What I have always wondered about this project is how they expect to deal with the huge number of parameters associated with the model. Each neuron has several parameters that control its response, and the neurons can be connected in a huge number of ways, even if restricted to small-world networks.
This is related to the bias-variance dilemma in machine learning; the larger the number of parameters the larger the variance in the models that fit the behavior. A nice recent article on the limitations of reverse engineering and the brain is http://frontiersin.org/computationalneuroscience/paper/10.33...
This is related to the bias-variance dilemma in machine learning; the larger the number of parameters the larger the variance in the models that fit the behavior. A nice recent article on the limitations of reverse engineering and the brain is http://frontiersin.org/computationalneuroscience/paper/10.33...