Yup, I'd say that's a fair way of expressing it, although I think we take "neural substrate that is structurally similar to the human brain" much more seriously than other people that use phrases like that. It's a similar enough substrate that if fixes a lot of our parameter values for us, leaving us less open to "fiddle with parameters until it works".

We've also tried to make sure to highlight that it can't learn new tasks, so it's not able to work in the fluid domains people do. It also doesn't have any intrinsic motivation to do that switching.

Interestingly, there are starting to be good non-neural theories of human task switching (e.g. [http://act-r.psy.cmu.edu/publications/pubinfo.php?id=831] ). These are exactly the sorts of theories we want to take a close look at and see how they could be realistically implemented in spiking neurons.

Hi, it's Terry again (one of the researchers on the project)

The interesting thing (for me) isn't that it can shift from task to task, but that it can shift from task to task just like the human brain. In other words, we're showing how a realistic neural system can shift between tasks. That's something that's not found in other neural models, where you tend to either have it do one task or you have external (non-neural) systems modify the model for different tasks. We're showing a way of doing that selecting routing and control in an entirely neural way that maps nicely onto the cortex-basal ganglia-thalamus loop.

Oh, and, since we constrain the model with a bunch of physical parameters influencing the timing of the system (reabsorption of neurotransmitter, mostly), we can also look at how long it takes the system to switch tasks, and compare that to human brains. It's these sorts of comparisons that let us use this sort of model to test hypotheses about what different parts of the brain are doing.

Hi, I'm Terry Stewart, one of the researchers on the project.

I like the roadmap, and it seems to be the right way to go if the goal is to emulate a particular person's brain. However, our whole goal is to understand the human brain, so we want to reach for whole-system understanding, which is exactly what the WBE approach doesn't need.

I believe that the approach we are taking is a novel method for understanding the human brain that has a reasonable chance of producing results faster than the pure WBE approach (or, at the very least, the advances in understand provided by our approach may make WBE significantly simpler). Of course, to make that claim, I need to justify why our approach is significantly different from hundreds of other researchers who also are trying to understand the human brain.

The key difference is that we have a neural compiler: a system for taking a mathematical description of the function to be computed and the properties of the neurons involved, and producing a set of connection weights that will cause those neurons to approximate that function. This is a radically different approach to building neural networks, and we're still working out the consequences of this compiler. There's a technical overview of this system here [http://ctnsrv.uwaterloo.ca/cnrglab/node/297] and the system itself is opensource and available at [http://nengo.ca]. This is what let us build Spaun -- we took a bunch of descriptions of the function of different brain areas, converted them into math, and compiled them into neurons.

Right now, we use a very simple neuron model (LIF -- basically the simplest spiking neuron model), but the technique is applicable to any type of neuron we feel like using (and have the computational power to handle). An interesting part of the research is determining what increased functional capacities you get from using more complex neural models.

Indeed, the main thing that makes me think that this is a novel and useful way of understanding the brain is that we get constraints on the types of computations that can be performed. For example, it turns out to be really easy to compute the circular convolution of two 500-dimensional vectors (an operation we need for our approach to symbol-like reasoning), but very hard to get neurons to find which of five numbers is the largest (the max function). These sorts of constraints have cused us to examine very different types of algorithms for reasoning, and we found that certain inductive reasoning problems are surprisingly easy with these sorts of algorithms [http://ctnsrv.uwaterloo.ca/cnrglab/node/16].