Computational neuroscience:

Computational neuroscience is the study of the brain and its various mechanisms by analytical and numerical methods. This emerged from the idea that understanding the brain was a multidisciplinary endeavor. (Trappenberg, 2010) Computational neuroscience at its heart tries to answer the questions: How does the brain process information? How are memories stored and accessed? How do neurons interact as a network?

Part of computational neuroscience is developing a model, this is a numerical representation of the function of a physical object. The model must be carefully evaluated and should be as simple as possible but retaining all of the desired output. A model might be a single neuron a cell specializing in signal processing or a whole network of neurons connected together by multiple synapses.

When multiple neurons are connected together in a network they are able to process information and they start to show emergent behavior. Emergent behavior is when complex behavior arises from a relatively simple set of rules. An example of this would be birds flocking, the birds behavior relies on a few rules:

1. Avoid crowding your neighbors, but do not get to far away from them.2. Make sure you are heading in the average direction of all of your neighbors.3. Move towards the center of all of your neighbors.4. Move away from danger. (Robertson, 1987)

These four rules are all that govern a behavior as complicated as a flock of birds flying. No single bird is directing the pattern but the complex behavior emerges from the rules. Another example of emergent behavior is the way ants move to find food. Ants leave pheromone trails behind them and return on the trails when they find food, this reinforces the trail and another ant will follow the reinforced trail until the trail is a multi-ant highway.

Illustration 1: Ant behaviorReductionist biology -- examining individual brain parts, neural circuits and molecules -- has brought us a long way, but it alone cannot explain the workings of the human brain, an information processor within our skull that is perhaps unparalleled anywhere in the universe. We must construct as well as reduce and build as well as dissect. To do that, we need a new paradigm that combines both analysis and synthesis. (Markram, 2012) The Blue Brain Project hopes to do just that by creating a simulation of a human brain, which they would be able to use to investigate how trauma effect the brain, computer vision, and image classification problems.

To put it into perspective, the human brain specs are roughly: 4 x 1016 operations per second with a memory of 3.5 exabytes of memory. The fastest super computer, NUDT Tianhe-2 in Guangzhou, China, has a peek speed of 3.3 x 1016 operation per second with a storage of 12.4 petabytes. The brain thoroughly beats the NUDT Tianhe-2 in economy sipping only 20 watts of power to its 24 megawatts. The Blue Brain Project's goal is to reverse engineer the brain. Which is no small task as most of the process will be a black box problem; we know the input and the output but are unaware of the internal mechanisms. The Blue Brain Project has gone though several important mile stones beginning in 2005 when it completed the simulation of a single cell model, in 2008 it simulated a neocortical column of 10 000 cells. This preliminary model of a neocortical column a set of about 10,000 cells that work together equivalent to one in the brain of a 2-week-old rat. (Kruglinski, 2007) In 2011 it simulated 100 neocortical columns, which, according to the Blue Brain Project, is the equivalent of one rat brain. If Moore's law continues to be relevant, and it has for over 30 years, processing power will double every 18 months; how many doubling periods is a rat brain from a human brain? How many doubling periods is a human brain from the sum of human intelligence?

The Spaun system is another attempt to simulate the brain, however, this model is not taking The Blue Brain Project's precise reverse engineering approach it will be using a top down model, opting to simplify neuron and its interconnections. The researchers behind Spaun took all the available information about the brain from various scanning methods and physiological models and merged the information. [S]cientists threw eight tasks at it, some of which resembled IQ test puzzlers, like a complete-the-pattern quiz. Spaun was also asked to reason, memorize and even gamble. As Spaun worked through these jobs, some curiously human quirks emerged. Just like human volunteers, Spaun was better at remembering the first and last number in a series. Also like people, Spaun took longer to count to higher numbers.(Sanders, 2013)

References:

Gewaltig, M., & Cannon, R. (2014). Current Practice in Software Development for Computational Neuroscience and How to Improve It. Plos Computational Biology, 10(1), 1-9. doi:10.1371/journal.pcbi.1003376

Kruglinski, S. (2007). THE BLUE BRAIN PROJECT. Discover, 28(12), 50-52.

Markram, H. (2012). THE HUMAN BRAIN PROJECT. Scientific American, 306(6), 50-55.

Robertson, B. (1987). Flocks, schools, herds and Boids: a behavior model makes individuals move together. Computer Graphics World, (7). 68.

Sanders, L. (2013). Model brain mimics human quirks. Science News, 183(1), 13.

Trappenberg, T. P. (2010). Fundamentals of Computational Neuroscience. Oxford: Oxford University Press.