"We've known for several years that at the behavioral level, we're 'Bayes optimal,' meaning we are excellent at taking various bits of probability information, weighing their relative worth, and coming to a good conclusion quickly," says Pouget. "But we've always been at a loss to explain how our brains are able to conduct such complex Bayesian computations so easily."
Two years ago, while talking with a physics friend, some probabilities in Pouget's own head suddenly resolved.
"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
Source:University of Rochester