Max Planck Institute for Dynamics and Self-Organization -- Department for Nonlinear Dynamics and Network Dynamics Group
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Thursday, 07.07.2016 13 c.t.

Using large-scale microelectrode recording technology to understand the emergent properties of the non-human primate cerebral cortex

by Nicholas Dotson
from Montana State University, Bozeman, MT, USA

Contact person: Viola Priesemann


DPZ Deutsches Primatenzentrum, Großer Hörsaal, Kellnerweg 4, 37077 Göttingen


Cognitive processes produce highly complex spatiotemporal activity patterns spanning widespread cortical and subcortical areas. In order to measure these activity patterns (LFPs and spikes), we have developed and run several experiments with large-scale microelectrode recording devices with up to 256 microelectrodes that span an entire cerebral hemisphere. Using 32 channel recording devices implanted in both prefrontal and posterior parietal cortices in animals performing a visual working memory task, we were able to identify a bimodal patterning (near 0° and near 180°) of the relative phase relationships among cortical signals (LFPs 8 – 25 Hz) within the frontoparietal network. These phase relationships were modified in a task-dependent manner, and in some areal combinations, the relative phase angle between pairs of signals would flip by nearly 180°. In a more recently completed set of experiments using 256 channel recording devices and a similar behavioral task, a bimodal patterning of the relative phase relationships was revealed throughout an entire hemisphere (LFPs 8 – 25 Hz), along with task-dependent phase-flips. Collectively, these findings indicate that in-phase and anti-phase relative phase relationships and phase-flips may be a general property of the cortex. While these synchronization properties are novel in the primate cortex, they are commonplace in many model non-linear oscillator systems. In this talk, I will discuss our recent advances in large-scale recording methods and relate these emergent phenomena, specifically the phase-flip transition, to better-understood model systems.

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