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Examining Local Network Processing using Multi-contact Laminar Electrode Recording
Published on: September 8, 2011
Stereotyped Spatiotemporal Dynamics of Spontaneous Activity in Visual Cortex Prior to Eye Opening
Luna Kettlewell1,2, Audrey Sederberg1,2,3, Gordon B Smith1,2,3
1Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455.
Abstract:
Over the course of development, functional sensory representations emerge in the visual cortex. Prior to eye opening, modular patterns of spontaneous activity form long-range networks that may serve as a precursor for mature network organization. Although the spatial structure of these networks has been well studied, their temporal features, which may contribute to their continued plasticity and development, remain largely uncharacterized. To address this, we imaged hours of spontaneous network activity in the visual cortex of developing ferrets of both sexes utilizing a fast calcium indicator (GCaMP8m) and wide-field imaging at high temporal resolution (50 Hz). The spatial structure of this activity was highly modular, exhibiting distributed and spatially segregated active domains and long-range correlated networks on the timescale of tens of milliseconds. We found that the majority of events showed a clear dynamic component in which the patterns of active modules shifted over the course of events lasting a few hundred milliseconds. Although only a minority of events were well fit with a linear traveling wave, more complex spatiotemporal patterns occurred in repeated and stereotyped motifs across hours of imaging. Finally, we found that the most frequently occurring single-frame spatial activity patterns were predictive of future activity and separable spatiotemporal trajectories extending over many hundreds of milliseconds. Together, our results demonstrate that spontaneous activity in the early developing cortex exhibits a stereotyped spatiotemporal structure on fast timescales, suggesting a potential role in the maturation and refinement of future functional representations.
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