How does the brain make perceptual predictions over time?

Prediction is crucial for brain function without forecasting, our actions would always be too late because of the delay in neural processing. However, there has been limited theoretical work explaining how our brains perform perceptual predictions over time.

In the latest issue of Proceedings of the National Academy of Sciences (PNAS), New York University neuroscientist David Heeger offers a new framework to explain how the brain makes predictions. He outlines how "prediction" may be a general principle of cortical function along with the already-established role of inference.

"It has long been recognized that the brain performs a kind of inference, combining sensory information with expectations," explains Heeger, a professor in NYUs Center for Neural Science. "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 formalizes it mathematically."

Largely missing from our understanding of brain function had been models akin to those routinely employed by meteorologists. In making their predictions, forecasters rely on past weather information to project climate conditions over the next several days.

"Similarly, the neural networks in our brains embody a type of model of our surroundings," Heeger observes. "However, we dont have a clear understanding of how they operate to make predictions."

Existing theories of brain function and neural networks used in artificial intelligence use a hierarchical structure: sensory input comes in at one end and progressively more abstract representations are computed along the hierarchy.

But this "feedforward"/pipeline processing architecture, Heeger argues, does not account for the brains predictive capabilities because, unlike weather models, it does not explain how it loops in earlier information a dynamic Heegers theory includes.

"Its possible to run this process the other way around to take an abstract representation at the top of the hierarchy and run it backwards, from top to bottom through the neural net, to generate something like a sensory prediction or expectation," he explains.