Simulations

GeNN is a GPU enhanced Neuronal Network simulation environment supporting multiple frontends and backends. Spiking neural network models can be specified directly in Python or C++ or through interfaces to Brian 2, SpineCreator (SpineML), or PyNN.

A whole-cortex macaque structural connectome constructed from a combination of axonal tract-tracing and diffusion-weighted imaging data. Created for modeling brain dynamics using TheVirtualBrain (thevirtualbrain.org) platform. A detailed description and example usage can be found in the paper here: https://www.

Two-photon (2P) microscopy is a cornerstone technique in neuroscience research. However, combining 2P imaging with spectrally arbitrary light stimulation can be challenging due to crosstalk between stimulation light and fluorescence detection.

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.

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.

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.

TrainingSpace is an online hub that aims to make neuroscience educational materials more accessible to the global neuroscience community developed by the Training and Education Committee composed of members from the INCF network, HBP, SfN, FENS, IBRO, IEEE, BD2K, and iNeuro Initiative.