Our group uses structural and functional neuroimaging, including EEG, fMRI, DTI and fNIRS, to identify the neural constructs underlying cognition and behavior, particularly within the context of rapid decision making.
BRAIN COMPUTER INTERFACES
We are developing brain computer interfaces (BCIs) which opportunistically measure neural correlates of attentional shift, arousal, engagement and recognition that are used to construct labels and/or reinforcement signals for machine learning systems.
At the core of much of our research is machine learning (ML) for predicting, classifying and fusing multiple neural and non-neural data streams to better identify the cortical and subcortical networks underlying rapid decision making.
COMPUTATIONAL NETWORK MODELS
We use computational modeling to link our macroscopic observations/findings from our human neuroimaging work to mesoscopic scale neuronal population activity and circuity.
Columbia University’s Laboratory for Intelligent Imaging and Neural Computing (LIINC) was founded in September 2000 by Paul Sajda. The mission of LIINC is to using principles of reverse “neuro”-engineering to characterize the cortical networks underlying perceptual and cognitive processes, such as rapid decision making, in the human brain. Our laboratory pursues both basic and applied neurosciences research projects.