RT @ThomasVanRiet2: All of David Tong's (freely available!) lecture notes are now also available in published book formats. .

RT @surojitsural: Towering behavior and collective dispersal in Caenorhabditis nematodes.

We are happy for feedback and plan to add more! You will also find most of my activity on the other site.

The tutorials explore principal components analysis, state space embedding, and Markov modeling, all through the dynamics of posture. They are the result of tremendous contributions from my own group members, and constructive feedback from participants at special-topics schools.

A vital and very enjoyable component of my academic work is teaching students at all levels how to extend the frontiers of knowledge. So I’m pleased to announce a set of Jupyter notebook tutorials organized around our ideas for “physics of behavior”:

Rest in peace, puff

RT @ItaiYanai: The 1st rule of night science is that only when we’re not trying to be efficient is there actually a chance for a new idea t….

These are fantastic!.

RT @APSphysics: 🐟 Researchers created a 3D tracking system to capture male #zebrafish movements during dominance battles. They observed uni….

And thank you @HFSP for the chance to do something really new!.

RT @EMBLEvents: 🤔 What does it mean to understand biology?.📖 What makes theory in biology different?.🧬 What are the emerging concepts in th….

RT @SebastianSeung: 🧵on Japan's underrated contributions to neural nets. Shun-ichi Amari @UTokyo_News_en @riken_en is another one of my her….

RT @RuvinskyIlya: Excellent and promising work by @aexbrown and team. In @elife: Systematic creation and phenotyping of Mendelian disease….

But in detailing exactly what we can model, we also better expose the dynamics that lie underneath. For example, adaptation or long-lived internal states. And that’s another deep problem with practical significance!.

Does our model contain all of the dynamics? Of course not. In particular there are longer-time and non-stationary behaviors that we cannot capture.

Using Resistive Force Theory, we could even use our model to generate realistic trajectories in the environment.

Despite the tremendous microscopic complexity (yes even in the worm-I’m looking at you vertebrate and mammalian enthusiasts), we constructed a remarkably effective Markov model dynamics, inspired from ideas in dynamical systems, statistical physics and information theory.

Here are two very nice popular science summaries, thank you @OISTedu @VU_Science @PhysicsAstron_.

Thus we’re pleased to share our @antoniocbscosta @_mlechha answer to this challenge using the nematode C. elegans.@PNASnews