Building Neural Networks: Assembly Strategies

This lecture focuses on assembling the building blocks of neural networks, including ion channels, morphologies, and connections, to create a functional network. Different strategies for defining and populating the space with neurons are discussed, such as arbitrary volumes, atlas-based volumes, and simplified volumes. The challenges of setting correct parameters for volume geometry and cell positioning are explored, along with the trade-offs between experimental constraints and model reusability. The lecture also covers the use of synthetic cells and the integration of experimental data to optimize parameters. Various assumptions and strategies for dealing with missing data in network modeling are presented, emphasizing the importance of explicit assumptions and model validations.