A Python library for creating and simulating large-scale brain models
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jgosmann and tbekolay Add sample_every argument to trange.
This creates matching tranges for probes with a sample_every
parameter that is not a multiple of the simulator timestep.

Note that this essentially replaces the old dt argument.
For backwards compatibility reasons, it still exists
as a keyword argument, but will warn and use the logic
of sample_every.

Fixes #1368.
Latest commit ddb4a19 Nov 16, 2017

README.rst

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Nengo: Large-scale brain modelling in Python

An illustration of the three principles of the NEF

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. Yet, Nengo is highly extensible and flexible. You can define your own neuron types and learning rules, get input directly from hardware, build and run deep neural networks, drive robots, and even simulate your model on a completely different neural simulator or neuromorphic hardware.

Installation

Nengo depends on NumPy, and we recommend that you install NumPy before installing Nengo. If you're not sure how to do this, we recommend using Anaconda.

To install Nengo:

pip install nengo

If you have difficulty installing Nengo or NumPy, please read the more detailed Nengo installation instructions first.

If you'd like to install Nengo from source, please read the developer installation instructions.

Nengo is tested to work on Python 2.7 and 3.4+.

Examples

Here are six of many examples showing how Nengo enables the creation and simulation of large-scale neural models in few lines of code.

  1. 100 LIF neurons representing a sine wave
  2. Computing the square across a neural connection
  3. Controlled oscillatory dynamics with a recurrent connection
  4. Learning a communication channel with the PES rule
  5. Simple question answering with the Semantic Pointer Architecture
  6. A summary of the principles underlying all of these examples

Documentation

Usage and API documentation can be found at https://www.nengo.ai/nengo/.

To build the documentation yourself, run the following command:

python setup.py build_sphinx

This requires Pandoc to be installed, as well as some additional Python packages. For more details, see the Developer Guide.

Development

Information for current or prospective developers can be found at https://www.nengo.ai/developers.html.

Getting Help

Questions relating to Nengo, whether it's use or it's development, should be asked on the Nengo forum at https://forum.nengo.ai.