A MATLAB® toolbox that given a three-dimensional spine reconstruction computes a set of characteristic morphological measures that unequivocally determine the spine shape.
When building a network simulation, biophysically detailed electrical models (e-models) need to be tested for every morphology that is possibly used in the circuit. E-models can e.g. be obtained using BluePyOpt by data-driven model parameter optimization. Developing e-models can take a lot of time and computing resources. Therefore, these models are not reoptimized for every morphology in the network. Instead we want to test if an existing e-model matches that particular morphology `well enough’. This process is called Cell Model Management (MM). It takes as input a morphology release, a circuit recipe and a set of e-models with some extra information. Next, it finds all possible (morphology, e-model)-combinations (me-combos) based on e-type, m-type, and layer as described by the circuit recipe, and calculates the scores for every combination. Finally, it writes out the resulting accepted me-combos to a database, and produces a report with information on the number of matches – BluePyMM. This software is also part of the Human Brain Project Brain Simulation Platform.
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Arbor is a high-performance library for computational neuroscience simulations with multi-compartment, morphologically-detailed cells, from single cell models to very large networks. Arbor is written from the ground up with many-cpu and gpu architectures in mind, to help neuroscientists effectively use contemporary and future HPC systems to meet their simulation needs. Arbor supports NVIDIA and AMD GPUs as well as explicit vectorization on CPUs from Intel (AVX, AVX2 and AVX512) and ARM (Neon and SVE). When coupled with low memory overheads, this makes Arbor an order of magnitude faster than the most widely-used comparable simulation software. Arbor is open source and openly developed, and we use development practices such as unit testing, continuous integration, and validation.
BioExcel Building Blocks Workflows is a collection of biomolecular workflows to explore the flexibility and dynamics of macromolecules, including signal transduction proteins or molecules related to the Central Nervous System. Molecular dynamics setup for protein and protein-ligand complexes are examples of workflows available as Jupyter Notebooks. The workflows are built using the BioBB software library, developed in the framework of the BioExcel Centre of Excellence. BioBBis a collection of Python wrappers on top of popular biomolecular simulation tools, offering a layer of interoperability between the wrapped tools, which make them compatible and prepared to be directly interconnected to build complex biomolecular workflows.