rimmon @rimmon@qoto.org

#Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity https://www.frontiersin.org/articles/10.3389/fnins.2022.940972/full by @kateiyas et al.; #neuroscience #AI

Not been on here long, but a few
🧵 #feditips🦉 learnt:

🍄 Go-wide/follow more 🦔 Your timeline’s populated by who you follow so follow liberally (not just high-profile folks) and go to the edges of the fediverse (not just your own instance)

🦊 There’s no algorithmic boosting 📈 Boost liberally to share and promote

🐑 Share and promote toots not (high-profile) people 📢 To expand + thicken the network, do less sharing of those with lots of followers and promote toots that might not otherwise spread

Multisensory integration in the mouse visual cortex https://www.abstractsonline.com/pp8/#!/10619/presentation/83308 "The auditory cortex, for instance, sends numerous projections to the visual cortex in mice"; via Twitter, "What information does auditory cortex (AC) send to the visual cortex (VC) and how is this info distributed across visual areas?" https://twitter.com/Flor_Iacaruso/status/1591550779009253377 #SfN22 #SfN2022 #crossmodal #neuroscience

#art #loop #generativeart

Thanks to Harvard University, you can now virtually enter the Great Pyramid of #Giza in 3D and 360º

Hey #Bayesian #MCMC #stats #statistics #modeling people, are you aware of research on what model derived quantities can tell us about model misspecification? Eg. residuals, prior/posterior predictive checks, predictive performance and so on.

cc @rstats

I am looking for people who can translate example codes in #R to an equivalent #Python, #Julia, or #MS #Excel code for my #educational #datascience #blog.

https://dataalltheway.com

#rstats #statistics #MachineLearnning #deeplearnig

Science people! 👩‍🔬 👨‍🔬

There are some curated lists of scientists to follow on Mastodon here:

➡️ https://github.com/nathanlesage/academics-on-mastodon

(Originally shared by @kcarruthers but reposted so that I can pin the link on my account)

Referenced link: https://hackernoon.com/all-about-ens-current-value-decentralized-domain-names-and-what-to-expect-in-the-future
Discuss on https://discu.eu/q/https://hackernoon.com/all-about-ens-current-value-decentralized-domain-names-and-what-to-expect-in-the-future

Originally posted by HackerNoon | Learn Any Technology / @hackernoon@twitter.com: https://twitter.com/hackernoon/status/1590197500291399682#m

All You Need to Know about Web3 Centralization vs Decentralization: Websites, Domain Name Services, and More - https://hackernoon.com/all-about-ens-current-value-decentralized-domain-names-and-what-to-expect-in-the-future #web3 #centralization

The web-based open source software #CATMAID was devised as "google maps but for volumes". Documentation at https://catmaid.org and source code at https://github.com/catmaid/CATMAID/

Modern #CATMAID enables hundreds of #neuroscience researchers world wide to collaboratively map neuronal circuits in large datasets, e.g., 100 TB or larger, limited only by bandwidth and server-side storage. The goal: to map and analyse a whole brain #connectome.

Running client-side on #javascript and server-side on #django #python #postgresql, it's a pleasure to use–if I may say so–and easy to hack on to extend its functionality with further widgets.

The first minimally viable product was produced in 2007 by Stephan Saalfeld (what we now refer to, dearly, as "Ice Age CATMAID), who demonstrated to us all that the web, and javascript, where the way to go for distributed, collaborative annotation of large datasets accessed piece-wise. See the original paper: https://academic.oup.com/bioinformatics/article-abstract/25/15/1984/210794

See also public instances at the #VirtulaFlyBrain http://virtualflybrain.org/ particularly under "tools - CATMAID - hosted EM data such as this #Drosophila first instar larval volume of its complete nervous system https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=108250&yp=82961.59999999999&xp=54210.799999999996&tool=tracingtool&sid0=1&s0=2.4999999999999996&help=true&layout=h(XY,%20%7B%20type:%20%22neuron-search%22,%20id:%20%22neuron-search-1%22,%20options:%20%7B%22annotation-name%22:%20%22papers%22%7D%7D,%200.6)

#Drosophila larvae rockin’ and rollin’ by the hand of master @dantracey ’s lab. Opening the gates to understand how neural circuits control motor behavior via the #connectome

The four stages of Mastodon user:
* what’s a Mastodon
* I feel lost and alone how does this work
* oh wow this is actually pretty cool
* I have compiled a linux kernel

I'm reading Bayesian modelling and computation in #python as a way to break out of my usual silo.

https://bayesiancomputationbook.com/welcome.html

Really enjoying the slightly different perspective and approach so far so would recommend it.

Does anyone have suggestions for epi-related #pymc projects I can dig into before attempting to use in anger?

Hello #rstats users. I work on things #timeseries #AnomalyDetection #RPackages #visualisation #ggplot2 #python #datascience #Astronomy among other things. Hoping to connect with the @rstats community here.

re - #introduction because I've moved from mastodon.online (which appears to have server instability?) to sigmoid.social.

Hi all! 👋 I'm a PhD student at Johns Hopkins working on medical image processing, specifically super-resolution of MRI.

Looking forward to connecting with everyone!

#science #phd #mri #machinelearning

Super-res is an inverse problem and therefore we have to "make up" information. This is particularly dangerous in medical scenarios, so I'm working on understanding when (if ever!) machine learning-based super-res can safely be applied to MRI with signal processing foundations.

Lately, I'm moving more into just studying the forward problem (i.e., blurring with a point-spread function and sub-sampling the signal) before applying s.o.t.a computer vision models.