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Three-dimensional (3D) structural information on many length scales is of central importance in biological research. Excellent methods exist to obtain structures of molecules at atomic, organelles at electron microscopic, and tissue at light-microscopic resolution. A gap exists, however, when 3D tissue structure needs to be reconstructed over hundreds of(More)
It is becoming increasingly clear that single cortical neurons encode complex and behaviorally relevant signals, but efficient means to study gene functions in small networks and single neurons in vivo are still lacking. Here, we establish a method for genetic manipulation and subsequent phenotypic analysis of individual cortical neurons in vivo. First,(More)
Neurons of the mammalian CNS are thought to originate from progenitors dividing at the apical surface of the neuroepithelium. Here we use mouse embryos expressing GFP from the Tis21 locus, a gene expressed throughout the neural tube in most, if not all, neuron-generating progenitors, to specifically reveal the cell divisions that produce CNS neurons. In(More)
The detection of image motion is fundamental to vision. In many species, unique classes of retinal ganglion cells selectively respond to visual stimuli that move in specific directions. It is not known which retinal cell first performs the neural computations that give rise to directional selectivity in the ganglion cell. A prominent candidate has been an(More)
Glutamatergic inputs clustered over approximately 20-40 microm can elicit local N-methyl-D-aspartate (NMDA) spike/plateau potentials in terminal dendrites of cortical pyramidal neurons, inspiring the notion that a single terminal dendrite can function as a decision-making computational subunit. A typical terminal basal dendrite is approximately 100-200(More)
The proper connectivity between neurons is essential for the implementation of the algorithms used in neural computations, such as the detection of directed motion by the retina. The analysis of neuronal connectivity is possible with electron microscopy, but technological limitations have impeded the acquisition of high-resolution data on a large enough(More)
Electron microscopy is the only currently available technique with a resolution adequate to identify and follow every axon and dendrite in dense neuropil. Reconstructions of large volumes of neural tissue, necessary to reconstruct even local neural circuits, have, however, been inhibited by the daunting task of serially sectioning and reconstructing(More)
Three-dimensional electron-microscopic image stacks with almost isotropic resolution allow, for the first time, to determine the complete connection matrix of parts of the brain. In spite of major advances in staining, correct segmentation of these stacks remains challenging, because very few local mistakes can lead to severe global errors. We propose a(More)
Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by(More)
Recent technological developments have renewed the interest in large-scale neural circuit reconstruction. To resolve the structure of entire circuits, thousands of neurons must be reconstructed and their synapses identified. Reconstruction techniques at the light microscopic level are capable of following sparsely labeled neurites over long distances, but(More)