The human brain is often described as a massively parallel computing machine. That raises an interesting question: just how parallel is it?
Today, we get an answer thanks to the work of Harris Georgiou at the National Kapodistrian University of Athens in Greece, who has counted the number of “CPU cores” at work in the brain as it performs simple tasks in a functional magnetic resonance imaging (fMRI) machine. The answer could help lead to computers that better match the performance of the human brain.
The brain itself consists of around 100 billion neurons that each make up to 10,000 connections with their neighbors. All of this is packed into a structure the size of a party cake and operates at a peak power of only 20 watts, a level of performance that computer scientists observe with unconcealed envy.
fMRI machines reveal this activity by measuring changes in the levels of oxygen in the blood passing through the brain. The thinking is that more active areas use more oxygen so oxygen depletion is a sign of brain activity.
Typically, fMRI machines divide the brain into three-dimensional pixels called voxels, each about five cubic millimeters in size. The complete activity of the brain at any instant can be recorded using a three-dimensional grid of 60 x 60 x 30 voxels. These measurements are repeated every second or so, usually for tasks lasting two or three minutes. The result is a dataset of around 30 million data points.
Georgiou’s work is in determining the number of independent processes at work within this vast data set. “This is not much different than trying to recover the (minimum) number of actual ‘cpu cores’ required to ‘run’ all the active cognitive tasks that are registered in the entire 3-D brain volume,” he says.
This is a difficult task given the size of the dataset. To test his signal processing technique, Georgiou began by creating a synthetic fMRI dataset made up of eight different signals with statistical characteristics similar to those at work in the brain. He then used a standard signal processing technique, called independent component analysis, to work out how many different signals were present, finding that there are indeed eight, as expected.
Next, he applied the same independent component analysis technique to real fMRI data gathered from human subjects performing two simple tasks. The first was a simple visuo-motor task in which a subject watches a screen and then has to perform a simple task depending on what appears.
In this case, the screen displays either a red or green box on the left or right side. If the box is red, the subject must indicate this with their right index finger, and if the box is green, the subject indicates this with their left index finger. This is easier when the red box appears on the right and the green box appears on the left but is more difficult when the positions are swapped. The data consisted of almost 100 trials carried out on nine healthy adults.
The second task was easier. Subjects were shown a series of images that fall into categories such as faces, houses, chairs, and so on. The task was to spot when the same object appears twice, albeit from a different angle or under different lighting conditions. This is a classic visual recognition task.
The results make for interesting reading. Although the analysis is complex, the outcome is simple to state. Georgiou says that independent component analysis reveals that about 50 independent processes are at work in human brains performing the complex visuo-motor tasks of indicating the presence of green and red boxes. However, the brain uses fewer processes when carrying out simple tasks, like visual recognition.
That’s a fascinating result that has important implications for the way computer scientists should design chips intended to mimic human performance. It implies that parallelism in the brain does not occur on the level of individual neurons but on a much higher structural and functional level, and that there are about 50 of these.
Georgiou points out that a typical voxel corresponds to roughly three million neurons, each with several thousand connections with its neighbors. However, the current state-of-the-art neuromorphic chips contain a million artificial neurons each with only 256 connections. What is clear from this work is that the parallelism that Georgiou has measured occurs on a much larger scale than this.
“This means that, in theory, an artificial equivalent of a brain-like cognitive structure may not require a massively parallel architecture at the level of single neurons, but rather a properly designed set of limited processes that run in parallel on a much lower scale,” he concludes.
Anybody thinking of designing brain-like chips might find this a useful tip.
Ref: arxiv.org/abs/1410.7100 Estimating The Intrinsic Dimension In fMRI Space Via Dataset Fractal Analysis
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