How the Brain Thinks

There have been tremendous advancements in neuroscience the last twenty years, with huge improvements in the tools used to monitor how the brain functions as well as better theories as to how it works. Specifically, it is becoming more and more apparent that our brains don't operate like computers but are in fact complex networks that are precisely tuned to quickly percieve stimuli and calculate probable actions based upon those stimuli. In a recent article, appearing in Nature Neuroscience, researchers from the University at Rochester show that what was percieved as chaotic noise is in fact a precise way to enhance brain function through complex systems.

Here is some of the article below and here is the link:

"One day I had a drink with some machine-learning researchers, and we suddenly said, 'Oh, it's not noise,' because noise implies something's wrong," says Pouget. "We started to realize then that what looked like noise may actually be the brain's way of running at optimal performance."

Bayesian computing can be done most efficiently when data is formatted in what's called "Poisson distribution."

And the neural noise, Pouget noticed, looked suspiciously like this optimal distribution.

This idea set Pouget and his team into investigating whether our neurons' noise really fits this Poisson distribution, and in his current Nature Neuroscience paper he found that it fit extremely well.

"The cortex appears wired at its foundation to run Bayesian computations as efficiently as can be possible," says Pouget. His paper says the uncertainty of the real world is represented by this noise, and the noise itself is in a format that reduces the resources needed to compute it. Anyone familiar with log tables and slide rules knows that while multiplying large numbers is difficult, adding them with log tables is relatively undemanding.

The brain is apparently designed in a similar manner--"coding" the possibilities it encounters into a format that makes it tremendously easier to compute an answer.

Pouget now prefers to call the noise "variability." Our neurons are responding to the light, sounds, and other sensory information from the world around us. But if we want to do something, such as jump over a stream, we need to extract data that is not inherently part of that information. We need to process all the variables we see, including how wide the stream appears, what the consequences of falling in might be, and how far we know we can jump. Each neuron responds to a particular variable and the brain will decide on a conclusion about the whole set of variables using Bayesian inference.

As you reach your decision, you'd have a lot of trouble articulating most of the variables your brain just processed for you. Similarly, intuition may be less a burst of insight than a rough consensus among your neurons.

Pouget and his team are now expanding their findings across the entire cortex, because every part of our highly developed cortex displays a similar underlying Bayes-optimal structure.

"If the structure is the same, that means there must be something fundamentally similar among vision, movement, reasoning, loving--anything that takes place in the human cortex," says Pouget. "The way you learn language must be essentially the same as the way a doctor reasons out a diagnosis, and right now our lab is pushing hard to find out exactly how that noise makes all these different aspects of being human possible."

Pouget's work still has its skeptics, but this, his fourth paper in Nature Neuroscience on the topic, is starting to win converts.

"If you ask me, this is the coming revolution," says Pouget. "It hit machine learning and cognitive science, and I think it's just hitting neuroscience. In 10 or 20 years, I think the way everybody thinks about the brain is going to be in these terms."