Building a consensis

 1 / 1CSHL Cynthia R. Stebbins Fellow Mitra Javadzadeh and collaborators have developed circuit models of the primary visual cortex (V1) and lateromedial visual area (LM) in the brain's neocortex. These models allowed the team to map and predict neural activity in response to different visual stimuli.
Credit: Javadzadeh lab/CSHL

Beauty is in the eye of the beholder but in terms of how we see, it's complicated.

To wit.

Javadzadeh and her collaborators measured the activity of a combined 194 V1 neurons and 228 LM neurons across seven mice to build accurate circuit models of each brain region.
Credit: Javadzadeh lab/CSHL

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 studied two neighboring regions of the visual cortex. When the regions agreed, their shared activity lasted longer. But when they disagreed, the mismatch quickly faded within a fraction of a second. This shift offers a glimpse into how the brain forms a consensus for reaching conclusions.

Brain regions have specialized blocks that receive distinct streams of information from sensory inputs, yet the brain still operates as a unified whole. "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.

Researchers have long identified two visual processing areas in the brain's neocortex: the primary visual cortex (V1) and lateromedial visual area (LM). Whenever the brain sees and comprehends something, it is not a one-way street. These two systems remain in two-way contact with each other.

It always starts with one

Javadzadeh and her collaborators trained mice to distinguish between two visual patterns tilted at opposite angles, offering a reward for only one orientation. Next, they briefly silenced one of the visual processing areas—V1 or LM—in the middle of a task. They then observed how one region functioned when its partner went quiet.

Using these recorded observations, they built an artificial neural network model representing the V1-LM circuit. They were then able to simulate how the circuit would respond when specific neurons were manipulated.

The team discovered that mismatched activity patterns between the two regions quickly dissipated, whereas patterns in which both areas agreed were sustained. "We find that over time, these types of connections between areas implement a mechanism we call consensus building," Javadzadeh explains.

Click here for the paper.