ventral, & lateral streams)

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**ecstatic** to share our @iclr-conf.bsky.social paper: sparse components distinguish visual pathways & their alignment to neural networks, with @nancykanwisher.bsky.social and meenakshi khosla (openreview.net/forum?id=IqH...)

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A 19-year-old university student from Gaza I know, who is brilliant and charming and usually upbeat, just wrote back: "we are not OK. The bombing is continuous and non-stop. Please pray for us. Happy Eid to you."

I’m hiring a full-time lab tech for two years starting May/June. Strong coding skills required, ML a plus. Our research on the human brain uses fMRI, ANNs, intracranial recording, and behavior. A great stepping stone to grad school. Apply here:
careers.peopleclick.com/careerscp/cl...
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