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How Does The Brain Make Predictions? Neuroscientist Has A Theory

First Posted: Feb 08, 2017 05:54 AM EST
Brain
How does the brain make predictions?
(Photo : Matt Cardy/Getty Images)

Some people are just good at predicting stuff that ends up coming true. While a concrete explanation on the neural process of prediction continues to remain unknown, an NYU neuroscientist comes up with an interesting theory.

The Irish News reported that David Heeger, a neuroscientist at New York University, figured out a new framework to give an explanation to a person's ability to predict future events.

"It has long been recognised that the brain performs a kind of inference, combining sensory information with expectations," he said. "Those expectations can come from the current context, from memory recall, or as an ongoing prediction over time. This new theory puts all of this together and formalises it mathematically."

According to Heeger, the brain is just like what meteorologists use to come up with weather forecasts: observations on past weather conditions and current climate patterns.

"Similarly, the neural networks in our brains embody a type of model of our surroundings," he said. "However, we don't have a clear understanding of how they operate to make predictions."

Heeger added that while existing theories on brain functioning and neural networking used in artificial intelligence follow a hierarchal structure, which he calls a "feedforward or pipeline processing architecture," running the hierarchy backward could possibly generate a forecast.

In the "feedforward" processing architecture, sensory input comes in at one end and more abstract representations (such as memory recall or mental imagery) are progressively computed along the hierarchy. Meanwhile, running things from top to bottom through the neural net may create a sensory prediction or expectation.

According to Science Daily, Heeger explained that this hypothesis may also help AI replicate human decision-making and processing as the "feedforward" processing architecture limits it from including prediction and exploration.

"The theory of neural function that I'm outlining aims to fill in some of the significant dynamics that AI is missing," he said.

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