1D-3D hybrid modeling—from multi-compartment models to full resolution models in space and time - Grein, Stephan; Stepniewski, Martin; Reiter, Sebastian; Knodel, Markus M.; Queisser, Gillian (Frontiers in Neuroinformatics, 2014)
Investigation of cellular and network dynamics in the brain by means of modeling & simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling & simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in level of detail to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing spatial aspects of the cells. For single cell or small-world networks, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the 3D morphology of cells and organelles into 3D space and time-dependent simulations. Every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. We present a hybrid simulation approach, that makes use of reduced 1D-models using e.g. the NEURON which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed 3D-morphology of neurons and organelles. To couple 1D- & 3D-simulations, we present a geometry and membrane potential mapping framework, with which graph-based morphologies, e.g. in swc-/hoc-format, are mapped to full surface and volume representations of the neuron; membrane potential data from 1D-simulations are used as boundary conditions for full 3D simulations. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved highly detailed 3D-modeling approaches. The new framework is applied to investigate electrically active neurons and their intracellular spatio-temporal Calcium Dynamics.