Human survival and adaptation hinge on the nuanced interplay between stability and flexibility in visual perception. This project is dedicated to probing this intricate balance, with a specific focus on the dynamics of visual perception.
Visual perception entails the continuous processing of information by the brain. To unravel the mechanisms governing stability and flexibility in visual perception, we employ a novel experimental paradigm utilizing complex stereoscopic visual stimuli. These stimuli, characterized by bistable appearances, transition between stable and ambiguous states, providing an opportunity to investigate the neural networks underpinning perceptual stability and flexibility.
The research project integrates advanced electrophysiological recording and stimulating techniques with online decoding through adaptive machine learning algorithms. This integration seeks to unravel the temporal correlation between dynamic changes in visual stimuli and corresponding neuronal activity across cortical columns, with a specific focus on V5/MT.
Building upon existing knowledge, the project utilizes dense electrophysiological recordings in V5/MT, capturing the firing activity of the neuronal population and associated with online decoding of neuronal activity. In addition, we propose a novel paradigm by integrating closed-loop electrical stimulation in V5/MT to modulate real-time perceptual decisions regarding bistable visual stimuli.
The introduction of closed-loop stimulation in real time modulates key neuronal circuits and signaling patterns, establishing a causal link between neuronal activity and perceptual changes. This integrated approach represents a promising avenue for studying the dynamic relationship between neural processes and visual perception.
This approach enables the examination of neural codes and temporal dynamics, aiming to construct a comprehensive model elucidating how neuronal signals encode primate visual perceptual experiences.
The overarching objective of this project is to develop perceptual Brain-Machine Interfaces (BMIs) with promising implications for rehabilitating individuals with various visual perceptual impairments. This innovative approach holds potential for addressing a spectrum of disorders, including eye diseases, Parkinson's, Schizophrenia, and Autism. In summary, named CѺGSTIM, this project endeavors to unravel the functional neuronal networks governing visual perception, exploring their role in stability and flexibility. By leveraging cutting-edge technologies and innovative methodologies, the research aims to make significant strides in both scientific understanding and societal impact, paving the way for future rehabilitative BMI applications.
