A Spiking Neural Network Builder for Systematic Data-to-Model Workflow.

ABSTRACT: In building biological neural network models, it is crucial to efficiently convert diverse anatomical and physiological data into parameters of neurons and synapses and to systematically estimate unknown parameters in reference to experimental observations. Web-based tools for systematic model building can improve the transparency and reproducibility of computational models and can facilitate collaborative model building, validation, and evolution. Here, we present a framework to support collaborative data-driven development of spiking neural network (SNN) models based on the Entity-Relationship (ER) data description commonly used in large-scale business software development. We organize all data attributes, including species, brain regions, neuron types, projections, neuron models, and re

SUBMITTER: Gutierrez CE 

PROVIDER: S-EPMC9326306 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature