November 25 – 30 , 2018, Dagstuhl Seminar 18481

High Throughput Connectomics


Moritz Helmstaedter (MPI for Brain Research – Frankfurt am Main, DE)
Jeff Lichtman (Harvard University – Cambridge, US)
Nir Shavit (MIT – Cambridge, US)

For support, please contact

Simone Schilke for administrative matters

Michael Gerke for scientific matters


List of Participants
Shared Documents


Modern connectomics, the mapping of the connectivity of neural tissue at synaptic resolution, produces ‘big data’ that must be analyzed at unprecedented rates, and will require, as with genomics at the time, breakthrough algorithmic and computational solutions. This Dagstuhl Seminar will bring together key researchers in the field in order to understand the problems at hand and provide new approaches towards the design of high throughput systems for mapping the micro-connectivity of the brain.

The massive amounts of storage and computation in connectomics pipelines require expertise not only in computational neurobiology, machine learning, and alignment techniques, but also in parallel computation, distributed systems, and storage systems. Our aim is to bring researchers from all these areas. Our goal will be to both build an understanding of the state of the art in high-throughput connectomics pipelines, and to brainstorm on how to move the field forward so that high throughput connectomics systems become widely available to neurobiology labs around the world.

Concretely, we would like to come out of this seminar with a hierarchical plan for future connectomics systems that solve existing systems’ problems. We will begin the seminar by having workgroups discuss these problems in existing systems and then dedicate the latter part to collectively working out solutions. We will consider three levels:

  1. The system layer: how data is stored, moved around and computed on in a distributed and parallel fashion.
  2. The pipeline layer: how processing progresses from stitching through alignment and reconstruction.
  3. The algorithm layer: the specific machine learning and error detection and correction algorithms used in various pipeline stages to bring the datasets to analyzable connectivity graphs.

Each day will consist of two lecture sessions in which several colleagues will present their work surveying the current state of the art and problems still in need of solutions. Each such session will be followed by a break into several workgroups on topics that were agreed as being contentious or in need of improved solutions. Our plan is to spend the majority of the time in workgroup discussions and a lesser fraction of it in the lecture hall. As is the tradition, there will also be ample time for discussions over beer in the late afternoons and eves.

Our hope is to conclude the seminar with a coherent plan on how the field should proceed in coming years.

  Creative Commons BY 3.0 DE
  Moritz Helmstaedter, Jeff Lichtman, and Nir Shavit


  • Data Structures / Algorithms / Complexity


  • Connectomics
  • Big Data
  • Parallel Computing
  • Distributed Computing
  • Machine Learning

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Books from the participants of the current Seminar 

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