Hari Kalidindi
@harikalidindi.bsky.social
Research Fellow, Sensorimotor & Computational Neuroscience Donders Institute, Netherlands | Studying how brain produces movements
Happy to see our article included in #JNeurosci featured research!
SfN Journals@sfnjournals.bsky.social · 3mo ago#JNeurosci: Kalidindi and Crevecoeur explored the different ways in which individuals respond to novel forces and learn to anticipate repeated ones. https://doi.org/10.1523/JNEUROSCI.2158-25.2026
In the last 48h: - Jr researcher asked me wheter to use AI in making talks - Saw two talks, with AI {slop, enhanced} slides Collected my thoughts and wrote a post. Tl;dr: don't steal your own thinking, don't remove *you* from your talks. Also, give a &#@% about your talks.
📢📢 Multiple control strategies influencing goal-directed arm reaching in dynamic environments. I am happy to share this early release article with @fredcrevecoeur.bsky.social in @sfnjournals.bsky.social #JNeurosci
Fred Crevecoeur@fredcrevecoeur.bsky.social · 4mo agoAvailable as early release, latest work from the lab by my talented collaborator @harikalidindi.bsky.social : www.jneurosci.org/content/earl...
In our new study in Neural Computation, we demonstrate how the modulation of movement duration can emerge as a natural property of task-dependent control policies within an infinite horizon framework, rather than requiring a priori specification. Full story ➡️ direct.mit.edu/neco/article...
Infinite Horizon Control With Nonlinear Dynamics Models Reproduces Temporal Modulation of Reaching Movements
Abstract. Movement duration, a fundamental aspect of motor control, is often viewed as a preprogrammed parameter requiring dedicated selection mechanisms. An alternative view posits that movement dura...
direct.mit.edu
What stands out to me in this paper is the emphasis on good old engineering and modeling compared to the current trend of throwing transformers/data at every robotic problem!
Ale Santuz@alesantuz.bsky.social · 4mo ago
New preprint: "Linear equivalence of nonlinear recurrent neural networks." For large nonlinear (potentially chaotic) RNNs with random connectivity, the full N×N covariance matrix takes the same form as that of a ~linear~ network with the same couplings, driven by independent noise.
Linear equivalence of nonlinear recurrent neural networks
Large nonlinear recurrent neural networks with random couplings generate high-dimensional, potentially chaotic activity whose structure is of interest in neuroscience, machine learning, ecology, and o...
arxiv.org
✍️ In the @kempnerinstitute.bsky.social blog: our new tool built to compare the dynamics of complex systems when both internal circuitry and external inputs shape their behavior. Catch @annhuang42.bsky.social presenting this work at #ICLR! kempnerinstitute.harvard.edu/research/dee...
InputDSA: Demixing then comparing recurrent and externally driven dynamics in complex systems - Kempner Institute
We explored how to measure the similarity between two complex systems when they are driven by external inputs, like biological neural circuits or reinforcement learning agents. Our novel method, calle...
kempnerinstitute.harvard.edu
Task demands shift motor learning from adaptation to feedback control in a naturalistic bimanual task www.biorxiv.org/content/10.1...
"Science is often slow, repetitive, and unpredictable, but engaging with it from a different angle can restore perspective and motivation. For me, stepping outside the lab did not take me away from science. It helped me rediscover why I cared about it" www.science.org/content/arti...
Science | AAAS
science.org
New preprint from my lab! We study how reinforcement learning & selective attention interact. To do so, we built a set of models describing different ways that value & reward prediction error can modulate top-down attention. We compare model outcomes to monkey data from a color value learning task
bioRxiv Neuroscience@biorxiv-neursci.bsky.social · 5mo agoModulation of feature attention by reward prediction error explains value learning behavior https://www.biorxiv.org/content/10.64898/2026.04.10.717847v1
➡️ Robust control (reject "unmodelled" disturbances), ➡️ Online adaptive control, ➡️ Trial-by-trial adaptation. These components are separable behaviourally and reveal individual traits characterising how we handle external disturbances...
Very happy to put this work out! Movement errors are reduced even in unpredictable environments, where anticipation is not possible. We addressed the complex processes interacting within an ongoing action to achieve this...
Fred Crevecoeur@fredcrevecoeur.bsky.social · 5mo agoPreprint out by @harikalidindi.bsky.social : reaching movements result from multiple control and adaptation processes dependent on whether the context is predictable: www.biorxiv.org/content/10.6...
