We wrote a tiny neural network library that meets the demands of this educational visualization. For real-world applications, consider the TensorFlow library.

You can use NEST either with the interpreted programming language Python (PyNEST) or as a stand alone application (nest). PyNEST provides a set of commands to the Python interpreter which give you access to NEST's simulation kernel. With these commands, you describe and run your network simulation.

Note that distance can be defined different ways, such as Manhattan (sum of all features, or inputs), Euclidean (geometric distance), p-norm distance...typically Euclidean is used

Nengo is a graphical and scripting based Python package for simulating large-scale neural networks.

Brian is a free, open source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms. We believe that a simulator should not only save the time of processors, but also the time of scientists.

NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons.

Nengo is a Python library for building and simulating large-scale neural models. Nengo can create sophisticated spiking and non-spiking neural simulations with sensible defaults in a few lines of code.

PyNN (pronounced 'pine') is a simulator-independent language for building neuronal network models. In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST and Brian 2) and on a number of ...

Nengo, also known as “Brain Maker”, is a versatile open-source Python package designed for building, testing, and applying neural networks, and it supports many backends for spiking neural networks (SNNs).

To simulate neural networks, NEST can be used interactively from the Python prompt. NEST allows users to create neuron models and devices, connect neurons to devices, specify synapse properties and simulate the network for a given number of milliseconds.