3DSpineMFE
A MATLAB® toolbox that given a three-dimensional spine reconstruction computes a set of characteristic morphological measures that unequivocally determine the spine shape.
Modelling and simulation
NEAT
NEAT is a python library for the study, simulation and simplification of morphological neuron models. NEAT accepts morphologies in the de facto standard .swc format, and implements high-level tools to interact with and analyze the morphologies.
NEAT also allows for the convenient definition of morphological neuron models. These models can be simulated, through an interface with the NEURON simulator, or can be analyzed with two classical methods:
- The separation of variables method to obtain impedance kernels as a superposition of exponentials and
- Koch's method to compute impedances with linearized ion channels analytically in the frequency domain. Furthermore, NEAT implements the neural evaluation tree framework and an associated C++ simulator, to analyze subunit independence.
Finally, NEAT implements a new and powerful method to simplify morphological neuron models into compartmental models with few compartments. For these models, NEAT also provides a NEURON interface so that they can be simulated directly, and will soon also provide a NEST interface.
Other software
All softwareArbor
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.
Modelling and simulationCellular level simulation
BioExcel Building Blocks
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.
Modelling and simulationMolecular and subcellular simulation