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…research, and what proof, creativity, understanding and storytelling contribute to the discipline. The recording took place on 26 July 2026; Quanta published the episode on 3 September 2026.

Podcast episode · Mathematics

To better understand human scene understanding, we extracted features from images using CLIP, a neural network model of visual concept trained with supervision from natural language. We then constructed voxelwise encoding models to explain whole brain responses arising from viewing natural images from the Natural Scenes Dataset (NSD) - a large-scale fMRI dataset collected at 7T. Our results reveal that CLIP, as compared to convolution based image classification models such as ResNet or AlexNet, as well as language models such as BERT, gives rise to representations that enable better prediction

Wed, Dec 1, 2021 · 11:00 UTC

Seminar · Computational Neuroscience

An Algorithmic Barrier to Neural Circuit Understanding

Venkat Ramaswamy · Birla Institute of Technology & Science

…Establishing this type of causal understanding will require multiple perturbational (e.g optogenetic) experiments. It has been unclear exactly how many such experiments are needed and how this number scales with the size of the nervous system in question. Here, using techniques from Theoretical Computer Science, we prove that establishing the m

Fri, Oct 2, 2020 · 15:00 UTC

Seminar · Computational Neuroscience