It's deep learning day at #NeuromatchAcademy! (github.com/NeuromatchAcad…) Some highlights:
Tutorial 3 by @JAMenendez11 and @Yalda_Mhz (edited by @EllaBatty and others) will guide you through representational similarity analysis: colab.research.google.com/github/Neuroma… Before you start on that, I'd recommend watching Aude Oliva's fascinating introduction to deep convolutional networks and their application in neuroscience:

Releasing #cellpose, a generalist algorithm for cellular segmentation. Try it now directly on the website cellpose.org, install the code package github.com/MouseLand/cell…, read the preprint biorxiv.org/content/10.110… or watch the talk . We developed Cellpose as a generalist algorithm, because many small and big problems in biology require cell segmentation, and there just isn’t enough time to write a new pipeline for every type of data.

Story time! Single neurons in the brain can’t be depended on for reliable information. Here are some neurons from our recent study, recorded twice in response to the same visual stimuli. Different neurons are active at different times!
biorxiv.org/content/10.110… Ask a neuron what angle the corner of your screen makes and it will say 75 degrees right now, 100 degrees in 5 minutes, and some other random number close to 90 every time you ask.

Thread: A picture is worth a thousand words, and your brain needs billions of neurons to process it. Why do we need so many neurons? To find out, we recorded thousands of them in mouse visual cortex. Here’s some data, and a link to the paper:
biorxiv.org/content/early/… 2. One reason to have so many neurons may be that they each have different jobs:
Neuron A recognizes the pointedness of a fox’s ears,
Neuron B recognizes the color of the fox’s fur.
Neuron C recognizes a fox nose,
etc