Getting started


To install Nengo, we recommend using pip.

pip install nengo

pip will do its best to install all of Nengo’s requirements when it installs Nengo. However, if anything goes wrong during this process, you can install Nengo’s requirements manually before doing pip install nengo again.

Installing NumPy

Nengo’s only required dependency is NumPy, and we recommend that you install it first. The best way to install NumPy depends on several factors, such as your operating system. Briefly, what we have found to work best on each operating system is:

For more options, see’s installation page. For our recommended options, read on.


If you’re new to Python and just want to get up and running, Anaconda is the best way to get started. Anaconda provides an all-in-one solution that will install Python, NumPy, and other optional Nengo dependencies. It works on all operating systems (Windows, Mac, Linux) and does not require administrator privileges. It includes GUI tools, as well as a robust command line tool, conda, for managing your Python installation.

Package managers

If you are comfortable with the command line, operating systems other than Windows have a package manager that can install Python and NumPy.

  • Mac OS X: Homebrew has excellent Python support. After installing Homebrew, brew install python and pip install numpy.

  • Linux: Linux distributions come with a package manager capable of installing Python and NumPy. In Debian, Ubuntu, and other distributions with apt use: sudo apt-get install python-numpy. In Fedora and others distributions with yum use: sudo yum install python-numpy. For other package managers, try searching the package list for numpy.

From source

If speed is an issue and you know your way around a terminal, installing NumPy from source is flexible and performant. See the detailed instructions here.

Installing other packages

While NumPy is the only hard dependency, some optional Nengo features require other packages. These can be installed either through Anaconda, a package manager, or through Python’s own package manager, pip.

  • Additional decoder solvers and other speedups require SciPy and scikit-learn.

  • Running the test suite requires pytest, Matplotlib, and Jupyter.

  • Building the documentation requires Sphinx, NumPyDoc and nengo_sphinx_theme.

These additional dependencies can be installed through pip when installing Nengo.

pip install nengo[optional]  # Additional solvers and speedups
pip install nengo[docs]  # For building docs
pip install nengo[tests]  # For running the test suite
pip install nengo[all]  # All of the above


Everything in a Nengo model is contained within a nengo.Network. To create a new Network:

import nengo
net = nengo.Network()

Creating Nengo objects

A Nengo object is a part of your model that represents information. When creating a new object, you must place it within a with block in order to inform Nengo which network your object should be placed in.

There are two objects that make up a basic Nengo model. A nengo.Ensemble is a group of neurons that represents information in the form of real valued numbers.

with net:
    my_ensemble = nengo.Ensemble(n_neurons=40, dimensions=1)

In this case, my_ensemble is made up of 40 neurons (by default, Nengo uses leaky integrate-and-fire neurons) and it is representing a one dimensional signal. In other words, this ensemble represents a single number.

In order to provide input to this ensemble (to emulate some signal that exists in nature, for example) we create a nengo.Node.

with net:
    my_node = nengo.Node(output=0.5)

In this case, my_node emits the number 0.5.

In most cases, however, we want more dynamic information. We can make a nengo.Node using a function as output instead of a number.

with net:
    sin_node = nengo.Node(output=np.sin)

This node will represent a sine wave.

Connecting Nengo objects

We can connect nodes to ensembles in order to represent that information in the activity a group of neurons.

with net:
    nengo.Connection(my_node, my_ensemble)

This connects my_node to my_ensemble, meaning that my_ensemble will now represent 0.5 in its population of 40 neurons.

Ensembles can also be connected to other models. When the dimensionality of the objects being connected are different, we can use Python’s slice syntax to route information from one node or ensemble to another. For example:

with net:
    two_d_ensemble = nengo.Ensemble(n_neurons=80, dimensions=2)
    nengo.Connection(sin_node, two_d_ensemble[0])
    nengo.Connection(my_ensemble, two_d_ensemble[1])

This creates a new ensemble that represents two real-valued signals. By connecting sin_node to two_d_ensemble, its first dimension now represents a sine wave. Its second dimensions now represents the same value as my_ensemble.

When creating connections, we can specify a function that will be computed across the connection.

with net:
    square = nengo.Ensemble(n_neurons=40, dimensions=1)
    nengo.Connection(my_ensemble, square, function=np.square)

Functions can be computed over multiple dimensions, as well.

def product(x):
    return x[0] * x[1]

with net:
    product_ensemble = nengo.Ensemble(n_neurons=40, dimensions=1)
    nengo.Connection(two_d_ensemble, product_ensemble, function=product)

Probing Nengo objects

Once you have defined the objects in your model and how they’re connected, you can decide what data you want to collect by probing those objects.

If we wanted to collect data from our 2D Ensemble and the Product of those two dimensions:

with net:
    two_d_probe = nengo.Probe(two_d_ensemble, synapse=0.01)
    product_probe = nengo.Probe(product_ensemble, synapse=0.01)

The argument synapse defines the time constant on a causal low-pass filter, which approximates a simple synapse model. The output of ensembles of spiking neurons can be very noisy, so a filter is recommended.

Running an experiment

Once a model has been constructed and we have probed certain objects, we can run it to collect data.

To run a model, we must first build a simulator based on the model we’ve defined.

sim = nengo.Simulator(net)

We can then run that simulator. For example, to run our model for five seconds:

Once a simulation has been run at least once (it can be run for additional time if desired) the data collected can be accessed for analysis or visualization.


For more details on these objects, see the API documentation.

Next steps