Human neurons are continuous input, with active dendrites and dendritic compartmentalization. Spikey artificial NNs seem to hit problems with riddled basins so

Human neurons are continuous input, with active dendrites and dendritic compartmentalization. Spikey artificial NNs seem to hit problems with riddled basins so far. A riddled basin is a set with no open subsets. Feed forward networks are effectively DAGs, and circuit like, not TM like. Caution is warranted when comparing perceptrons with biological neurons. Dendrites can perform XOR operations before anything makes it to the soma for another lens. While there is much to learn, here is one highly cited paper on dendritic compartmentalization. https://mcgovern.mit.edu/wp-content/uploads/2019/01/1-s2.0-S0092867418311061-main-1.pdf https://mcgovern.mit.edu/wp-content/uploads/2019/01/1-s2.0-S... I think that the perceptron model of learning plasticity is on pretty shaky ground as being a primary learning model for humans.