Marine Schimel

Postdoctoral Researcher in Computational Neuroscience @ Stanford | Previously PhD student @ Cambridge CBL

[Marine Schimel @marineschimel]

I'm very excited to share a (slightly delayed) thread on our recent work with Mitra Javadzadeh, @GJEHennequin, @yasharadian and @SonjaBHofer, investigating how connections between cortical areas shape computations in the neocortex! Details at https://t.co/PWFOSNdZpC

[Sonja Hofer @SonjaBHofer]

Cool study led by Mitra Javadzadeh and @marineschimel on the function of inter-areal communication in the neocortex, using data from my lab and biologically-constrained latent circuit modelling with @GJEHennequin and @yasharadian https://t.co/yJwwnN9ulB

[Laureline Logiaco @LLogiaco]

The video recordings from many of these talks are now available! These could be great resources for anyone interested in theoretical and computational neuroscience. Thanks again to the organizers for putting together such a great event!

https://t.co/7jJQFYBiWg

[Javier Antorán @JaviAC7]

Super excited to announce @Angstrom_ai, our startup using Gen AI to build fast and experimentally accurate simulations of molecular interactions together with @SilkyDogfish, @jmhernandez233 and Gabor Csanyi.
We are backed by @ycombinator!
See below for a simulation demo (1/3)🧵.

[Machine Learning in Science @mackelab]

We are very excited to share our new work, which unifies probabilistic neural encoding, behavioral decoding, and joint dimensionality reduction in a single model.

How?

Through modeling conditional distributions in VAEs!

Preprint: https://t.co/OfYpCrDcj4

[Jorge A Menendez @JAMenendez11]

Animals can learn to use all kinds of brain-computer interfaces, from controlling single neurons to large populations of neurons in motor ctx. How do they do this?

Thrilled to be finally sharing our research on this question, now up on biorxiv! 🧵

[Cold Spring Harbor Laboratory @CSHL]

Wishing a warm welcome to new CSHL Fellow Mitra Javadzadeh, who joined our neuroscience faculty! Her lab studies neural computations behind visual perception and the ways in which the brain processes visual information. 🧠
https://t.co/620BgKlYAZ

[Guillaume Hennequin @GJEHennequin]

Exciting postdoc position between the labs of @lengyel_m + @GJEHennequin (Cambridge U) and @DMWolpert (Columbia U) to study the neural network mechanisms of context-dependent (continual) motor learning. Máté and I will be at Cosyne, come talk to us! https://t.co/ihBcer1SH9

[Javier Antorán @JaviAC7]

SGD can draw posterior samples from GP conditioned on millions of observations with surprising accuracy😯!

New paper with @JihaoAndreasLin @shreyaspadhy David Janz @jmhernandez233 @avt_im text: https://t.co/0grL1OAKSb 🧵

[Javier Antorán @JaviAC7]

Registration for #AABI2023 in-person in 🌴Hawaii🌴 (co-located with ICML) is now open at https://t.co/XwQ1gnpiYT !!

Spots are free but limited.

[HHMI | Janelia @HHMIJanelia]

🚨 Attention grad students and postdocs in theoretical neuroscience 🚨

Come to @HHMIJanelia this fall to share your work, explore the latest advances and network with colleagues.

🗓️ Apply for our junior scientist workshop by July 5 ➡️ https://t.co/v1mVa0Ogo7
@janeliaconf

[Joanna Chang @joanna_chang_]

🚨Tweetprint time! 🚨
We studied how de novo motor learning alters the underlying neural activity & affects motor adaptation using RNNs 📜 https://t.co/8Dq74VfXN5

Special thanks to coauthors @mattperich @PresNCM @JAlGallego @ClopathLab

Pls read 🧵 + RT! 1/n

[Marine Schimel @marineschimel]

Big thanks to @kao_calvin, @GJEHennequin, & everyone else who provided input along the way😊 I’m excited to see how this optimal control framework might generally be useful to understand the interaction between neural dynamics and inputs! (5/5)

[Marine Schimel @marineschimel]

Very excited to share our recent work w/ @kao_calvin & @GJEHennequin! We investigated when & why motor preparation arises in RNN models of motor control. Is preparation optimal for RNNs performing delayed reaching tasks? Spoiler: it is! (1/5) https://t.co/EuaEQ3uGdw

[Marine Schimel @marineschimel]

Digging deeper, we explored how preparation depends on network dynamics/connectivity, using control theory tools to understand which networks benefit most from preparation. (4/5) ⚙️

[Kristopher Torp Jensen @KrisTorpJensen]

We're also happy to share our 'tutorial on generative models', which @marineschimel, @davindi09 and I generated for this workshop:
https://t.co/AazoTH1jtB

It consists of three notebooks giving an overview of some of the models discussed in the workshop!

[Chethan Pandarinath @chethan]

Great upcoming talks at our #Cosyne23 workshop on generative modeling of neural and behavioral data! 🏆

I'm excited to present at 4:30p!
+@scott_linderman @neuronair @theiStone @anqiwu_angela M Dowling & C Savin.

Thanks @KrisTorpJensen @marineschimel @davindi09 for organizing!

[Marine Schimel @marineschimel]

⛷️Very excited to be organising this workshop on generative models for neural & behavioural data analysis at #cosyne2023! ⛷️
We have a lot of exciting talks lined up, covering both methodological advancements and some very cool applications of generative models🧠

[Kristopher Torp Jensen @KrisTorpJensen]

What I cannot create I do not understand: analyzing neural and behavioral data with generative models!

Thrilled to run this @CosyneMeeting workshop with @marineschimel & @davindi09! Find us in Mont-Tremblant on the 14th and on https://t.co/L78ViKA1Em