PL Neuro is officially live! plneuro.xyz We exist to break bottlenecks, accelerate progress in neurotech & NeuroAI, and to invest in innovations that benefit humanity. PL Neuro will focus on 3 core areas: - Neural augmentation - Biologically-inspired intelligence - Whole brain emulation
Homepage — PL Neuro
plneuro.xyz
#philsky #philsci #booksky #evosky #histsci #AcademicSky #BNPreorder
Alejandro Fábregas-Tejeda@alejandrofabregastejeda.com · 6mo ago📢 @barnesandnoble.com is offering 25% off book preorders from today through March 26th, including my forthcoming The Organism–Environment Pairing! (@mitpress.bsky.social) 📗👇 www.barnesandnoble.com/w/the-organi... If you were thinking about getting a copy, now is the perfect time! #HPS #booksky🐋🌱
Very late to the show on Bluesky but finally found time to join! And I’ll start with some belated news 🎉 I have officially started my own research group as a CNRS researcher at the Institut de Neurosciences de la Timone in Marseille 🎉
Thanks! It's always a concern, but here we have some extremely different ask structures actually, e.g., our human participant moving objects across a table in trials order of ~ 10s, compared to mice lever pulling in trials order of ~ 100ms. Which makes me think there's more to it than that
Very happy to share our review on Reinforcement Learning vs Statistical Learning, with @ambrafer.bsky.social and @predictivebrain.bsky.social: www.sciencedirect.com/science/arti... A nice summary: www.sainsburywellcome.org/blog/two-eng...
Where learning paths meet: Convergence and divergence of statistical and reinforcement learning
Learning enables organisms to adapt to a dynamic world by forming and updating internal representations of their environment. Statistical Learning (SL…
sciencedirect.com
Sainsbury Wellcome Centre@sainsburywellcome.bsky.social · 6mo agoNew review maps the convergence and divergence of statistical and reinforcement learning, from origins to neural implementation. @athenaakrami.bsky.social, @ambrafer.bsky.social & Floris de Lange Blog: www.sainsburywellcome.org/blog/two-eng... Paper: www.sciencedirect.com/science/arti...
Favourite paper I have read this year. Check it out! Great work @harikalidindi.bsky.social and @fredcrevecoeur.bsky.social!
Fred Crevecoeur@fredcrevecoeur.bsky.social · 7mo agoPaper is out by @harikalidindi.bsky.social : feedback control model of neural population activity www.cell.com/cell-reports...
Enjoyed a lot doing this work with @fredcrevecoeur.bsky.social throughout! Glad it's finally out 🎉🎉🎉 Here is the accompanying code for implementing: github.com/neurohari/si...
GitHub - neurohari/simulateLQRNetworks: code associated with the article "Feedback control of random networks as a model of motor cortical dynamics across tasks"
code associated with the article "Feedback control of random networks as a model of motor cortical dynamics across tasks" - neurohari/simulateLQRNetworks
github.com
Fred Crevecoeur@fredcrevecoeur.bsky.social · 7mo agoPaper is out by @harikalidindi.bsky.social : feedback control model of neural population activity www.cell.com/cell-reports...
Amid the rise of billion-parameter models, I argue that toy models, with just a few neurons, remain essential—and may be all neuroscience needs, writes @marcusghosh.bsky.social. #neuroskyence www.thetransmitter.org/theoretical-...
Not playing around: Why neuroscience needs toy models
Amid the rise of billion-parameter models, I argue that toy models, with just a few neurons, remain essential—and may be all neuroscience needs.
thetransmitter.org
Now published in the Journal of Neurophysiology: journals.physiology.org/doi/full/10.... Get in touch if you think this tool could help in your science! We will be developing improvements and extensions over the next year.
ATHENA: automatically tracking hands expertly with no annotations | Journal of Neurophysiology | American Physiological Society
Studying naturalistic hand behaviors is challenging due to the limitations of conventional marker-based motion capture, which can be costly, time-consuming, and encumber participants. Although markerl...
journals.physiology.org
Jonathan A. Michaels@jonathanamichaels.bsky.social · last yr.We're thrilled to introduce ATHENA: Automatically Tracking Hands Expertly with No Annotations – our open-source, Python-based toolbox for 3D markerless hand tracking! Paper: www.biorxiv.org/content/10....
1/ Why does RL struggle with social dilemmas? How can we ensure that AI learns to cooperate rather than compete? Introducing our new framework: MUPI (Embedded Universal Predictive Intelligence) which provides a theoretical basis for new cooperative solutions in RL. Preprint🧵👇 (Paper link below.)
Good piece by @kohitij.bsky.social on why neuroscientists use an "outdated" vision model. Neuroscience is different than AI and that's ok! medium.com/@kohitij_716...
Why AlexNet Died in AI but Lingers in Neuroscience — Through the Lens of Popper and Kuhn
When I talk to any of my machine learning researcher colleagues in 2025, they tell me that a model from 2023 is prehistoric. In AI, a year…
medium.com
The brain computes by processing information over time through interactions between connectivity and dynamics that are hard to model. Here we infer these interactions from data and find they better predict cognitive performance! www.nature.com/articles/s41... w/ @lindenmp.bsky.social
Join us for Fall 2026. In our group, you can run studies from human behavior and neuroimaging, to large-scale NHP ephys, and join them up with a robust computational foundation. Bonus: you can help build the reading list.
Jörn Diedrichsen@diedrichsenjorn.bsky.social · 10mo agoThe Sensorimotor Superlab with @gribblelab.org and @andpru.bsky.social is a unique place to work and learn. We are now accepting MSc and PhD applications for Fall 2026. Join our awesome team at Western University... For application instructions see diedrichsenlab.org and gribblelab.org/join.html!
