|Date and Time:||Jul 17 (Fri.), 2015, 13:00 – 14:30|
|Place:||Room #802, Building E-4, UEC|
|Speaker:||Dr. Tatsuhiro Tajima (Postdoc. Researcher, Lab of Cognitive Computational Neuroscience, University of Geneva, Switzerland)|
|Chair:||Assoc. Prof. Yoichi Miyawaki|
|Title:||Untangling complex brain dynamics|
|Abstract:||The brain is a complex dynamical system. While a variety of innovative large-scale recording technologies are yielding a plethora of biological data at unprecedented spatiotemporal resolution, it may sometimes seem hopeless to derive a reduced, intuitive model from the nonlinear, heterogeneous and high-dimensional dynamics of a large neural population. However, extracting simple structures that govern complex neurophysiological phenomena is still possible by focusing on their deterministic aspects—constraints on the neural signals in the temporal domain. I will present two studies where theoretically-motivated analyses of intrinsic brain dynamics shed light on the underlying neural processing mechanisms.
First, using a nonlinear mapping from neural state to stimulus space, we find flexibly-modulated attractor dynamics during task-switching in monkey visual cortex. This is in contrast to a prevailing view that dynamics outside of sensory cortices solely account for the flexible sensory-action association. The temporal evolution of neural modulation is not explained by static gain-control mechanisms, suggesting an involvement of sensory cortex in recurrent dynamics that underlies the flexible perceptual abilities.
Next, I introduce a more generic method based on dynamical systems theory, which is capable of analyzing large-scale and heterogeneous neural interaction. Applying this method to whole-brain electrocardiography data from behaving monkey reveals a universal relationship between dynamical complexity and area-to-area interaction, which dissociates conscious from unconscious brain states. Remarkably, the method captures state-dependent structures of cross-area interaction that can be missed by conventional correlation-based analysis. These results underscore the importance of intrinsic dynamics in understanding complex neuronal systems.