Extracellular 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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We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal or dendritic tree, its actions of elongation, branching and turning are described in a stochastic, phenomenological manner. In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, can be generated. Synapses are formed as neurons grow out and axonal and dendritic branches come in close proximity of each other. NETMORPH is a flexible tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Research applications include studying the complex relationship between neuronal morphology and global patterns of synaptic connectivity. Possible future developments of NETMORPH are discussed.

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The topological structure of a binary tree is characterized by a measure called tree asymmetry, defined as the mean value of the asymmetry of its partitions. The statistical properties of this tree-asymmetry measure have been studied using a growth model for binary trees. The tree-asymmetry measure appears to be sensitive for topological differences and the tree-asymmetry expectation for the growth model that we used appears to be almost independent of the size of the trees. These properties and the simple definition make the measure suitable for practical use, for instance for characterizing, comparing and interpreting sets of branching patterns. Examples are given of the analysis of three sets of neuronal branching patterns. It is shown that the variance in tree-asymmetry values for these observed branching patterns corresponds perfectly with the variance predicted by the used growth model.