Neuroscientist. Assistant Professor of Cognition and Brain Science @GeorgiaTech
IISc | MIT | Georgia Tech
Joined August 2009
- Tweets785
- Following1.2K
- Followers1.9K
- Likes8K
Pinned Tweet
Very proud to share our @iclr_conf paper: TopoNets! High-performing vision and language models with brain-like topography! Expertly led by grad student @mayukh091 and @MainakDeb19! A brief thread..
Apurva Ratan Murty retweeted
Causal reasoning lies at the core of human intelligence but few studies have tested its neural basis. In a new study, we find that despite its importance, causal reasoning is implemented not in a single unified brain system, but rather in separate domain-specific regions.
Apurva Ratan Murty retweeted
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
Seems as good a time any to post our recent position paper. 😉 @paleunahu Now selected as an Oral at the Neurips workshop on Interp4discovery
Our position paper has been selected for Oral at @NeurIPS interp4discovery. We propose a framework for turning local interpretability findings into testable mechanistic explanations: openreview.net/forum?id=8Duo…
Thanks to Chen Jiang @PsychBoyH @apurvaratan and Sashank Varma.
Apurva Ratan Murty retweeted
1/
New preprint! 🦇 🤖
We built a functional “digital twin” of the hippocampus: a transformer trained on Neuropixels recordings and 3D flight in freely flying bats.
Probing its latent space revealed previously unknown structure in hippocampal population activity.
🧵
Apurva Ratan Murty retweeted
Leading AI models appeared to predict brain activity equally well…
…until we asked them to synthesize images that precisely control neural firing, like these 👇
Across 25 neural sites, closed-loop experiments revealed striking hidden differences in model-brain alignment.
🧵
Another update from our topography work from (high-schooler!) Navneet Prakash. Navneet trained path-integrating RNNs with TopoLoss and found that they recover a much more brain-like center-surround RF connectivity structure than baseline models (w/o any post-hoc sorting)!
And here is code to replicate the findings: github.com/murtylab/grid-pat…
Today I want to share an update from our topographic ANN work led by @haidertahan. What does adding topography actually do to a model’s internal representations? Does it make is more brain like? Introducing TopoAudio
All of this led us to conclude that Topographic constraints shape brain-like component structure in topographic auditory modes.
An early version of the paper is here: arxiv.org/pdf/2509.24039?
The CCN Proceedings: openreview.net/pdf?id=sELPpr…
Reviews: openreview.net/forum?id=sELP…
Many thanks to @mayukh091 and @jenellefeather who were important collaborators on this work. Topographic audio models are still new. So try them out in your own work and let us know if you have feedback. This work was expertly led by @haidertahan!