1. Yes, the brain is massively parallel and "analog" - BUT not every neuron forms a distinct cognitive function (FBNs) and neuron do fire in pulses/spikes (almost binary) rather than continuous (analog) outputs.
2. No, the article does NOT identify 'cpu cores' in the brain. It uses this metaphor (stated clearly in the paper as such) to point out the level of parallelism needed to run anything remotely similar to the complete functional 'package' in the brain.
3. The resolution of modern fMRI is at 3mm^3 (30K-50K active voxels) but this has to do more with the localization of the activations and less with the inherent complexity (dimensionality) of the spanned data space. In other words, in this work e.g. the visual center is detected as activated or not, regardless of how fine the resolution is.
4. The fMRI captures the complete 3-D brain volume, hence the detected activations include all the "always on" circuitry like respiration, cardiac rhythm, etc. Cognitive processes are only a few of these activations and are identified by experts when looking at the actual activation maps.
5. The methodology is completely data-driven and it includes two very popular non-parametric approaches: one is ICA for blind-source separation (measuring how many components are needed to describe the data) and the other is dataset fractal analysis (estimating the intrinsic dimensionality of any dataset). In both cases, the maximum number for such a plain visuo-motor task seems to be around 50.
6. The number 50 is only indicative, as it is measured for specific fMRI visuo-motor experiments. In intense cognitive situations, e.g. a pilot trying to land a plane on an aircraft carrier at night with bad weather, this is probably much higher - but in he same order of magnitude. On the other hand, when very small activations are ruled out (pre-processing by voxel smoothing), this number becomes much lower.
7. Currently, we have no idea how to develop a fully functional "brain" just by putting together 10 or 50 or even 1000 parallel processes. The simple idea of the data-driven approach is to point out that we should focus on independent -neural networks- rather than -single neurons- when trying to simulate an actual brain.
8. The current state-of-the-art neuromorphic chip by IBM provides just about 1/3 of a single voxel with 1/40 of neuron synapses within, so it is imparative to see how we can use these resources the best we can.
I hope these hints make things a bit clearer now :-)