The BBC article left out a critical constraint from DARPA. The final deliverable (with the "complexity of a cat's brain") isn't just a model or a simulation. It has to be a physical artifact which requires no more than two liters of volume and consumes no more than two kilowatts of power (this information comes from the DARPA BAA). So yes, prototyping will be done in software, but to reach that kind of efficiency, drastically new hardware is called for.

I'm not intimately familiar with IBM's plans, but this is one of the applications HP has lined up for the memristor technology they've been working on (HP is another one of the three prime contractors on the original DARPA grant). The benefit to the HP approach is that data and computation are both local to the applicable memristor, which is much closer to a neural system. That means no time or energy is wasted shuttling data around and the entire system state can be updated in parallel.

For an idea of why this is so exciting, keep in mind that HP plans build memristors at about a density of a trillion per square centimeter, clocked at about a kilohertz. You get the rough equivalent of one floating point operation per memristor per cycle. At this estimated manufacturing density, the expected performance of these things is on the order of a petaflop per square centimeter, drawing on the order of tens of watts. It isn't really fair to make a comparison to Von Neumann machines since the architecture is so dramatically different and so application-specific, but for certain kinds of computations these new chips will be vastly faster and more efficient.

(for the sake of disclosure, I'm working on the DARPA SyNAPSE project, but not with IBM)