• IEEE Trans Biomed Eng · Nov 2004

    Long-term characterization of firing dynamics of spontaneous bursts in cultured neural networks.

    • Jaap van Pelt, Pieter S Wolters, Michael A Corner, Wim L C Rutten, and Ger J A Ramakers.
    • Graduate School Neurosciences Amsterdam, Netherlands Institute for Brain Research, Neurons and Networks Research Group, 1105 AZ Amsterdam, The Netherlands. j.van.pelt@nih.knaw.nl
    • IEEE Trans Biomed Eng. 2004 Nov 1; 51 (11): 2051-62.

    AbstractExtracellular action potentials were recorded from developing dissociated rat neocortical networks continuously for up to 49 days in vitro using planar multielectrode arrays. Spontaneous neuronal activity emerged toward the end of the first week in vitro and from then on exhibited periods of elevated firing rates, lasting for a few days up to weeks, which were largely uncorrelated among different recording sites. On a time scale of seconds to minutes, network activity typically displayed an ongoing repetition of distinctive firing patterns, including short episodes of synchronous firing at many sites (network bursts). Network bursts were highly variable in their individual spatio-temporal firing patterns but showed a remarkably stable underlying probabilistic structure (obtained by summing consecutive bursts) on a time scale of hours. On still longer time scales, network bursts evolved gradually, with a significant broadening (to about 2 s) in the third week in vitro, followed by a drastic shortening after about one month in vitro. Bursts at this age were characterized by highly synchronized onsets reaching peak firing levels within less than ca. 60 ms. This pattern persisted for the rest of the culture period. Throughout the recording period, active sites showed highly persistent temporal relationships within network bursts. These longitudinal recordings of network firing have, thus, brought to light a reproducible pattern of complex changes in spontaneous firing dynamics of bursts during the development of isolated cortical neurons into synaptically interconnected networks.

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