Hello! I’m Oscar a undergraduate student in computer science and applied mathematics located in Melbourne Australia, I’ve been wanting to go into research in computational neuroscience and brain based AI. This kind of technology has been a passion and interest of mine since high school since deep learning and mainstream approaches always seemed inherently flawed (I only came aware of this project this year) so I hope I hope to learn about this project in depth and maybe contribute some new ideas or help out (right now its more of the higher concepts but less so the computational implementation so I have been reading a lot).
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Welcome aboard! Between the official docs and this channel, there’s a lot of interesting reading to do. Also, be sure to watch some of the videos!
Welcome @Oscar_Schmidt !
Hey Oscar, welcome to the next frontier. Many challenges lie ahead, so make yourself comfortable!
Here’s the tutorial to get Monty up and running on your machine if you wanna start digging into it:
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Nice to meet you @Oscar_Schmidt! Feel free to post if you run into any questions or issues 
Or if you are interested in any specific topics, I am sure we can also point you to resources to learn more about them (there is a lot!)
I’ve been wondering whether it might be useful to have a container-based version of Monty. This could reduce both the hassle and risks involved in getting it set up. Has anyone looked into that?
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Containers are advantageous when you have a stable program with specific system libraries that gets deployed on many machines.
Since Monty is not production-ready, that Conda already isolates the libraries, and that the userbase is rather small, I don’t think containerizing it would provide much value at this stage.
The setup is rather simple right now: install Conda, clone Monty, initialize the Conda env, and you’re good to go. The rest of the tutorials is just user configuration.
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Makes sense; thanks for the explanation…