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lectrons, fluorophores, and nucleotides: bridging the gaps in high-throughput connectomics

Monday, 27 February 2017

Magpie B


Organizers: Alex Vaughan, Albert Cardona


In recent years, a variety of techniques have enabled connectomic insights at unprecedented scale and detail. Technological breakthroughs have enabled massive new datasets arising from diverse modalities – high-throughput electron microscopy, large-scale virus injections, Brainbow, and in situ sequencing – that share similar goals of reconstructing neural circuit anatomy at the micro- to macro- scale. This workshop aims order to help cross-pollinate this diverse field. We hope to find common ground in analytical tools, technical tricks, methods, and in particular, suitable neural circuits for validating and comparing each inferred structure despite the large diversity in technique. In addition, we hope to spark links between connectomics and broader questions in behavioral and physiological approaches that have historically driven systems neuroscience.


Morning session

8.00a Mitya Chklovskii, Theory for relating connectomes to function

8.30a Marta Costa, Using NBLAST to bridge light level and EM connectomics data

9.00a Justus Kebschul, Exploiting high-throughput sequencing for projection mapping and connectomics

9.30a Alex Vaughan, The perils and promises (and methods) of sequence-based connectomics

10.00a Marco Tripodi, Life-long genetic access to neural circuits using Self-inactivating Rabies

10.30a Discussion


Afternoon session

4.30p Albert Cardona, Identified neurons as the key to bind RNAseq, ephys, LSM, EM and behavior

5.00p Dagmar Kainmueller, Leveraging shape knowledge for automated analysis of fly brain imagery

5.30p Alexi Koulakov, Barcoding the brain: From network cloning to network alignment

6.00p Anna Kreshuk, Automated circuit reconstruction from 3D EM data

6.30p Uygar Sümbül, Tools for color coded connectomics

7.00p Adam Marblestone, Towards a 'best of all worlds' connectomics: Integrating expansion microscopy 3D morphology, in-situ barcoding and machine learning

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