Lars Muckli - 2016 CCN Workshop: Predictive Coding

Center for Cognitive Neuroscience at Dartmouth
2016 Workshop: Predictive Coding

LARS MUCKLI, UNIVERSITY OF GLASGOW
Visual predictions in different layers of visual cortex

Abstract:
Our brain imaging research has contributed to what is now seen as a paradigm shift in cognitive Neuroscience. Many agree that the brain can be conceptualized as a prediction machine; internal models predict future states, which are then compared to the incoming stream of sensory information. This new conceptual framework opens a number of essential empirical questions: How are predictions communicated? How precise are top-down projected predictions? How are prediction-errors signalled upstream and how are they used to update internal models? We have pioneered several empirical approaches, the most recent one utilizing ultra-high field fMRI, to investigate layer specific information content in cortical feedback (Muckli et al., 2015, Curr Biol). We use paradigms in which direct feedforward inputs to retinotopic visual areas are occluded (Muckli & Petro 2013 Curr Opin Neurobiol), including visual illusions (apparent motion, Alink et al. 2010, JNS; Petro & Muckli 2016, PNAS comment), auditory contextual scene stimulation in blindfolded subjects (Vetter et al. 2014 Curr Biol), and variations on our occlusion paradigm (Smith & Muckli 2010, PNAS) to uncover contextual feedback information to superficial layers of primary visual cortex. These paradigms allow us to measure spatial precision of feedback, temporal unfolding of feedback during saccadic eye-movements (Edwards et al., under review, Curr Biol), and other abstract categorical and task-dependent feedback information.
We are extending our framework to reconstruct and visualize cortical feedback – an approach that can be conceptualized as a day-dream reader: i.e. visualizing the internal models during mental imagery. We are planning extensions into long-term temporal predictions and mental time travel. In collaboration with rodent research labs, we are investigating the dendritic contribution to the superficial layers processing. Research on predictive processing affects brain-scale simulations (HBP), and conceptual and philosophical collaborations (Andy Clark, Jacob Hohwy).