I am delighted to share that my doctoral research was published online @NatureNeuro last week. Incredibly grateful to my advisor @ValerioMante, Maneesh Sahani, and John Reppas/Bill Newsome (for sharing data). A brief summary.1/13
https://t.co/MWTxZwEWSo
I am extremely happy to finally be able to share this, after lots of work and several iterations. It was a privilege to work with my advisors, Maneesh Sahani and @ValerioMante. Special thanks to Valerio, who's support was critical to make this happen.
https://t.co/tdPS0KAp0E
#Tweetprint time 🤓Our study is finally out on @CurrentBiology ! 🧠How learning is affected by task-irrelevant noise? Can this tell us about where learning is located? 1/n
https://t.co/Dj57RbQzsM
Our new pre-print is out today! We demonstrate a brain-computer interface that turns speech-related neural activity into text, enabling a person with paralysis to communicate at 62 words per minute - 3.4 times faster than prior work.
https://t.co/jK1rdMT5Zl
There will never be another, but the world (and science) would be better off if everyone followed the rare example @shenoystanford set. He always cared about people first. The fact that that led to better science somehow seemed incidental. We'll miss you forever
Saddened by this news. Had the privilege to meet @shenoystanford in 2019. I was admittedly nervous, but was blown away by his warmth & humility. His lasting legacy will be the long list of fantastic people he trained and the impact his work had on so many of us. RIP
So sad to hear about Krishna. A brilliant scientist that transformed his field, a great mentor, a generous human that touched family, friends and colleagues #thankyoukrishna@shenoystanford
I am delighted to share that my doctoral research was published online @NatureNeuro last week. Incredibly grateful to my advisor @ValerioMante, Maneesh Sahani, and John Reppas/Bill Newsome (for sharing data). A brief summary.1/13
https://t.co/MWTxZwEWSo
I would like to thank the reviewers @NatureNeuro for their insightful feedback, and @SCglobalbrain, @snsf_ch for funding. Code is available on my Github. https://t.co/ak7kf2vfDw. 13/13
Finally, in idealized simulations of modular RNNs, we show that our approach can – (i) infer local recurrent dyn. from multi-area recordings, (ii) identify dynamic patterns of inter-areal communication, (iii) generate predictions of the effect of small, causal perturbations.12/n