Apoorva Bhandari

@apaxon.bsky.social

Cognitive neuroscientist at Brown University

🚨 New paper in @nature.com We asked why two hippocampal areas with very different anatomy, CA3 and CA1, often seem to code space so similarly? By recording bats flying up to 200m, we found that the difference was hidden by scale! www.nature.com/articles/s41... 🧵 1/11

Sparse-to-dense coding transformation between hippocampal areas CA3 and CA1 - Nature

The hippocampus exhibits a CA3-to-CA1 coding transformation that combines fast learning with an efficient, compressed neural code.

nature.com

New Paper: Continuous Thought Machines pub.sakana.ai/ctm/ Neurons in brains use timing and synchronization in the way that they compute, but this is largely ignored in modern neural nets. We believe neural timing is key for the flexibility and adaptability of biological intelligence. Thread ↓

Sakana AI@sakanaai.bsky.social · last yr.

“Continuous Thought Machines” Blog → sakana.ai/ctm Modern AI is powerful, but it's still distinct from human-like flexible intelligence. We believe neural timing is key. Our Continuous Thought Machine is built from the ground up to use neural dynamics as a powerful representation for intelligence.

Maps are everywhere in the brain...and finally we've discovered one in the nose! Led by @davidhbrann.bsky.social, we uncovered the logic that specifies the positions of each of the 1,000 sensory neuron subtypes in the nose and aligns their projections to the brain.👇👃see more details below👃👇

A spatial code governs olfactory receptor choice and aligns sensory maps in the nose and brain

Although topographical maps organize many peripheral sensory systems, it remains unclear whether olfactory sensory neurons (OSNs) choose which of the ~1100 odor receptors (ORs) to express based upon t...

biorxiv.org

Excited to share our latest story! We found disentangled memory representations in the hippocampus that generalized across time and environments, despite the seemingly random drift and remapping of single cells. This code enabled the transfer of prior knowledge to solve new tasks

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New preprint w/ Haley Keglovits & David Badre. lPFC flexibly codes tasks of diff structure. How? We test 2 prevalant ideas 1) it uses a high dim, expressive geometry, agnostic to structure 2) it learns tailored geometries for each structure. tldr - Its 1* www.biorxiv.org/content/10.1...

Task structure tailors the geometry of neural representations in human lateral prefrontal cortex

bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

biorxiv.org