SmartEM: machine-learning guided electron microscopy.

Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis.

X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics.

Comprehensive, synapse-resolution imaging of the brain will be crucial for understanding neuronal computations and function. In connectomics, this has been the sole purview of volume electron microscopy (EM), which entails an excruciatingly difficult process because it requires cutting tissue into many thin, fragile slices that then need to be imaged, aligned, and reconstructed. Unlike EM, hard X-ray imaging is compatible with thick tissues, eliminating the need for thin sectioning, and delivering fast acquisition, intrinsic alignment, and isotropic resolution.

Connectomes across development reveal principles of brain maturation.

An animal's nervous system changes as its body grows from birth to adulthood and its behaviours mature. The form and extent of circuit remodelling across the connectome is unknown. Here we used serial-section electron microscopy to reconstruct the full brain of eight isogenic Caenorhabditis elegans individuals across postnatal stages to investigate how it changes with age.

Sparse sign-consistent Johnson-Lindenstrauss matrices: compression with neuroscience-based constraints.

Johnson-Lindenstrauss (JL) matrices implemented by sparse random synaptic connections are thought to be a prime candidate for how convergent pathways in the brain compress information. However, to date, there is no complete mathematical support for such implementations given the constraints of real neural tissue. The fact that neurons are either excitatory or inhibitory implies that every so implementable JL matrix must be sign consistent (i.

The big data challenges of connectomics.

The structure of the nervous system is extraordinarily complicated because individual neurons are interconnected to hundreds or even thousands of other cells in networks that can extend over large volumes. Mapping such networks at the level of synaptic connections, a field called connectomics, began in the 1970s with a the study of the small nervous system of a worm and has recently garnered general interest thanks to technical and computational advances that automate the collection of electron-microscopy data and offer the possibility of mapping even large mammalian brains. However, modern connectomics produces 'big data', unprecedented quantities of digital information at unprecedented rates, and will require, as with genomics at the time, breakthrough algorithmic and computational solutions.