M Ganesh Kumar

M Ganesh Kumar

@mgkumar138.bsky.social

Neuro-AI Postdoc @ MPI Biological Cybernetics. Previously @Harvard, A*STAR & NUS. 🇸🇬

269 followers220 following34 posts

M Ganesh Kumar reposted

M Ganesh Kumar reposted

I’m very happy to share the latest from my lab published in @Nature Hippocampal neurons that initially encode reward shift their tuning over the course of days to precede or predict reward. Full text here: rdcu.be/eY5nh

M Ganesh Kumar reposted

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

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

M Ganesh Kumar reposted

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...

nature.comTheoretical neuroscience has room to growNature 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...

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

Simons Foundation@simonsfoundation.org

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...

M Ganesh Kumar reposted

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.

arxiv.orgConnectivity structure and dynamics of nonlinear recurrent neural networksStudies 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...
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M Ganesh Kumar reposted

M Ganesh Kumar reposted

M Ganesh Kumar reposted

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:

M Ganesh Kumar reposted

M Ganesh Kumar reposted

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.

M Ganesh Kumar reposted

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?

New preprint! We trained an RNN using RL to solve a decision making task used to characterize suboptimal decision making by Schizophrenic patients. First project exploring comp psych models, thanks to @adam-manoogian.bsky.social @shawnrhoadsphd.bsky.social @bqian.bsky.social @cpehlevan.bsky.social

bioRxiv Neuroscience@biorxiv-neursci.bsky.social

Neurocomputational underpinnings of suboptimal beliefs in recurrent neural network-based agents https://www.biorxiv.org/content/10.1101/2025.03.13.642273v1

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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.

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.

New preprint! We trained an RNN using RL to solve a decision making task used to characterize suboptimal decision making by Schizophrenic patients. First project exploring comp psych models, thanks to @adam-manoogian.bsky.social @shawnrhoadsphd.bsky.social @bqian.bsky.social @cpehlevan.bsky.social

bioRxiv Neuroscience@biorxiv-neursci.bsky.social

Neurocomputational underpinnings of suboptimal beliefs in recurrent neural network-based agents https://www.biorxiv.org/content/10.1101/2025.03.13.642273v1

Agents have to learn 2 solutions to predict changes in target location (change-point) and ignore outliers (oddballs). Decreasing the reward discount factor caused agents to under-update and over-update in each conditions respectively, replicating the maladaptive behavior seen in patients.

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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We're currently validating some of these model predictions by analyzing neural datasets. If you are interested in exploring this model further or have relevant datasets, please DM me! #Hippocampus #RL #NeuroAI

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