🔥Preprint out: `Toward a realistic model of speech processing in the brain with self-supervised learning’: arxiv.org/abs/2206.01685 by J. Millet*, Charlotte Caucheteux @ICML24* and our wonderful team: The 3 main results summarized below 👇

We’ve spent some time modeling language in the brain with LMs, but can we apply the same approach to shiny new, speech models like WavLM and if yes, what does this tell us about the brain? Our new ICML paper answers these questions, and more! (Led by Aditya Vaidya @ NeurIPS ) 1/

I've spent the past year on a passion project, asking: When it comes to understanding the brain, what are we trying to achieve? What have we learned? What challenges do we face? And what's our plan? As I pull it all together into a book, I want to champion these 11 references:

Glad to share a preprint of new work with hansemsohn and Mehrdad Jazayeri where we investigated the role of "mental simulations" in physical inferences using dense electrophysiology in the dorsomedial frontal cortex of monkeys playing Mental-Pong. biorxiv.org/content/10.110…

🚨 Job alert! 🚨 My new lab at Tulane is seeking a Research Tech (lab manager) and Postdoc to conduct research on the cognitive and neural basis of social understanding, starting as early as Sept. Please share, and get in touch if you're interested! Here's my pitch for the lab 🧵

1/8 Can we use embodied AI to gain insight into *why* neural systems are as they are? In previous work👇, we demonstrated that a contrastive unsupervised objective substantially outperforms supervised object categorization at generating networks that predict mouse visual cortex.

PINEAPPLE, LIGHT, HAPPY, AVALANCHE, BURDEN Some of these words are consistently remembered better than others. Why is that? Here we provide a simple Bayesian account and show that it explains >80% of variance in word memorability psyarxiv.com/p6kv9/

Self-supervised representation learning is greatly facilitated by the knowledge of objects and their layouts in real-world scenes. Rather than hard-coding these priors, with our new method Odin we found that objects can be discovered from the learned representations themselves.

Excited to present a comprehensive analysis on the extent to which deep neural network (DNNs) can account for brain responses in the human auditory cortex! Work with Jenelle Feather (co-1st) Dr. Dana Boebinger Josh McDermott biorxiv.org/content/10.110…

New opening for a full-time lab tech in the Kanwisher Lab: careers.peopleclick.com/careerscp/clie… This job is a great stepping stone to graduate programs in cognitive neuroscience. Demonstrated serious interest in cog neuro and good coding skills required. Machine learning skills a plus.

Bispectral Neural Networks go to #ICLR2023! In this work, we present a new neural network architecture capable of learning unknown groups purely from the symmetries implicit in data —with Christian Shewmake, Bruno Olshausen, and Christopher Hillar 1/17

We're thrilled to announce a breakthrough in 3D world generation. Now, transform ANY image - AI-generated, concept art or real world shots - into high-resolution game-engine ready 3D asset. Check it out: 🎈 Public Showcase on Discord: 3d.csm.ai/discord-sessio… 🤖 Generate your own

This work from NeurIPS 2019 was the start of a much larger project that is now out in Nature Neuroscience! Check it out here: nature.com/articles/s4159…

Our work on models of the auditory cortex is hereby published in PLOS Biology! The paper presents an extensive analysis of audio neural networks as models of auditory processing in the brain. Paper: tinyurl.com/mw2e59ud w/ Jenelle Feather (co-1st) Dr. Dana Boebinger Josh McDermott

Seeking: Creative and ambitious computational neuroscience / ML PhDs interested in building next-generation brain-computer interfaces. My team at science.xyz is hiring research scientists. Reach out at [email protected] for more info.

do large-scale vision models represent the 3D structure of objects? excited to share our benchmark: multiview object consistency in humans and image models (MOCHI) with Stephanie Fu Yutong Bai Thomas O'Connell @_yonifriedman Nancy Kanwisher @[email protected] Josh Tenenbaum and Alexei Efros 1/👀

1/6 I usually don’t comment on these things, but Rylan Schaeffer et al.'s paper contains enough misconceptions that I thought it might be useful to address them. In short, effective dimensionality is not the whole story for model-brain linear regression, for several reasons:

This nicely summarizes some misconceptions in the recent position paper about measures of brain-model comparison. One additional point: While I agree with the idea in the original paper that we shouldn't *just* focus on regression, many other measures have been proposed! 1/4

We just wrote a primer on how the physics of sound constrains auditory perception: authors.elsevier.com/a/1jzSR3QW8S6E… Covers sound propagation and object interactions, and touches on their relevance to music and film. I enjoyed working on this with Vin Agarwal and James Traer.

I’m recruiting PhD students to join the Computational Minds and Machines Lab at the University of Washington in Seattle! Join us to work at the intersection of computational cognitive science and AI with a broad focus on social intelligence. (Please reshare!)