The BigPint package can help examine any large multivariate dataset. However, we note that the example datasets and example code in this package consider RNA-sequencing datasets. If you are using this software for RNA-sequencing data, then it can help you confirm that the variability between your treatment groups is larger than that between your replicates and determine how various normalization techniques in popular RNA-sequencing analysis packages (such as edgeR, DESeq2, and limma) affect your dataset.
morphologica is a header-only C++ library which provides simulation support facilities for simulations of dynamical systems.
It helps with:
Configuration: morphologica allows you to easily set up a simulation parameter configuration system, using the JSON reading and writing abilities of morph::Config.
NetPyNE (Networks using Python and NEURON) is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of biophysical neuronal networks using the NEURON simulator.
For more details, installation instructions, documentation, tutorials, forums, videos and more, please visit: www.
Easy whole-brain modeling for computational neuroscientists 👩🏿🔬💻🧠
In its essence, neurolib is a computational framework for simulating coupled neural mass models written in Python. It helps you to easily load structural brain scan data to construct brain networks where each node is a neural mass representing a single brain area.
With YAPiC you can make your own customized filter (also called model or classifier) to enhance a certain structure of your choice with a simple Python based command line interface, installable with pip.
This is the 4th edition of the online, freely available textbook, providing a complete, self-contained introduction to the field of Computational Cognitive Neuroscience, where computer models of the brain are used to understand a wide range of cognitive functions, including perception, attention, motor control, learning, memory, language, and executive function.
Neural network simulation software written in Go and Python, for developing biologically-based but also computationally functional neural models. Features an interactive 3D interface for visualizing networks and data, and has many implemented models of a wide range of cognitive phenomena.
Uncertainpy is a python toolbox for uncertainty quantification and sensitivity analysis tailored towards computational neuroscience.
Uncertainpy is model independent and treats the model as a black box where the model can be left unchanged.
PsychRNN is designed for neuroscientists and psychologists who are interested in RNNs as models of cognitive function in the brain.
Despite growing interest in RNNs as models of brain function, this approach poses relatively high barriers to entry to researchers, due to the technical know-how required for specialized deep learning software (e.