We demonstrate a meaningful prospective power analysis for an (admittedly idealized) illustrative connectome inference task. Modeling neurons as vertices and synapses as edges in a simple random graph model, we optimize the trade-off between the number of (putative) edges identified and the accuracy of the edge identification procedure. We conclude that explicit analysis of the quantity/quality trade-off is imperative for optimal neuroscientific experimental design. In particular, identifying edges faster/more cheaply, but with more error, can yield superior inferential performance.

All centralized nervous systems possess modulatory neurons that arborize broadly across multiple brain regions. Such modulatory systems are critical for proper sensory, motor, and cognitive processing. How single modulatory neurons integrate into circuits within their target destination remains largely unexplored due to difficulties in both labeling individual cells and imaging across distal parts of the CNS. Here, we take advantage of an identified modulatory neuron in Drosophila that arborizes in multiple olfactory neuropils. We demonstrate that this serotonergic neuron has opposing odor responses in its neurites of the antennal lobe and lateral horn, first and second order olfactory neuropils respectively. Specifically, processes of this neuron in the antennal lobe have responses that are inhibitory and odor-independent, while lateral horn responses are excitatory and odor-specific. The results show that widespread modulatory neurons may not function purely as integrate-and-fire cells, but rather their transmitter release is locally regulated based on neuropil. As nearly all vertebrate and invertebrate neurons are subject to synaptic inputs along their dendro-axonic axis, it is likely that our findings generalize across phylogeny and other broadly-projecting modulatory systems.

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes- neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

The last decade has seen a rapid increase in the number of tools to acquire volume electron microscopy (EM) data. Several new scanning EM (SEM) imaging methods have emerged, and classical transmission EM (TEM) methods are being scaled up and automated. Here we summarize the new methods for acquiring large EM volumes, and discuss the tradeoffs in terms of resolution, acquisition speed, and reliability. We then assess each method’s applicability to the problem of reconstructing anatomical connectivity between neurons, considering both the current capabilities and future prospects of the method. Finally, we argue that neuronal ’wiring diagrams’ are likely necessary, but not sufficient, to understand the operation of most neuronal circuits: volume EM imaging will likely find its best application in combination with other methods in neuroscience, such as molecular biology, optogenetics, and physiology.