| 日時： | 2015年7月17日（金）13:00-14:30 |
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| 場所： | 電気通信大学 東4号館802会議室 |
| 講師： | 田嶋 達裕氏（ジュネーブ大学 Lab of Cognitive Computational Neuroscience 博士研究員） |
| 司会： | 宮脇陽一 准教授 |
| 題目： | Untangling complex brain dynamics（複雑な脳のダイナミクスを”解きほぐす”） |
| 概要： | 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. |
