The mystery of noisy neurons

According to researchers at the University of Rochester, our brain is a Bayesian computer. They've found that 'noisy' signals used by the brain's cortex are not noisy at all. Instead, "this noise dramatically enhances the brain's processing, enabling us to make decisions in an uncertain world."

According to researchers at the University of Rochester, our brain is a Bayesian computer. They've found that 'noisy' signals used by the brain's cortex are not noisy at all. Instead, 'this noise dramatically enhances the brain's processing, enabling us to make decisions in an uncertain world.' And according to the researchers, these chaotic and noisy signals 'may actually be the brain's way of running at optimal performance.' So is really our brain a Bayesian computer? Not everybody in the world of neuroscience is convinced yet, but read more...

Let's start with the first paragraph of this University of Rochester News article.

Researchers at the University of Rochester may have answered one of neuroscience's most vexing questions -- how can it be that our neurons, which are responsible for our crystal-clear thoughts, seem to fire in utterly random ways?

These researchers include Alex Pouget and his team at the Computational Cognitive Neuroscience Lab.

Pouget's work for the first time connects two of the brain's biggest mysteries; why it's so noisy, and how it can perform such complex calculations. As counter-intuitive as it sounds, the noise seems integral to making those calculations possible.

According to the researchers, we're still thinking that our brain works in a traditional approach coming from the mid-80s.

You see an image and you relate that image to one stored in your head. But the reality of the cranial world seems to be a confusing array of possibilities and probabilities, all of which are somehow, mysteriously, properly calculated.

In order to understand what Pouget and his team are doing, here are some illustrations coming from a paper published in 2005. Below are some examples of neural responses to 'air puff' stimuli delivered through needles to two monkeys. The top part shows a color-coded map of a visual receptive field (RF) displayed on a 'flattened' representation of a face, where red indicates high-frequency cell response and blue an absence of response. On the bottom part you can see "superimposed contour maps of all the tactile RFs recorded in some monkey." (Credit: Alex Pouget's lab)

Neural responses to stimuli delivered to monkeys

Two years ago, Pouget started to think that was previously called 'noise' was instead what he calls now '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.

For more information, the latest research work by Pouget and his colleagues has been published in Nature Neuroscience under the name "Bayesian inference with probabilistic population codes" (Volume 9, Number 11, November 2006). Here are two links to the abstract and to the full paper (PDF format, 7 pages, 352 KB).

For more information, here is a link to another paper published in the same issue of Nature Neuroscience, "Noisy neurons can certainly compute." Finally, here is a link to a previous paper by Pouget and his team, "Reference frames for representing the location of visual and tactile stimuli in the parietal cortex." (PDF format, 9 pages, 661 KB) The above images have been extracted from this paper which was also published by Nature Neuroscience in 2005.

Now, Pouget and his team are looking at our entire cortex because they think 'every part of our highly developed cortex displays a similar underlying Bayes-optimal structure,' regulating vision, movement, reasoning or loving.

Sources: University of Rochester News, November 10, 2006; and various websites

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