Appearing in this week's edition of Cell is a paper summarizing the current status of Henry Markram's Blue Brain Project. You can download the paper for free until Oct 22 here. The paper reports on a morphological and electrophysiological statistically accurate reconstruction of a rat somatosensory cortex. I think it is a pretty impressive piece of work. They first did a survey of cortex (14 thousand recorded and labeled neurons) to get probability distributions for various types of neurons and their connectivities. The neurons are classified according to their morphology (55 m-types), electrophysiology (11 e-types), and synaptic dynamics (6 s-types). The neurons are connected according to an algorithm outlined in a companion paper in Frontiers in Computational Neuroscience that reproduces the measured connectivity distribution. They then created a massive computer simulation of the reconstructed circuit and show that it has interesting dynamics and can reproduce some experimentally observed behaviour.
Although much of the computational neuroscience community has not really rallied behind Markram's mission, I'm actually more sanguine about it now. Whether the next project to do the same for the human brain is worth a billion dollars, especially if this is a zero sum game, is another question. However, it is definitely a worthwhile pursuit to systematically catalogue and assess what we know now. Just like how IBM's Watson did not really invent any new algorithms per se, it clearly changed how we perceive machine learning by showing what can be done if enough resources are put into it. One particularly nice thing the project has done is to provide a complete set of calibrated models for all types of cortical neurons. I will certainly be going to their data base to get the equations for spiking neurons in all of my future models. I think one criticism they will face is that their model basically produced what they put in but to me that is a feature not a bug. A true complete description of the brain would be a joint probability distribution for everything in the brain. This is impossible to compute in the near future no matter what scale you choose to coarse grain over. No one really believes that we need all this information and thus the place to start is to assume that the distribution completely factorizes into a product of independent distributions. We should at least see if this is sufficient and this work is a step in that direction.
However, the one glaring omission in the current rendition of this project is an attempt to incorporate genetic and developmental information. A major constraint in how much information is needed to characterize the brain is how much is contained in the genome. How much of what determines a neuron type and its location is genetically coded, determined by external inputs, or is just random? When you see great diversity in something there are two possible answers: 1) the details matter a lot or 2) details do not matter at all. I would want to know the answer to this question first before I tried to reproduce the brain.
