Anil Ananthaswamy in Wired:
HOW OUR BRAIN, a three-pound mass of tissue encased within a bony skull, creates perceptions from sensations is a long-standing mystery. Abundant evidence and decades of sustained research suggest that the brain cannot simply be assembling sensory information, as though it were putting together a jigsaw puzzle, to perceive its surroundings. This is borne out by the fact that the brain can construct a scene based on the light entering our eyes, even when the incoming information is noisy and ambiguous.
Consequently, many neuroscientists are pivoting to a view of the brain as a “prediction machine.” Through predictive processing, the brain uses its prior knowledge of the world to make inferences or generate hypotheses about the causes of incoming sensory information. Those hypotheses—and not the sensory inputs themselves—give rise to perceptions in our mind’s eye. The more ambiguous the input, the greater the reliance on prior knowledge. “The beauty of the predictive processing framework [is] that it has a really large—sometimes critics might say too large—capacity to explain a lot of different phenomena in many different systems,” said Floris de Lange, a neuroscientist at the Predictive Brain Lab of Radboud University in the Netherlands. However, the growing neuroscientific evidence for this idea has been mainly circumstantial and is open to alternative explanations. “If you look into cognitive neuroscience and neuro-imaging in humans, [there’s] a lot of evidence—but super-implicit, indirect evidence,” said Tim Kietzmann of Radboud University, whose research lies in the interdisciplinary area of machine learning and neuroscience.
So researchers are turning to computational models to understand and test the idea of the predictive brain. Computational neuroscientists have built artificial neural networks, with designs inspired by the behavior of biological neurons, that learn to make predictions about incoming information. These models show some uncanny abilities that seem to mimic those of real brains. Some experiments with these models even hint that brains had to evolve as prediction machines to satisfy energy constraints.