A comment below makes an important point that I think is worth repeating: "The power of digital computing is the power of modular expansion of objects. Analog

A comment below makes an important point that I think is worth repeating: "The power of digital computing is the power of modular expansion of objects. Analog circuits and computers don't have that. And current trained deep learning models don't have combinability and modularity either." A point I'd like to make: the brain exhibits properties of both digital and analog computers. It also exhibits repeating units in the neocortex which do vary but are uniform enough that neuroscientists are comfortable classifying them as discrete units within the brain. I believe we must look to how the brain implements effective modularity in the context of analog computation in order to replicate the success of digital computers with deep nets.