I'm in the beginning stages of planning a project to perform whole brain emulation and am considering using NuPIC as the engine. Unlike the BBP/BBP, my plan is to focus on biological plausibility at the macroscale level of nuclei, rather than concern for plausibility at the meso or neuronal level for the sake of computation requirements. This is similar to the idea behind Spaun, in that they are shooting for functional realism rather than biological fidelity.
I started building my models using the Nengo platform (which is used by Spaun), but because it still shoots for relative biological plausibility at the neuronal level, it's computational demands are simply too great for my purpose (and my limited hardware). Therefore, my intent is to take my models (beginning with a simplified caudate nucleus, simple sensory and motor cortices, rudimentary thalamus and possible a basic hippocampus) and implement them on top of the NuPIC or similar engine. I envision this as multiple distinct networks, each interconnected, as in the thalami-cortical loop. Once I'm able to get this simplified model performing correctly, I plan on building out additional structures and refining the existing ones, as well as their connectivity in an iterative fashion guided by experiment. My question to the NuPIC group is, do you envision any architectural limitations in NuPIC that might be of concern in using it in this manner, or any thoughts on why NuPIC may or may not be a good fit for this project? I've been toying with NuPIC since it's first release, but only as a replacement for simple ANN applications, but I've just recently subscribed to the maillist so if this is not the correct venue for a question such as this, kindly let me know and I'll direct it elsewhere. Thanks, Dean Horak
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