Tag Archives: Ethan Kerzner

Congratulations Dr. Kerzner

Congratulations to Dr. Ethan Kerzner who successfully defended his PhD dissertation in the Viz Design Lab at the University of Utah’s Scientific Computing Institute.

Ethan’s work has been instrumental in helping us to understanding complex gap junctional networks in our retinal connectomics initiatives.  His Graffinity software package allowed us to explore multivariate graphs, and pull out complex relationships of neurons and gap junctions that would not have been easily possible with other approaches.

Ethan is now off to Google X, and we wish him the very best and look forward to many more interactions in the future.

 

Graffinity: Visualizing Connectivity in Large Graphs

We have a new publication out, (direct link)(Wiley link), Graffinity: Visualizing Connectivity in Large Graphs.  Authors are, Ethan Kerzner (@EthanKerzner), Alexander Lex, Crystal Sigulinsky, Timothy Urness, Bryan W. Jones,  Robert Marc, and Miriah Meyer.

Abstract:  Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node-link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query-based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand. We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open-source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open-source tool with illustrative examples using flight and connectomics data.

The Rod-Cone Crossover Connectome of Mammalian Bipolar Cells

We have a new publication out (direct link), The rod-cone crossover connectome of mammalian bipolar cells authored by Scott Lauritzen, Crystal Sigulinsky, James Anderson, Michael Kalloniatis, Noah Nelson, Danny Emrich, Chris Rapp, Nicolas McCarthy, Ethan Kerzner, Mariah Meyer, Bryan W. Jones, and Robert Marc.

Abstract: The basis of cross-suppression between rod and cone channels has long been an enigma. Using rabbit retinal connectome RC1, we show that all cone bipolar cell (BC) classes inhibit rod BCs via amacrine cell (AC) motifs (C1-6); that all cone BC classes are themselves inhibited by AC motifs (R1-5, R25) driven by rod BCs. A sparse symmetric AC motif (CR) is presynaptic and postsynaptic to both rod and cone BCs. ON cone BCs of all classes drive inhibition of rod BCs via motif C1 wide-field GABAergic ACs (γACs) and motif C2 narrow field glycinergic ON ACs (GACs). Each rod BC receives ≈ 10 crossover AC synapses and each ON cone BC can target ≈ 10 or more rod BCs via separate AC processes. OFF cone BCs mediate monosynaptic inhibition of rod BCs via motif C3 driven by OFF γACs and GACs and disynaptic inhibition via motifs C4 and C5 driven by OFF wide-field γACs and narrow-field GACs, respectively. Motifs C4 and C5 form halos of 60-100 inhibitory synapses on proximal dendrites of AI γACs. Rod BCs inhibit surrounding arrays of cone BCs through AII GAC networks that access ON and OFF cone BC patches via motifs R1, R2, R4 R5 and a unique ON AC motif R3 that collects rod BC inputs and targets ON cone BCs. Crossover synapses for motifs C1, C4, C5 and R3 are 3-4x larger than typical feedback synapses, which may be a signature for synaptic winner-take-all switches.