A fellow student in my lab is basing some of his preliminary work on neuron models devised by Eugene Izhikevich, who published a book last year in which he described a system for modeling the diverse spiking behavior of many types of neurons with an elegant set of equations. In a paper co-authored with Gerald Edelman (of Neural Darwinism fame) they implement a model of the cortex and thalamus and their interconnectivity.
Here are some features of the model:
1) Simulates one million multi-compartmental neuron models of 22 basic types
2) Includes approximately half a billion synapses
3) Macroscopic connectivity is based on data derived from diffusion tensor imaging (DTI) of magnetic resonance image (MRI) scans of the thalamus and cortex
4) Microscopic connectivity is based on reconstruction studies of cat visual cortex
5) Synapses are modified through spike-timing dependent plasticity (STDP)
Now, the thalamus is the part of your brain through which almost all sensory input is routed before being sent to the cortex (the exception is olfactory input). This structure is about the size of the end of your thumb, and it is a place through which nearly all of your input from the world flows (visual, auditory, and tactile). But like most brain areas, exactly what it does is not very well understood. For example, we know that it does not function merely as a relay station. There is extensive feedback from the cortex back to the thalamus, creating a thalamocortical loop. Why would the cortex need to send information back to the thalamus if it's just a relay station? Some theorists have proposed that the thalamus is something like an active blackboard, maintaining a constantly updating sketch of the world. Others have proposed that the loop is a way of keeping recent events in a kind of short-term buffer.
Whatever the case, the Izhikevich and Edelman model does not simulate the stream of sensory input from the world. So how does anything happen in the model? Well, initially it is quiescent. The modelers get it going by causing random neurons to spike, which effectively jump-starts ripples of activity throughout the system.
One interesting finding is that the brain state is very sensitive, so much so that the alteration of the spiking activity of a single neuron radically alters the global firing patterns throughout the model within less than half a second. This seems a bit counterintuitive. We might expect that the brain is robust to small changes. After all, neurons can be fairly noisy (that is, they don't always fire reliably), and they also tend to die off. So either their model is overly sensitive to small perturbations (i.e. the butterfly effect) or this really is a reflection of the sensitivity of real neural systems. Either way, it's an interesting result.
One last comment...the authors state that they "started with the thalamocortical system because it is necessary for human consciousness." In discussing the paper with another student, I mused about the ethical ramifications of this kind of simulation. I seriously doubt that a simulation of a million neurons evoked anything like consciousness when randomly jump-started, but then, consciousness is a very poorly-understood phenomenon. I told the other student that I was reminded of Johnny Got His Gun, an anti-war novel in which a soldier is wounded such that he loses all senses but touch, all his limbs, and most of his face. He tries to communicate by banging out Morse code with his head on the hospital bed.
"But this thing doesn't even have a head to try to bang out Morse code," I joked, before I realized just how creepy that sounded. So like I said, I seriously doubt we have to worry about the ethics of simulating consciousness at this point. There are about 100 billion neurons in a human brain and about 20 billion in the cortex, while this model uses one million neurons. But it's something to at least ponder, and definitely something to consider more as models become more and more sophisticated.
E. M. Izhikevich, G. M. Edelman (2008). Large-scale model of mammalian thalamocortical systems Proceedings of the National Academy of Sciences, 105 (9), 3593-3598 DOI: 10.1073/pnas.0712231105