course-content VS Kilosort

Yes. And if you want to get a jump start on what a computational neuroscience PhD might look like, check out our materials at https://compneuro.neuromatch.io. Like a PhD in a box... and all free to use/explore!

The neuromatch computational neuroscience course also seems quite interesting, though maybe less of practical use.

https://compneuro.neuromatch.io/

Recent research like "Relating transformers to models and neural representations of the hippocampal formation" might make it more relevant though (https://arxiv.org/abs/2112.04035v2)

quote from the abstract of that paper:

check out the curated datasets and project outlines for Neuromatch Academy's computational neuroscience course for some inspiration! https://compneuro.neuromatch.io

One possible thing I saw is taking https://compneuro.neuromatch.io/ courses, Although I'm not sure of the effect it adds to my CV.

https://github.com/NeuromatchAcademy/course-content https://github.com/NeuromatchAcademy/course-content-dl

If you'd like to learn more about it, you can check out last year's Comp Neuro course contents here, last year's Deep Learning course contents here, read the paper we wrote about the original NMA here, read our Nature editorial, or the Lancet article00074-0/fulltext) about us.

Check out Neuromatch Academy. If you don't have time or inclination to do the summer school this summer, you can simply access all the videos and tutorials for free at https://compneuro.neuromatch.io

https://compneuro.neuromatch.io has all the materials for free, including lecture videos and hands on Google Colab notebook programming exercises. You can also apply to take the course synchronously with a "pod" of 10 other people in a 3-week summer intensive online, with a TA to guide you and group projects to cement your understanding. To apply, check out http://academy.neuromatch.io. Applications open soon and it sounds like you'd be a great fit.

Yes, Neuromatch Academy. Their whole course is available as a Jupyter Book here

Maybe better than any single book or paper, I would recommend you check out neuromatch academy. There are 2 courses, computational neuroscience and deep learning and everything is free and open source. Play with the code, listen to the lectures, even do a "canned project" if you want experience.

And yet, we are starting to make sense of it all! The experimental tools are really what makes the difference. Lots of recently developed genetic tools (such as optogenetics ) are now allowing us for the first time to activate/inactivate specific neurons and molecular pathways and observe the causal effects . Other genetic methods (like Crispr) allow us to create mutant animals where a specific gene is mutated, so a lot of work is now in rodents, but I expect it to translate to humans in the next decade or so. Another really crazy one are cerebral organoids, where we grow in vitro a simplified human brain from stem cells to study the details of development and the function of different genetic pathways. When it comes to brain function, the ability to record from tens of thousands of neurons simultaneously using new probes like Neuropixels has been a recent game changer. Also, machine learning approaches are now incredibly useful to analyze data, from detecting the individual "spikes" to developing complex models of the brain dynamics to support computation. To give you a concrete example of progress, we can now read a person's mind so he can write words on a screen, pretty much as quickly as if he was typing them on a keyboard.

I'm assuming by clustering neurons, you mean clustering spike data. There are several excellent options that already exist for this purpose, the most notable of which is KiloSort.