Take a look at the papers of Hubel and Wisel and mcculloch and Pits in 40s. If I recall correctly, you can see diagrams of a collection layered neurons. The architecture of a layer of simple cells and a layer of complex cells modeling the visual cortex is a layered architecture. The multi layer perceptron was proposed right after the perceptron. That was biological inspiration, and the analogy with layered “deep” brain. Fukushima revisited this layered architecture in the context of convolutional nnets.
Cybenko,Hornik etc studied the mathematical properties of multi layer feed forward nnets late 80s.
Obviously, in the field of systems control equivalent “deep learning” and “reinforcement learning “ were studied since 50s. This includes what’s called “back propagation algorithm”
It was all multi layer nnets until it was simulated.
Cybenko,Hornik etc studied the mathematical properties of multi layer feed forward nnets late 80s.
Obviously, in the field of systems control equivalent “deep learning” and “reinforcement learning “ were studied since 50s. This includes what’s called “back propagation algorithm”
It was all multi layer nnets until it was simulated.