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"it takes, I think I read 10 ANN nodes to mimic the behavior of one biological neuron"

More like a deep network of 5-8 layers of 256 ANN nodes, according to recent work (turns out lots of computation is happening in the dentrites):

There's a fundamental difficulty with these sorts of attempts to emulate entire nervous systems (which gets exponentially worse as you scale up) that I don't think gets enough attention:  failure of averaging. See this paper on simulating single neurons:,does%20not%20contain%20its%20mean.

The abstract:

"Parameters for models of biological systems are often obtained by averaging over experimental results from a number of different preparations. To explore the validity of this procedure, we studied the behavior of a conductance-based model neuron with five voltage-dependent conductances. We randomly varied the maximal conductance of each of the active currents in the model and identified sets of maximal conductances that generate bursting neurons that fire a single action potential at the peak of a slow membrane potential depolarization. A model constructed using the means of the maximal conductances of this population is not itself a one-spike burster, but rather fires three action potentials per burst. Averaging fails because the maximal conductances of the population of one-spike bursters lie in a highly concave region of parameter space that does not contain its mean. This demonstrates that averages over multiple samples can fail to characterize a system whose behavior depends on interactions involving a number of highly variable components."

Historically, a similar problem was discovered by the US air force when trying to design the cockpits of fighter jets. They took anatomical measurements from hundred of pilots and designed a cockpit based on the average values, under the assumption that it would fit most pilots reasonably well. In actuality, it didn't fit anyone (they eventually solved the problem by making everything adjustable):