If we did, we probably would have built something like that - Google or Apple would easily throw $100M at that project.
You're probably talking about IBM's TrueNorth (http://wikipedia.org/wiki/TrueNorth), which simulates 1M neurons with 256 connections each. So yeah, it's the right direction, but it's 9 decimal orders of magnitude away from the human brain. And that was like a $10-20M project. If you just scale it linearly to 100 billion neurons, it will cost more than Google/Apple is worth.
Realistically, if improvements continue in the exponential fashion, maybe in 4-10 years it will become achievable without going broke.
We have large scale FPGA devices. Adapting this to be not just reprogrammable in a formal capacity, but constantly self-reprogramming isn't a huge conceptual leap even if it is an enormously complicated thing to prototype and get working.
This is just how the technology behind the transistor made modern computers possible, we've just been iterating and refining on the same basic principle ever since, even though some of those iterations are very painful and expensive to get working. See the current pains around 10nm processes.
When I say we have the technology I don't necessarily mean we have the will or the budget to pursue it. As the costs come down it's inevitable someone will find a solution that's not billions of dollars, but instead mere hundreds of millions.
We're a long way from a proven, working design and process, but at least we can make such a thing, theoretically speaking.
That's maybe enough to simulate the cockroach brain.
http://wikipedia.org/wiki/List_of_animals_by_number_of_neuro...
Don't get me wrong, I really really really want this to happen. I just doubt it will happen in the next 3-5 years.
There are other challenges, main one being creating the right connections. It took a long time for the evolution to create and perfect our brain. And it was a highly parallel process too.
100 billion transistors isn't a lot. The interconnect is a beast, but as we get better at layering chips this might not be a big deal.
The thing about biological neurons is they run at very, very low speeds, on the order of KHz, which means the heat normally associated with high transistor densities is a non-issue. You could create a chip so dense it's basically a cube without concern it was going to melt down.
The biggest problem with replicating this sort of thing in silicon is that the neurons reconfigure themselves physically, something not possible with today's VLSI technology.