Most models involving digital reconstructions are constrained and

Most models involving digital reconstructions are constrained and validated by measurements from experiments. For this purpose, the goal of real-scale simulations shifts the

demand to massive experimental data sets, not only to ensure sufficient statistical power for adequate estimation of all model parameters, but also to capture the natural diversity of neuron types (Hill et al., 2012). The amount of necessary experimental data requires fully automated digital tracing. Yet a century after Cajal’s drawings, the majority of publicly available morphological data is still being reconstructed manually (Halavi et al., 2012), because the extensive heuristic expertise of humans has not yet been matched by computer algorithms (Donohue and Ascoli, 2011). As recent developments pull within reach of full automation (e.g., Chiang et al., Epacadostat cell line 2011), the emphasis OTX015 mouse will move to

generalization of high quality results to all routine laboratory preparations. An important lesson taught by the DIADEM Challenge is that success hinges not only on independent advancements in imaging technology and algorithm design, but also on specifically tailoring the experimental details to the computational goal. As large volumes of reconstructions become attainable by high-throughput pipelines, quality control will still require human validation, which will probably become the ultimate bottleneck. In this review, we highlighted from the research designs and digital resources that fuel the thriving scientific progress of neuromorphology reconstruction in so many areas of neuroscience. Applications abound in morphometric and stereological analyses, biophysically realistic simulations of neuronal activity, computational models of developmental growth and migration, and stochastic generation of synaptically connected networks. Real-scale, four-dimensional reconstructions of entire plastic circuits at the single-neuron level promise to make the next decade the most exciting yet. This work was supported in part by grants R01-NS39600 from

the National Institutes of Health and MURI-N00014-10-1-0198 from the Office of Naval Research. We are grateful to Dr. Michele Ferrante for Figure 4C and to Dr. Maryam Halavi for Figure 5A. We thank Mr. Todd Gillette, Dr. Kerry Brown, and Dr. Michele Ferrante for feedback on an earlier version of this manuscript. “
“The Drosophila neuromuscular junction (NMJ) is a powerful system to investigate mechanisms underlying retrograde signaling ( Keshishian and Kim, 2004). Spaced stimulation of Drosophila larval and embryonic NMJs results in potentiation of spontaneous (quantal) release ( Ataman et al., 2008; Yoshihara et al., 2005) through a retrograde signaling mechanism requiring postsynaptic function of the vesicle protein Synaptotagmin 4 (Syt4) ( Barber et al., 2009; Yoshihara et al., 2005).

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