nemos (NEural MOdelS) is a statistical modeling framework optimized for systems neuroscience and powered by JAX. It streamlines the process of creating and selecting models, through a collection of easy-to-use methods for feature design.

The core of nemos includes GPU-accelerated, well-tested implementations of standard statistical models, currently focusing on the Generalized Linear Model (GLM).

The package is under active development and more methods will be added in the future.

For those looking to get a better grasp of the Generalized Linear Model, we recommend checking out the Neuromatch Academy's lesson here and Jonathan Pillow's tutorial from Cosyne 2018 here.

The glm.GLM object from nemos predict spiking from a single neuron in response to user-specified predictors. The predictors X must be a 2d array with shape (n_timebins, n_features), and y (or spike count) must be a 1d array with shape (n_timebins, ).

nemos streamlines the design of a GLM with multiples features. Implementing the model in the figure above can be done in a few lines of code :

import nemos as nmo

# Instantiate the basis
basis_1 = nmo.basis.MSplineBasis(n_basis_funcs=5)
basis_2 = nmo.basis.CyclicBSplineBasis(n_basis_funcs=6)
basis_3 = nmo.basis.MSplineBasis(n_basis_funcs=7)

basis = basis_1 * basis_2 + basis_3

# Generate the design matrix
X = basis.compute_features(feature_1, feature_2, feature_3)

# Fit the model
nmo.glm.GLM().fit(X, y)

Building the model above is more complicated. In this case, input spikes are convolved with a bank of basis functions that stretch in time. With nemos, you can convolve the basis functions with a few lines of code:

import nemos as nmo

# generate 5 basis functions of 100 time-bin, 
# and convolve the counts with the basis.
X = nmo.basis.RaisedCosineBasisLog(5, mode="conv", window_size=100
    ).compute_features(spike_counts)

# fit a GLM to the first neuron spike counts
glm = nmo.glm.GLM().fit(X, spike_counts[:, 0])

# compute the rate
firing_rate = glm.predict(X)

# compute log-likelihood
ll = glm.score(X, spike_counts[:, 0])

See Quickstart for an overview of basic nemos functionality.

We recommend using pynapple for initial exploration and reshaping of your data!

When initializing the GLM object, users can optionally specify the observation model and the regularizer.

Run the following pip command in your virtual environment.

For macOS/Linux users:

pip install git+https://github.com/flatironinstitute/nemos.git

For Windows users:

python -m pip install git+https://github.com/flatironinstitute/nemos.git

For more details, including specifics for GPU users and developers, refer to nemos docs.

Please note that this package is currently under development. While you can download and test the functionalities that are already present, please be aware that syntax and functionality may change before our preliminary release.

We communicate via several channels on Github:

• To report a bug, open an issue.
• To ask usage questions, discuss broad issues, or show off what you’ve made with nemos, go to Discussions.
• To send suggestions for extensions or enhancements, please post in the ideas section of discussions first. We’ll discuss it there and, if we decide to pursue it, open an issue to track progress.
• To contribute to the project, see the contributing guide.

In all cases, we request that you respect our code of conduct.