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Reposted by M Ganesh Kumar

PessoaBrain @pessoabrain.bsky.social · Sep 7

𝗕𝗿𝗮𝗶𝗻-𝗯𝗼𝗱𝘆 𝗽𝗵𝘆𝘀𝗶𝗼𝗹𝗼𝗴𝘆:
𝗟𝗼𝗰𝗮𝗹, 𝗿𝗲𝗳𝗹𝗲𝘅, 𝗮𝗻𝗱 𝗰𝗲𝗻𝘁𝗿𝗮𝗹 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻
Excellent review paper about reactive and anticipatory processes.
#neuroskyence
doi.org/10.1016/j.ce...

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M Ganesh Kumar @mgkumar138.bsky.social · Aug 27

I am extremely grateful to be awarded the National University of Singapore (NUS) Development Grant, and to be a Young NUS Fellow! Look forward to collaborating with the Yong Loo Lin School of Medicine on exciting projects. This is my first grant and hopefully many more to come! #NUS #NeuroAI

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Ann Kennedy @antihebbiann.bsky.social · Aug 20

I wrote a Comment on neurotheory, and now you can read it!

Some thoughts on where neurotheory has and has not taken root within the neuroscience community, how it has shaped those subfields, and where we theorists might look next for fresh adventures.

www.nature.com/articles/s41...

Theoretical neuroscience has room to grow

Nature Reviews Neuroscience - The goal of theoretical neuroscience is to uncover principles of neural computation through careful design and interpretation of mathematical models. Here, I examine...

www.nature.com

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Elliott Wimmer @elliottwimmer.bsky.social · Aug 20

🧵 New paper! We studied depression symptoms and goal-directed decisions under uncertainty

@shiyiliang.bsky.social, with @evanrussek.bsky.social & @robbrutledge.bsky.social

Surprisingly, we found that apathy–anhedonia was linked to enhanced goal-directed behavior. www.biorxiv.org/content/10.1...

www.biorxiv.org

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M Ganesh Kumar @mgkumar138.bsky.social · Aug 19

Not just for AI but these theories can improve our understanding of biological networks too!

Simons Foundation @simonsfoundation.org · Aug 19

Our new Simons Collaboration on the Physics of Learning and Neural Computation will develop powerful tools from #physics, #math, computer science and theoretical #neuroscience to understand how large neural networks learn, compute, scale, reason and imagine: www.simonsfoundation.org/2025/08/18/s...

Simons Foundation Launches Collaboration on the Physics of Learning and Neural Computation

Simons Foundation Launches Collaboration on the Physics of Learning and Neural Computation on Simons Foundation

www.simonsfoundation.org

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Reposted by M Ganesh Kumar

tal boger @talboger.bsky.social · Aug 19

On the left is a rabbit. On the right is an elephant. But guess what: They’re the *same image*, rotated 90°!

In @currentbiology.bsky.social, @chazfirestone.bsky.social & I show how these images—known as “visual anagrams”—can help solve a longstanding problem in cognitive science. bit.ly/45BVnCZ

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Tomer Ullman @tomerullman.bsky.social · Aug 19

trying this with GPT-5 and charting new frontiers in gaslighting

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David G. Clark @david-g-clark.bsky.social · Aug 19

Wanted to share a new version (much cleaner!) of a preprint on how connectivity structure shapes collective dynamics in nonlinear RNNs. Neural circuits have highly non-iid connectivity (e.g., rapidly decaying singular values, structured singular-vector overlaps), unlike classical random RNN models.

Connectivity structure and dynamics of nonlinear recurrent neural networks

Studies of the dynamics of nonlinear recurrent neural networks often assume independent and identically distributed couplings, but large-scale connectomics data indicate that biological neural circuit...

arxiv.org

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M Ganesh Kumar @mgkumar138.bsky.social · Aug 13

3. We present TeDFA-δ, a bio. plaus. deep spiking RL model that leverages temporal integration and weak learning rules to outperform standard MLPs+BP for policy learning, highlighting the importance of neural dynamics over credit assignment for effective control:

2025.ccneuro.org/poster/?id=S...

Poster Presentation

2025.ccneuro.org

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M Ganesh Kumar @mgkumar138.bsky.social · Aug 13

2. We developed a bio. plaus. computational model of the dentate gyrus that shows how both impaired synaptic plasticity and increased neurogenesis—modulated by Cbln4-Neo1 complex—disrupt behavioral pattern separation:

2025.ccneuro.org/poster/?id=P...

Poster Presentation

2025.ccneuro.org

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M Ganesh Kumar @mgkumar138.bsky.social · Aug 13

1. We developed a RNN-based meta-RL framework that models schizophrenia-like decision-making deficits. We see a positive correlation between the number of dynamical attractor states and suboptimal behavior:

2025.ccneuro.org/poster/?id=4...

Poster Presentation

2025.ccneuro.org

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M Ganesh Kumar @mgkumar138.bsky.social · Aug 13

1 proceeding and 2 extended abstracts at Cognitive Computational Neuroscience (CCN) Conference 2025! Short summaries and links are in the thread. Look forward to the discussions! #CCN25

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Nature Reviews Neuroscience @natrevneuro.nature.com · Aug 11

The emergence of NeuroAI: bridging neuroscience and artificial intelligence — a Comment article by Sadra Sadeh & Claudia Clopath

@sdrsd.bsky.social @clopathlab.bsky.social
‪
#neuroscience #neuroskyence

www.nature.com/articles/s41...

