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I am so happy—this is the first 1st author paper I have written, since the cancer diagnosis of my late wife seven years ago. 📑 PAPER ALERT: "Beyond binding: from modular to natural vision" in Trends in Cognitive Sciences (2025) sciencedirect.com/science/articl….

Timeless advice

Announcement: Workshop at #CCN2025 🧠 Modeling the Physical Brain: Spatial Organization & Biophysical Constraints 🗓️ Monday, Aug 11 | 🕦 11:30–18:00 CET |📍 Room A2.07 🔗 Register: tinyurl.com/CCN-physical-b… #NeuroAI CogCompNeuro

Come to our CCN workshop! Blending iophysical constraints and neural networks. Topographical ANNs + and much more. Meenakshi Khosla talia konkle pouya bashivan Nick Blauch Tim Kietzmann Laura Gwilliams Apurva Ratan Murty Jakob Macke Pieter Roelfsema Martin Schrimpf Andrew Miri Nabil Imam

Introducing All-TNNs: Topographic deep neural networks that exhibit ventral-stream-like feature tuning and a better match to human behaviour than the gold standard. Now out in Nature Human Behaviour. 👇

In this study, Lu et al. introduce All-Topographic Neural Networks (All-TNN) as a parsimonious model of the human visual cortex. nature.com/articles/s4156…

How can we build neural networks that develop the same topographic organization as primate visual cortex and better predict human spatial visual behavior?Nature Human Behaviour "End-to-end topographic networks as models of cortical map formation and human visual behaviour" • The

Exciting new preprint from the lab: “Adopting a human developmental visual diet yields robust, shape-based AI vision”. A most wonderful case where brain inspiration massively improved AI solutions. Work with Zejin Lu | 陆泽金 Sushrut Thorat and Radoslaw Cichy arxiv.org/abs/2507.03168

#AI and #computervision folks: this will be interesting to you! Robust visual inference beyond scale, but achieved by taking inspiration from how infant vision develops. Shown to work across many architectures and comes with a neat preprocessing pipeline that you can use.