Abstract
BackgroundWe present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender.ResultsOur pipeline is applied to create 55 exemplar neurons representing the various morphological types that are reconstructed from the somatsensory cortex of a juvenile rat. The pipeline is then used to reconstruct a volumetric slice of a cortical circuit model that contains ∼210,000 neurons. The applicability of our pipeline to create highly realistic volumetric models of neocortical circuits is demonstrated with an in silico imaging experiment that simulates tissue visualization with brightfield microscopy. The results were evaluated with a group of domain experts to address their demands and also to extend the workflow based on their feedback.ConclusionA systematic workflow is presented to create large scale synthetic tissue models of the neocortical circuitry. This workflow is fundamental to enlarge the scale of in silico neuroscientific optical experiments from several tens of cubic micrometers to a few cubic millimeters.AMS Subject ClassificationModelling and Simulation
Highlights
We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons
We present a systematic approach for building realistic large scale volumetric models of the neocortical circuity from the morphological representations of the neurons; in which the model can account for light interaction with the different structures of the tissue
A single neuronal morphology is meshed in the order of hundreds of milliseconds to a few seconds
Summary
We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. During the end of the last century, the neuroscience community has witnessed the birth of a revolutionary paradigm of scientific research: ‘in silico neuroscience’ This simulation-based approach has been established based on several aspects, fundamentally: the collection of sparse, yet comprehensive, experimental data to synthesize and build structural models of the brain in addition. Understanding the complex functional and structural aspects of the mammalian brain relying solely on ‘wet’ lab experiments has been proven to be extremely limiting and time consuming. This is due to the fragmentation of the neuroscience knowledge; there are multiple brain regions, different types of animals models, distinct research scopes, and various approaches for addressing the same questions [6]. The search space for unknown data is so broad, that it is debatable whether traditional experiments can provide enough data to answer all the questions in a reasonable time, unless a more systematic way is followed
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