The emergence of NeuroAI: bridging neuroscience and artificial intelligence - Nature Reviews Neuroscience

Neuroscience has inspired artificial intelligence (AI) for decades but, in recent years, AI tools have begun to revolutionize neuroscience research. The emerging field of NeuroAI has the potential to ...

www.nature.com

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Reposted by M Ganesh Kumar

Hyunwoo Gu @hyunwoogu.bsky.social · Jul 29

Excited to share that our paper is now out in Neuron @cp-neuron.bsky.social (dlvr.it/TM9zJ8).

Our perception isn't a perfect mirror of the world. It's often biased by our expectations and beliefs. How do these biases unfold over time, and what shapes their trajectory? A summary thread. (1/13)

Attractor dynamics of working memory explain a concurrent evolution of stimulus-specific and decision-consistent biases in visual estimation

People exhibit biases when perceiving features of the world, shaped by both external stimuli and prior decisions. By tracking behavioral, neural, and mechanistic markers of stimulus- and decision-rela...

dlvr.it

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Reposted by M Ganesh Kumar

Andrew Lampinen @lampinen.bsky.social · Aug 5

In neuroscience, we often try to understand systems by analyzing their representations — using tools like regression or RSA. But are these analyses biased towards discovering a subset of what a system represents? If you're interested in this question, check out our new commentary! Thread:

What do representations tell us about a system? Image of a mouse with a scope showing a vector of activity patterns, and a neural network with a vector of unit activity patterns
Common analyses of neural representations: Encoding models (relating activity to task features) drawing of an arrow from a trace saying [on_____on____] to a neuron and spike train. Comparing models via neural predictivity: comparing two neural networks by their R^2 to mouse brain activity. RSA: assessing brain-brain or model-brain correspondence using representational dissimilarity matrices

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Sam Gershman @gershbrain.bsky.social · Aug 4

A landmark volume, The Handbook of Dopamine, is now online:
www.sciencedirect.com/handbook/han...

Big kudos to the editors, Stephanie Cragg and Mark Walton, for putting this together.

Handbook of Behavioral Neuroscience | Volume 32: The Handbook of Dopamine | ScienceDirect.com by Elsevier

Read the latest chapters of Handbook of Behavioral Neuroscience at ScienceDirect.com, Elsevier’s leading platform of peer-reviewed scholarly literature

www.sciencedirect.com

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David Sussillo @sussillodavid.bsky.social · Aug 4

Coming March 17, 2026!
Just got my advance copy of Emergence — a memoir about growing up in group homes and somehow ending up in neuroscience and AI. It’s personal, it’s scientific, and it’s been a wild thing to write. Grateful and excited to share it soon.

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Dan Goodman @neural-reckoning.org · Jul 11

How can we test theories in neuroscience? Take a variable predicted to be important by the theory. It could fail to be observed because it's represented in some nonlinear, even distributed way. Or it could be observed but not be causal because the network is a reservoir. How can we deal with this?

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Grace Lindsay @neurograce.bsky.social · Jul 23

This summer my lab's journal club somewhat unintentionally ended up reading papers on a theme of "more naturalistic computational neuroscience". I figured I'd share the list of papers here 🧵:

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M Ganesh Kumar @mgkumar138.bsky.social · Jul 12

First #ICML2025 conference proceeding (icml.cc/virtual/2025...)! We (@frostedblakess.bsky.social, @jzv.bsky.social, @cpehlevan.bsky.social) developed a simple model to better understand state representation learning dynamics in both artificial and biological intelligent systems!

ICML Poster A Model of Place Field Reorganization During Reward MaximizationICML 2025

icml.cc

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M Ganesh Kumar @mgkumar138.bsky.social · Apr 28

State representation learning in the hippocampus?

Kempner Institute at Harvard University @kempnerinstitute.bsky.social · Apr 27

Monday 4/28 at #ICLR2025!

Submission: openreview.net/forum?id=Qcv...

'A Model of Place Field Reorganization During Reward Maximization'

@mgkumar138.bsky.social, Blake Bordelon, Jacob A Zavatone-Veth, @CPehlevan.bsky.social

#ML #neuroscience

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M Ganesh Kumar @mgkumar138.bsky.social · Apr 23

I'm heading back to Singapore for ICLR25! Hit me up for discussions or where to find good food!

#neuroai #home

Kempner Institute at Harvard University @kempnerinstitute.bsky.social · Apr 23

Are you at #ICLR2025? See the lineup of Kempner Institute presenters and check out their work!

#ML #AI

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M Ganesh Kumar @mgkumar138.bsky.social · Mar 27

Interestingly, we found no significant difference in under and over-updating behavior in Schizophrenia patient data (Nassar et al. 2021). Instead, analyzing the behavior using the delta area metric showed a significant difference, suggesting the utility of model-guided human-behavior data analysis.

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M Ganesh Kumar @mgkumar138.bsky.social · Mar 27

We used a fixed point finder algorithm and found that suboptimal agents (lower delta area value) exhibited smaller number of unstable fixed points compared to more optimal agents. The number of stable fixed points remained consistent across the delta area metric.

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M Ganesh Kumar @mgkumar138.bsky.social · Mar 27

Besides the (1) reward discount factor, we explored (2) prediction error scaling, (3) probability of disrupting RNN dynamics, (4) rollout buffer length. Each hyperparameter differently influenced the suboptimal decision making behavior, which we termed as delta area.

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