معرفی
Bijan Pesaran is a Global Distinguished Professor at New York University's Center for Neural Science, where he leads an active research laboratory focused on understanding large-scale brain circuits and developing brain-based therapies. His research investigates the dynamics of neuronal activity to understand how brain areas interact during decision-making processes.
Pesaran's research interests focus on neuronal dynamics and decision-making, with particular emphasis on how distributed brain networks coordinate movements such as saccadic eye movements and reaches. His work examines how specific patterns of neural activity can predict our choices before movements are executed. He employs a combination of experimental and engineering approaches, including multiple-area neural recordings and stimulation, to probe the network mechanisms of decision-making, coordination, and other cognitive functions. His research has significant implications for developing brain-machine interfaces that can translate thought into action, with potential applications for treating neurological disorders.
Pesaran's laboratory has produced influential research on local field potential (LFP) activity and its relation to spiking and behavior, finding that oscillations in LFP predict movement direction and are coherent with single cell spiking. His work on correlations between neural activity in different brain areas has revealed how these correlations reflect whether a subject is making choices or following instructions, potentially reflecting communication between decision-making areas. His lab continues to expand tools for multi-area recordings and stimulation to understand how different brain systems work together to guide behavior.
Pesaran has mentored numerous postdoctoral fellows and students who have gone on to academic and research positions. His laboratory has received funding for research on brain-machine interfaces, neural prosthetics, and understanding the neural basis of cognitive processes. The lab utilizes advanced technologies including virtual reality, motion capture systems, and neural recording systems to investigate large-scale brain networks.
