Your numbers are absurdly optimistic. For starters, analog signals are a lot harder to simulate than digital circuits in silicon. The simplest fourth order Rung

Your numbers are absurdly optimistic. For starters, analog signals are a lot harder to simulate than digital circuits in silicon. The simplest fourth order Runge-Kutta needs 4 evaluations of the gradient plus 8 floating point operation per timestep per dimension and probably you can get away with something like an order of magnitude smaller timestep than the dynamics you want to simulate. That is a penalty of something like two order of magnitude plus the evaluation of the gradient. Second, the complexity does not lie in the neurons, there one gets away with something like 100 billion neurons * 100 Flops/ GHz (Prozessor) ~ 100 Prozessors per dimension if we completely neglect the gradient. But what we need is the dynamics of the connections. And based on that, the estimate of 100 PFlops simulation gives roughly 10^5 clock cycles per connection, which is not very much if you compare it to the latency of a standard database in the microsecond range. (Network latencies are in a similar order of magnitude.) About your argument, that it would be enough to only simulate firing neurons, well we have to simulate a neuron to detect if it is firing. For completeness, Kurzweil mentions in The Singularity is near much lower estimates for functional simulation of a brain in the TFLOPS range, however that is with optimized algorithms and we probably need a singularity first to get these optimized algorithms.