Visual exploration and generation of connectivity in neural networks - - PowerPoint PPT Presentation

▶
visual exploration and generation of connectivity in
SMART_READER_LITE
LIVE PREVIEW

Visual exploration and generation of connectivity in neural networks - - PowerPoint PPT Presentation

Jan 26, 2023 95 likes •326 views

Visual exploration and generation of connectivity in neural networks bridging the gap between empirical data and theoretical model definition. Patrick Herbers a,b , Sergio E. Galindo c , *Wouter Klijn a , Sandra Diaz-Pier a , Juan Pedro Brito d ,

slide-1
SLIDE 1

Mitglied der Helmholtz-Gemeinschaft

Wouter Klijn, Göttingen, 14 Sept 2017

Patrick Herbersa,b, Sergio E. Galindoc, *Wouter Klijna, Sandra Diaz-Piera, Juan Pedro Britod, Pablo Tohariad, Susana Matac,d, Oscar D. Roblesc,d, Luis Pastorc,d, Juan J. Garcia-Canteroc, Alexander Peysera

aSimulation Lab Neuroscience, Bernstein Facility for Simulation and Database Technology, Institute for Advanced

Simulation, Jülich Aachen Research Alliance Forschungszentrum Jülich

bMRG Structure Of Memory, Ruhr-Universität Bochum, Bochum, Germany cUniversidad Rey Juan Carlos, Madrid, Spain dCenter for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain

Visual exploration and generation

  • f connectivity in neural networks

bridging the gap between empirical data and theoretical model definition.

slide-2
SLIDE 2

Mitglied der Helmholtz-Gemeinschaft

Contents

  • Visual Network Generation and Exploration
  • From Visualization to Simulation
  • Bridging the gap between empirical data and

theoretical model definition

  • Modular Science Workflow
  • Modular Apps via APIs and

functional contracts

slide-3
SLIDE 3

Mitglied der Helmholtz-Gemeinschaft

Visual Network Exploration and Generation

Caspers et al. 2014 Pastor et. al. 2015, Data: BBP Potjans et. al. 2011 Woodman, et al. 2017

NeuroScheme

slide-4
SLIDE 4

Mitglied der Helmholtz-Gemeinschaft

Proof of Concept Workflow

slide-5
SLIDE 5

Mitglied der Helmholtz-Gemeinschaft

NeuroScheme to Simulation Pipeline

slide-6
SLIDE 6

Mitglied der Helmholtz-Gemeinschaft

Live / In-Situ Visualization and Control Pipeline

slide-7
SLIDE 7

Mitglied der Helmholtz-Gemeinschaft

Automated Pipeline with Live Viewer

slide-8
SLIDE 8

Mitglied der Helmholtz-Gemeinschaft

Data/Model Derived Comparison Pipeline

slide-9
SLIDE 9

Mitglied der Helmholtz-Gemeinschaft

Separation in Front- and Back-end

slide-10
SLIDE 10

Mitglied der Helmholtz-Gemeinschaft

Major Computational Domains

slide-11
SLIDE 11

Mitglied der Helmholtz-Gemeinschaft

Modular Science Workflow

slide-12
SLIDE 12

Mitglied der Helmholtz-Gemeinschaft

Detailed Interactions of Two Modules

slide-13
SLIDE 13

Mitglied der Helmholtz-Gemeinschaft

The Major Computational Domains

slide-14
SLIDE 14

Mitglied der Helmholtz-Gemeinschaft

The Major Computational Domains

slide-15
SLIDE 15

Mitglied der Helmholtz-Gemeinschaft

HPC Data Transport Stack

slide-16
SLIDE 16

Mitglied der Helmholtz-Gemeinschaft

HPC Remote Process Control

slide-17
SLIDE 17

Mitglied der Helmholtz-Gemeinschaft

Isolation of Domain Specific Knowledge

slide-18
SLIDE 18

Mitglied der Helmholtz-Gemeinschaft

Integrated GUI Builder

slide-19
SLIDE 19

Mitglied der Helmholtz-Gemeinschaft

Orchestration and HPC Nice to Haves

slide-20
SLIDE 20

Mitglied der Helmholtz-Gemeinschaft

Modular Apps via APIs and functional contracts

slide-21
SLIDE 21

Mitglied der Helmholtz-Gemeinschaft

Connectivity in Modular Science

  • Connectivity is an important part of analysis workflows

that neuroscientist use today.

  • Building a framework that combines, maximizes and

reuses technical and scientific expertise is essential to study connectivity and by extension the brain.

  • A modular approach will improve collaborative work:

The framework diagram doubles as a social diagram.

slide-22
SLIDE 22

Mitglied der Helmholtz-Gemeinschaft

22

Suggestions?

The European Union’s Horizon 2020 Programme under grant no. 720270 (HBP SGA1) The Spanish Ministry of Economy and Competitiveness under grants C080020-09 and TIN2014-57481 JARA-HPC, the Helmholtz Association through the Portfolio Theme SMHB and the CRCNS grant.

Acknowledgements References

  • S. Caspers, et. al., Studying variability in human brain aging in a population-based German cohort—rationale and design of 1000BRAINS, 2014, Frontiers Aging

Neuroscience

  • L. Pastor, et. al.,NeuroScheme: Efficient multiscale representation for the visual exploration of morphological data in the human brain neocortex, 2015, Proc. of

Congreso Español de Informática Gráfica

  • T. Potjans, M. Diesman, The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model, 2014, Cerebral Cortex
  • M. Woodman et. al., Automatically generating HPC-optimized code for simulations using neural mass models, 2017, CNS
  • P. Gleeson, NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail, 2010, PLOS Computational

Biology

  • M. Djurfeldt, The Connection-Set Algebra—A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models, 2015, Neuroinformatics
  • M. Djurfeldt, et. al., Efficient Generation of Connectivity in Neuronal Networks from Simulator-Independent Descriptions, 2014, Frontiers in Neuroinformatics

w.klijn@fz-juelich.de