Spontaneous problem-solving in bumble bees.
Amazing work by the bees who were put in these puzzling situations by @akshayebhambore.bsky.social in the lab of @olliloukola.bsky.social
www.science.org/doi/10.1126/...
Only one week left until our PhD application deadline @mpicybernetics.bsky.social. If you’re interested in the intersection of RL, RNNs, and analyses of neural & behavioral data from novel, cross-species foraging experiments, make sure to submit your application by June 15! #NeuroJobs
More info👇
Video
Thrilled to see the first @cp-trendscognsci.bsky.social paper of the lab out 🤩 Wanna understand one of the most complex cognitive dysfunctions using one of the most basic adaptive functions, perception?
Paper: linkinghub.elsevier.com/retrieve/pii/S136466132600032X
Thread: bsky.app/profile/neur...
Ever wonder how your brain navigates the real world? 🧠
Most spatial navigation studies use flat, 2D mazes.
But the real world is hilly, irregular, and bumpy! ⛰️
Our new paper in #ScienceAdvances asked a simple question: How does the brain map uneven terrain?
📄 doi.org/10.1126/scia...
🧵👇️️️
1/
🚨🧠 Our paper is out! We introduce a simple computational model that generates macroscopic, long-timescale dynamics as seen in large-scale neural recordings. #neuroscience #dynamics @marius10p.bsky.social @zhong-lin.bsky.social @hhmijanelia.bsky.social Link: go.nature.com/4tRNjIu
1/11 Happy to share our TICS paper on using the flexibility of one of the most basic cognitive functions, perception, to understand one of the most complex cognitive dysfunctions, psychiatric conditions (also my first formal work in computational psychiatry 🎉)
📄: www.cell.com/trends/cogni...
🧵 : 👇
5/11 Perceptual multistability is a phenomenon conserved across evolution (observed from Drosophila to humans) and sensory modalities. It seems AI systems also elicit some form of it -- see the great work of @taherehtoosi.bsky.social).
✅ Yes, more articles like this, PLEASE. Not (only) the specific take (which I like), but the honest attempt to clarify a disastrously dubious concept, using history, technical details, clear writing, rhetoric.
'Actually, what is a gain-of-function mutation?' academic.oup.com/genetics/art...
New review with Cheng Xue at U Chicago @cxue.bsky.social in Trends [email protected]! We discuss the neural geometry of task-dependent computation: disentangled encoding, RNN modeling, switch cost, etc.
www.cell.com/trends/neuro...
John Tuthill
Computational models are a key part of science but discovering new ones is hard!
DataDIVER discovers concise models from data, which surface new mechanistic ideas and clear predictions for future experiments
From Google Deepmind Neuroscience Lab + collaborators
www.biorxiv.org/content/10.6...
Roxana Zeraati
Abstract. For more than a century, scientists have worked to characterize, understand, and predict the consequences of mutations. For almost as long, scien
Perceptual multistability, observed across species and sensory modalities, offers
valuable insights into numerous cognitive functions and dysfunctions. For instance,
differences in temporal dynamics a...
If these research directions resonate with you and you're interested in joining our team, we now have 2 open PhD positions!
More information about the positions: nextcloud.tuebingen.mpg.de/index.php/s/...
More information about our research: www.kyb.tuebingen.mpg.de/906930/natur...
#NeuroJobs
Shervin Safavi
Shervin Safavi
Gouki Okazawa
C. Brandon Ogbunu
Nature research paper: A critical initialization for biological neural networks
go.nature.com/4tRNjIu
Video
Kevin J Miller
joss.theoj.org
Roxana Zeraati
Basic properties of symmetric random matrices can explain the emergence of macroscopic patterns in neural networks, suggesting that initialization conditions of connected neural populations may confer computational advantages across the mouse brain.
Online Now: Perceptual multistability: a multifaceted window into brain dysfunctions
Nature
1/11 Happy to share our TICS paper on using the flexibility of one of the most basic cognitive functions, perception, to understand one of the most complex cognitive dysfunctions, psychiatric conditions (also my first formal work in computational psychiatry 🎉)
📄: www.cell.com/trends/cogni...
🧵 : 👇
To solve diverse real-world tasks, the brain must flexibly switch between task rules
and adjust computations. Recent advances in analyzing neural data and modeling neural
networks have revealed their ...
Perceptual multistability, observed across species and sensory modalities, offers valuable insights into numerous cognitive functions and dysfunctions. For instance, differences in temporal dynamics and information integration during percept formation often distinguish clinical from nonclinical populations. Computational psychiatry can elucidate these variations through two primary approaches: (i) Bayesian modeling, which treats perception as an unconscious inference, and (ii) an active, information-seeking perspective (e.g., reinforcement learning), which frames perceptual switches as internal actions. Our synthesis aims to leverage multistability to bridge these computational psychiatry subfields, linking human and animal studies as well as connecting behavior to underlying neural mechanisms. Perceptual multistability emerges as a promising noninvasive tool for clinical applications, facilitating translational research and enhancing our mechanistic understanding of cognitive processes and their impairments.
Perceptual multistability, observed across species and sensory modalities, offers
valuable insights into numerous cognitive functions and dysfunctions. For instance,
differences in temporal dynamics a...
Online Now: Perceptual multistability: a multifaceted window into brain dysfunctions
Perceptual multistability, observed across species and sensory modalities, offers valuable insights into numerous cognitive functions and dysfunctions. For instance, differences in temporal dynamics and information integration during percept formation often distinguish clinical from nonclinical populations. Computational psychiatry can elucidate these variations through two primary approaches: (i) Bayesian modeling, which treats perception as an unconscious inference, and (ii) an active, information-seeking perspective (e.g., reinforcement learning), which frames perceptual switches as internal actions. Our synthesis aims to leverage multistability to bridge these computational psychiatry subfields, linking human and animal studies as well as connecting behavior to underlying neural mechanisms. Perceptual multistability emerges as a promising noninvasive tool for clinical applications, facilitating translational research and enhancing our mechanistic understanding of cognitive processes and their impairments.
