A shadow can briefly resemble a face, or one object can momentarily be mistaken for another. New research suggests that the brain may resolve these visual conflicts by allowing different regions to compare their interpretations until a more consistent picture emerges. In a study published in Nature Neuroscience, Cold Spring Harbor Laboratory Cynthia R.
Stebbins Fellow Mitra Javadzadeh and collaborators at the University of Cambridge and University College London examined how two neighboring regions of the visual cortex interact. They found that when activity in the two regions matched, the shared pattern persisted. When the regions produced conflicting patterns, however, the disagreement faded within a fraction of a second.
The findings offer a possible explanation for how the brain combines information from specialized regions into a single, coherent perception. How Specialized Brain Regions Work Together Different parts of the brain are responsible for processing different streams of sensory information, yet our experience of the world usually feels unified rather than fragmented. Understanding how those specialized systems coordinate with one another is a major challenge in neuroscience.
"We are trying to understand how you can have such a high level of specialization between these different blocks, yet always have a consistent holistic outcome," Javadzadeh says. The researchers focused on two well known visual processing regions in the brain's neocortex: the primary visual cortex (V1) and the lateromedial visual area (LM). Visual processing does not simply move in one direction from one region to the next.
Instead, these areas continually exchange information with each other. Testing What Happens When Visual Areas Disagree To explore that communication, Javadzadeh and her colleagues trained mice to tell the difference between two visual patterns tilted in opposite directions. The animals received a reward for recognizing only one of the orientations.
During the task, the researchers temporarily silenced either V1 or LM and watched how the remaining region behaved without its usual partner. The team then used those observations to create an artificial neural network model of the V1-LM circuit. With that model, they could test how the system might respond when particular neurons were altered.
Extract — continue reading at the source.
