@neuralreckoning @neuromatch

This sounds like a very exciting course! Obviously neuro is too broad to cover everything, so I think it is still important to somewhat pick content based on 'likelihood of being important in the future'. If I had to pick two directions which might be helpful for future developments in AI, I believe I would go for (1) Mechanisms of higher cognitions and (2) architectures at the foundation of neuromorphic computing.

@neuralreckoning @neuromatch

Re (1) - Higher Cognition:

There still is a lot of discussion that what is essentially missing in AI is some sort of higher cognition. This was well covered by this classic workshop paper https://baicsworkshop.github.io/pdf/BAICS_10.pdf -- we also wrote a piece recently for a AAAI workshop which has a similar line of reasoning but discussed them a bit more closely with regards to implementation and some existing ML architectures https://arxiv.org/abs/2303.13651

@neuralreckoning @neuromatch

Re (2) - Neuromorphic:

I still believe that neuromophic chips have the potential to significantly disrupt the accelerator market in the future. So I think explaining what is behind the spiking neuron models, gradient descent problem in spiking nets etc is relevant. You obviously are an expert in all these things!

@neuralreckoning @neuromatch

As a side note, I do think that if its mostly computing scientists taking the class that mentioning that even folks like von Neumann already were very interested in the brain (https://en.wikipedia.org/wiki/The_Computer_and_the_Brain) before we ever thought about simulating large scale neural networks on computers.

Let me know if you want to discuss anything in more detail. Would be super happy to contribute if you pick up any of those directions.