ananyapassi

[SP Arun @sparuniisc]

In a study, now out in Attention Perception & Psychophysics (by @Psychonomic_Soc), @ananyapassi and I have some cool insights about how parts combine in sound-shape associations (in the famous Bouba-Kiki effect). Read on to find out more! 1/12

[Mick Bonner @michaelfbonner]

Exciting new preprint from the lab, led by an amazing student, @ckelseyhan!

[Mick Bonner @michaelfbonner]

Excited for #VSS2025! Check out this cool work from the lab.

[ananyapassi @ananyapassi]

@copy2thomas @sparuniisc @psychopy Congratulations ����

[ananyapassi @ananyapassi]

@ingalala @berkeleyecon Congratulations ✨✨✨

[ananyapassi @ananyapassi]

@dopaminergicmai Congratulations 🎉

[ananyapassi @ananyapassi]

@mkjolly15 @eLife @divyoj @BE_IISc @InSDB_79 @IITHyderabad @iiscbangalore @PoLDresden @struttlab @DynamicsLiving @physics_living @PhysicsofLifeUK @biopatrika Congratulations 🎉

[ananyapassi @ananyapassi]

@imelizabethim @leylaisi Congratulations 🎉

[ananyapassi @ananyapassi]

@karthikraman @Tanisha4M Congratulations! 🎉

[Mick Bonner @michaelfbonner]

Can we gain a deep understanding of neural representations through dimensionality reduction? Our new work shows that the visual representations of the human brain need to be understood in high dimensions. w/ @RajThrowaway42 & Brice M��nard. https://t.co/mY2TnHj4Et

[Zirui Chen @ziruichen44]

Why do varied DNN designs yield equally good models of human vision? Our preprint with @michaelfbonner shows that diverse DNNs represent images with a shared set of latent dimensions, and these shared dimensions turn out to also be the most brain-aligned.
https://t.co/vtOOYHQb47

[ananyapassi @ananyapassi]

@akrish30 @HopkinsKavli @KavliFoundation Congratulations!!

[Kathy Garcia @NeuroKathyG]

📢 Excited to announce our latest pre-print: “Modeling dynamic social vision highlights gaps between deep learning and humans”! 🌟 w/ @emaliemcmahon, Colin Conwell, @michaelfbonner, and @leylaisi🧵 #NeuroAI #CognitiveScience [1/7]

[Atlas Kazemian @AtlasKazemian]

What underlies the emergence of cortex-aligned representations in DNNs? Large-scale pre-training has been a major focus, but we show that certain architectural manipulations can yield high brain similarity even in untrained CNNs. w/@michaelfbonner https://t.co/4oULkqhQno

[ananyapassi @ananyapassi]

@abhijith_biji @agbt @asgari_samira @IcahnMountSinai @SinaiGenHealth @ChromoHogan Congratulations!

[Mick Bonner @michaelfbonner]

New paper out in @PLOSCompBiol. The best deep neural network models of visual cortex do not reduce representations to low-dimensional manifolds—instead, they benefit from high-dimensionality. Led by a fantastic student, @EricElmoznino. https://t.co/PPIuP6vaeN

[ananyapassi @ananyapassi]

@vivan_bharat Congratulations!!!!!

[ananyapassi @ananyapassi]

@AritraB24401348 @journal_evo Congratulations! 🎉🎉🎉

[ananyapassi @ananyapassi]

@ManasiMalik @NatureComms Congratulations!!!!! 🎉🎉🎉

[ananyapassi @ananyapassi]

@NikhitaKirthiv1 Congratulations 🎉👏