Real-time Network Interactions
Underlying Complex Behavior
– Valentin Dragoi, PhD
The Dragoi lab seeks to understand how the brain generates patterns of activity underlying perception, behavior, and cognition, and adapts under pathological conditions, by examining distributed neuronal networks. They recently discovered how neuronal populations undergo rapid plasticity at the same time scale as that of visual information acquisition. The human brain accomplishes this task hundreds of thousands of times daily, yet the underlying cellular and network mechanisms are still poorly understood. The Dragoi lab has published landmark studies demonstrating how certain areas of the human brain process and utilize environmental information. For instance, color, a key natural stimulus, is continuously processed by the human brain, influencing perception and behavior. Specific neuronal networks in V4 visual cortex encode color information. In addition, they found that neurons untuned to certain natural stimuli still play a significant role in the adaptive plasticity and coding of sensory inputs, despite usually being ignored in most studies to date. Moreover, following cross-feature adaptation, the remaining neurons that were tuned to either color or orientation stimuli, significantly improved their stimulus tuning. These findings demonstrate that mechanisms previously believed to operate under particular stimulus conditions, such as on a single feature, can operate at a much larger scale than previously assumed. Therefore, the study of the constant state of adaptation that human brain networks demonstrate, will lead to better understanding of normal physiological brain function, as well as of its change in neurological disorders and how to design better treatments.
Developing Neural Interfaces
That Can Decipher Brain Activities
– Taiyun Chi, PhD
The development of neural interfaces that can record and stimulate neural activity across many neurons and across all relevant time scales is a major technological need. Chi and his lab are developing new methodologies and hardware interfaces for neural recording and stimulation. One of Chi’s areas of focus includes brain-machine interfaces built on large-scale neural recording that can decipher brain activities; the decoded information can then be used to control neural prosthetics to restore lost sensory or motor functions for paralyzed patients.
Optical Sensing for Neuroscience Applications
– Ashok Veeraraghavan, PhD
Optical sensing plays an integral part in the study of neuroscience by providing both structural information about the brain as well as functional information about the neural activity. Innovations in computational imaging have driven an increase in the number of tools that can help understand the mechanisms of neural activity and communication. Veeraraghavan and his team build on this toolbox by adapting computational methods to improve signal recovery, capture information faster and miniaturize microscopy systems toward implantable, long-term subcranial imaging. These newly developed methods can help acquire useful information for unlocking complex brain functions, advance cognitive assistive technology, and better understand neurodegenerative diseases.