New Discovery on How the Brain Filters Visual Information

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The human brain has an amazing way of filtering sensory input. For example, when you look at Mount Rushmore National Memorial from far away, you see the faces of George Washington, Thomas Jefferson, Theodore Roosevelt and Abraham Lincoln. As you approach it, you begin to notice the individual grains in the granite, as the presidential faces dim in the backdrop. How does the brain achieve this? In a recent study led by The Salk Institute published in Neuron on December 31, 2018, researchers made a surprising discovery that counters the commonly-held view among neuroscientists on how the brain’s neurons filters what it sees.

The research team of Ambarish S. Pawar, Sergei Gepshtein, and Thomas D. Albright of La Jolla, California-based Salk Institute, together with Sergey Savel’ev of Loughborough University in the UK, discovered that “the previously common description of individual neurons as filters was incorrect.”

For the past few decades, scientists believed that neurons perceived either fine or rough details, and that every neuron performs its own filtering. Instead, the scientists uncovered that the “preference of neurons may shift due to a change in the balance of positive (excitatory) signals and negative (inhibitory) signals by which neurons communicate in the network.” In other words, neuronal selectivity by cortical neurons is flexible and dynamic, rather than stable.

The team measured the spatial frequency selectivity of neurons located in the visual cortex of alert monkeys by showing optical patterns with varying contrast between dark and light regions. They discovered that depending on pattern contrast, individual neurons could filter details regardless of whether it was fine or rough.

Based on the discovery, the team concluded that scientists need to “rethink what the computing units of the brain are” and that “this same flexible quality of neural networks is likely to hold true for other parts of the brain.” In the future, the researchers plan to study how changes in adaptable neuronal networks affect behavior to achieve a better understanding of the neural mechanisms of the brain.

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