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Short answer is no. The field is full with tenuous analogies. Then again „Neural Networks“ are also at best metaphorically related. More accurate existing Neuron models are actually also plagued by lots of limitations among them that they are typically implemented in 3 ancient domain specific languages with lots and lots of hardcoded constants copied from research papers.

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spiking neural networks are interesting though, and new computational substrates that allow for experiments at larger scales could produce some interesting results.

today's sum n' squash (sometimes not even squash) graph networks were just kind of a curiosity before gpus turned them into a new very successful computational paradigm. maybe we'll see something similar with these high element count optical spiking graphs, even if they aren't great approximations of the real biology.

i like to think that a new analog computational substrate (or mixed analog and digital system) will be what drives the next leap in machine computation.

I'm excited to see where spiking NNs go. Something like that is needed to progress now that conventional NNs on GPUs are pretty much tapped out (in terms of non-incremental advances) from their power consumption and the end of Moore's law. Things really do need to be more hardware-efficient.

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