NeuroConstruct-based implementation of structured-light stimulated retinal circuitry

Miriam Elbaz, Rachel Buterman, Elishai Ezra Tsur

Research output: Contribution to journalArticlepeer-review


Background: Retinal circuitry provides a fundamental window to neural networks, featuring widely investigated visual phenomena ranging from direction selectivity to fast detection of approaching motion. As the divide between experimental and theoretical visual neuroscience is fading, neuronal modeling has proven to be important for retinal research. In neuronal modeling a delicate balance is maintained between bio-plausibility and model tractability, giving rise to myriad modeling frameworks. One biologically detailed framework for neuro modeling is NeuroConstruct, which facilitates the creation, visualization and analysis of neural networks in 3D. Results: Here, we extended NeuroConstruct to support the generation of structured visual stimuli, to feature different synaptic dynamics, to allow for heterogeneous synapse distribution and to enable rule-based synaptic connectivity between cell populations. We utilized this framework to demonstrate a simulation of a dense plexus of biologically realistic and morphologically detailed starburst amacrine cells. The amacrine cells were connected to a ganglion cell and stimulated with expanding and collapsing rings of light. Conclusions: This framework provides a powerful toolset for the investigation of the yet elusive underlying mechanisms of retinal computations such as direction selectivity. Particularly, we showcased the way NeuroConstruct can be extended to support advanced field-specific neuro-modeling.

Original languageEnglish
Article number28
JournalBMC Neuroscience
Issue number1
StatePublished - 24 Jun 2020

Bibliographical note

Publisher Copyright:
© 2020 The Author(s).


  • Computational neuroscience
  • NeuroML
  • Neuron
  • Neuronal modeling


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