"The model reproduces a number of physiological and anatomical features of the mammalian brain. The key functional elements of the brain, neurons, and the connections between them, called synapses, are simulated using biologically derived models. The neuron models include such key functional features as input integration, spike generation and firing rate adaptation, while the simulated synapses reproduce time and voltage dependent dynamics of four major synaptic channel types found in cortex. Furthermore, the synapses are plastic, meaning that the strength of connections between neurons can change according to certain rules, which many neuroscientists believe is crucial to learning and memory formation. ... We were able to deliver a stimulus to the model then watch as it propagated within and between different populations of neurons. We found that this propagation showed a spatiotemporal pattern remarkably similar to what has been observed in experiments with real brains. In other simulations, we also observed oscillations between active and quiet periods, as is often observed in the brain during sleep or quiet waking."
I'm pretty skeptical about this line of work. The thing that's interesting about neural circuitry is not so much large scale population activity patterns but transformation of information (e.g. construction of a receptive field, place cells in hippocampus). As far as I can tell this work captures some of the large scale oscillatory dynamics but says nothing about the fine-grained information processing that is actually where the rubber meets the road.
----
Edit: that said, the tools for large scale computation being developed here could be an interesting foundation for further work -- given the right stimuli and learning rules, can one recapitulate formation of receptive fields? If yes, what would the relationship be between the simulation's implementation of receptive field structure, and the implementations found in biology?
But the media hype is pretty off scale for what is, essentially, preliminary work.