The same model also explains the much faster transition in mouse. Apart from genetic oscillators the transcriptional hourglass is a second paradigm of how cells determine time.

Our work demonstrates emergence in the training of deep neural networks, which impacts the achievable performance of deep neural networks. By Pascal de Jong and Felix Meigel.

We derive a theory from first principles that predicts that the homogeneous state of deep neural networks is unstable in a way that leads to the emergence of periodic channel structures.

We show that this behaviour relates to a PRC2 driven phenomenon that resembles wetting of fluids. Check out the preprint to find out more. By Aida Hashtroud with Wolf Reik, @vonmeyennlab.bsky.social , @steglelab.bsky.social Mark Jan Bonder

Thanks, could you add me, please?

He's been throwing things from left to right 10^23 times...