Nets used for practical machine learning today are too abstract to incorporate them in any meaningful way. When you reduce the whole "neuron" to a weighted sum

Nets used for practical machine learning today are too abstract to incorporate them in any meaningful way. When you reduce the whole "neuron" to a weighted sum wrapped by some nonlinear function, in discrete timesteps, you've killed so much structure in the modelling it doesn't matter if the original information transfer path was supposed to be by membrane depolarization or by modifying cytoskeleton strands. Arguably if there is a cytoskeleton mechanism for signalling or storage it's captured in some ways in changing the weights and evaluating the net, but not if it had any unique function beyond the abstraction which was already in place from the conventional view. Studying other structures than the synapses and membranes is prudent, but Hameroff must be somewhere on the line between crackpot and intellectual poseur. Use your time on some real science instead. Here's one option: http://www.gatsby.ucl.ac.uk/~lmate/biblio/dayanabbott.pdf http://www.gatsby.ucl.ac.uk/~lmate/biblio/dayanabbott.pdf