Kaustubh Kulkarni

Research Track Resident | @YalePsych | previously @IcahnMountSinai | 🧠 Studying craving and addictive decision-making.

[The Transmitter @_TheTransmitter]

The neuroscience field is grappling with whether to modify the long-standing theory of reward prediction error—or abandon it entirely.

By @avaskham

https://t.co/aaqxBxTpEs

[Anna Konova @_AnnaKonova_]

A quick read by me and @AZilverstand highlighting new work in @BiologicalPsyc1: CNNI by @krkulkarni_ Matt Schafer @xiaosigu and colleagues that derives a generalizable and interpretable classifier of cannabis use linked to craving from fMRI cue reactivity
https://t.co/ZlyFbXE471

[Ankur Handa @ankurhandos]

@sarahookr I like this https://t.co/y7csIP8oYq

[PyMC Labs @pymc_labs]

💥Announcing CausalPy💥 A Python package for Bayesian causal reasoning in quasi-experiments.

This is a @pymc_labs supported ptoject, driven by Benjamin Vincent (@inferencelab)

Find out more in our beta release announcement ➡️ https://t.co/NAkJ2wXTp6

[Saleem Bokhari @SaleemBokhari1]

1. How to write your First Research Article & get it published (Everything you need to know) 🧾🖋
📖 A 14 points detailed thread for Beginner Researchers
@PhDVoice @PhD_Genie @firstphdchat @hapyresearchers @careerconversa1 @ThePhDPlace @PRGPakistan
#phdchatter #research

[Kaustubh Kulkarni @krkulkarni_]

@mertrory @SaveToNotion #thread

[Mert R. Sabuncu 🤖🩻⚕️ @mertrory]

Some advice I give my graduate students about how to keep on top of the literature in their field. A 🧵: 1/

[David Chalmers @davidchalmers42]

so, what's the best reason to think large language models are not sentient? more precisely: what's the best candidate for X such that LLMs clearly lack X and X is required for sentience?

[Friedman Brain Institute @SinaiBrain]

TOMORROW, 10/7, at 1pm EST for @sinaiCCP's monthly Speaker Series. Dr. Dion Khodagholy @dk2955 will be presenting "Translational Neuroelectronics" — see details below! JOIN US👉https://t.co/tkSMnpvQmr @xiaosigu @krkulkarni_ @christinamaher_

[James Whittington @jcrwhittington]

If everything's a big manifold, why do neurons often code for human-interpretable factors? In https://t.co/xCaJbmtwwi we show the most efficient biological representation puts different factors in different neurons. This lets us build machines that disentangle too! 1/9

[Kat Hoffman @kat_hoffman_]

Visual Guide to Inverse Probability Weighting (IPW) ⚖️

IPW estimators use the prob. of treatment or propensity score (PS) to reweight an outcome.

IPW estimators rely on getting the PS right. You can use a doubly robust method to lessen that reliance!

🎨https://t.co/QoOPGPEQUc

[Shawn Rhoads @shawnrhoadsphd]

I can cry thinking about how much time this will save me (without sacrificing reproducibility). Thank you @RichardGerum for developing this awesome tool! 🙏

docs: https://t.co/VwFKXYGjJY
paper: https://t.co/68jrhz5HCn
demo: https://t.co/4AjlAWg1oS

#python #pylustrator #phdchat

[NARC @bic_ismms]

In collab with Twitterless Daniela Schiller, we will embark on a newly NIH-funded MRI study to behaviorally (reconsolidation) and pharmacologically (methylphenidate) enhance forgetting of drug memories in people with addiction. Looking for post-doc training? e-mail RZ Goldstein

[Luiz Pessoa @PessoaBrain]

𝗗𝗲𝗰𝗼𝗱𝗶𝗻𝗴 𝗶𝗻 𝗻𝗲𝘂𝗿𝗼𝘀𝗰𝗶𝗲𝗻𝗰𝗲 is cool but want to go beyond that?
"Predictive models avoid excessive reductionism in cognitive neuroimaging" by Gael Varoquaux and Russell Poldrack.
https://t.co/tgvmeMHZt4

[Patrick Mineault @patrickmineault]

Want to make awesome browser-based visualizations? I just updated my blog post with gradio, Pyscript and Idyll. Python users, learn you a Javascript for great good! https://t.co/4Oi5Flof0S

[Kirk Borne @KirkDBorne]

700-page PDF — #Algorithms for Decision-Making — download brilliant & comprehensive FREE eBook from MIT: https://t.co/TRP1USGDrF
—————
#abdsc #AI #MachineLearning #BigData #DataScience #DataScientists #Mathematics #Statistics #DecisionScience #DataLeadership #AnalyticsStrategy

[Max Kleiman-Weiner @maxhkw]

Humans switch from model-free to model-based reinforcement learning only after receiving instructions about how an abstract task works. Social learning is a hidden default in many "learning from experience" experiments
https://t.co/Xh59FkrCXC

[Sheila Shanmugan @SheilaShanmugan]

Super excited to announce that our work on sex differences in personalized functional networks is now published in PNAS! Huge thank you to
@sattertt
and all of our amazing collaborators!! https://t.co/N4ihy3799a #PennLINC

[Agoston Mihalik @AgostonMihalik]

Thrilled to share our tutorial and toolkit on Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS) with @chapmajw @dr_rick_adams @NilsRWinter @FSFerreir @johnshawetaylor @MouraoMiranda: https://t.co/czvEoxaBuX
https://t.co/2rTMiyERcL 1/7

[Majid Saberi/مجید صابری @_majidsaberi_]

Shen's parcellation looks more informative when projected to Yeo's canonical networks!We categorized ROIs and interpreted our findings better,successful execution of standardizing parcellation approach
https://t.co/UqRZjTztmZ
https://t.co/VMxLOQcFTf
@bttyeo @parkvillegeek @esfinn