Excited to share that seq-JEPA has been accepted to NeurIPS 2025!

We’ll be at CCN next week with two posters, one contributed talk and one satellite talk! If you are interested in neural coding in primate hippocampus, modeling visual search, or cortical topography come and talk to us! See👇for details:

A few years ago, with @tyrellturing.bsky.social @patrickmineault.bsky.social and others, we showed one single objective (prediction) was sufficient for explaining specialized pathways of mouse visual cortex: tinyurl.com/2vsjbve7

Super cool to see the same observation now for human visual cortex.

📌 Poster Session:
When📅: Friday, April 25 from 10:00 AM to 12:30 PM
Where: Hall 3 + Hall 2B #60
Check out our project page: amirozhan.github.io/CBSOM/

See you soon!

@mila-quebec.bsky.social

I’ll be at @iclr-conf.bsky.social presenting our work “Credit-based self organizing maps: training deep topographic networks with minimal performance degradation” — if you're into #NeuroAI, cortical topography, vision, or just up for a good convo, drop by and DM me! 🧠✨

📢 We have a new #NeuroAI postdoctoral position in the lab!

If you have a strong background in #NeuroAI or computational neuroscience, I’d love to hear from you.

(Repost please)

🧠📈🤖

People who might be interested: @taliakonkle.bsky.social, @mschrimpf.bsky.social, @apurvaratan.bsky.social @nancykanwisher.bsky.social

11/ Last (but not least), this work wouldn’t have been possible without the help of my collaborators
@Xinyu Qian, @asafarahani.bsky.social and research advisor @bashivan.bsky.social ⭐️

10/ Our results signify the potentially critical role of top-down assigned credits in shaping the topographical organization within the cortex.

9/ Finally, we show that compared to other baselines including classic SOM (AB-SOM) and TDANN, CB-SOM shows substantially higher alignment with neural activity in the visual cortex of both macaques (Brain-Score) and humans (NSD).

8/ Similarly, the model’s deeper layers replicated the organization of the higher-level visual cortical regions according to category-selectivity, resulting in category-selective filter clusters in these layers.

7/ We also observed an exponentially decaying pairwise correlation between filters as a function of their distance on the simulated cortical sheet.

6/ Our model replicated the topographical organization seen in the primary visual cortex topographical organization, including smooth transitions in selectivity according to stimulus orientation and spatial frequency.

5/ With this approach, our ResNet-18 model achieved a substantial performance boost while preserving topographical organization.

4/ We proposed an alternative approach based on self-organizing maps, Credit-Based Self-Organizing Maps(CB-SOMs), where the competition mechanism is guided by the importance of the assigned credit to each weight – i.e. filters with the highest gradient norm in each conv layer.

3/ Previous approaches to simulate topographical organization in neural networks involved using various objective functions, but this often results in weaker performance on object categorization.
cell.com/neuron/fullt...
pnas.org/doi/10.1073/...
arxiv.org/pdf/2308.09431

2/ Cortex in many animals, including humans, is topographically organized, where functionally similar neurons are spatially close to each other. We sought to understand the computational principles underlying this visual topographical organization.

I am excited to share that my undergraduate research has now been accepted to #ICLR2025.
📎: openreview.net/pdf?id=wMgr7...
🧑‍💻: github.com/BashivanLab/...

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