I'm one of those people who research low precision NNs. There's a trade off between network size and network precision, so to get binarized networks to work "re

I'm one of those people who research low precision NNs. There's a trade off between network size and network precision, so to get binarized networks to work "reasonably well" you need to make them much larger in terms of weight count. SNNs are not well suitable for existing silicon based process technologies. It's much more power efficient to perform GEMM computation in analog domain, using non-spiking voltage or current levels (e.g. memristor or floating gate transistor crossbars). A brain uses spikes because of very specific biological constraints. We use silicon, because it's extremely reliable, extremely fast, and very cheap. And it does not have those constraints which shaped brain evolution. Brain simulation research is welcome, but the current SNN algorithms are just slightly less crude brain simplifications than regular ANNs. Using spikes in silicon to do ML computations does not make much sense, imo. I'd rather see neuroscientists focus on fundamental understanding of core brain algorithms, like what Numenta is trying to do.