API Reference

The nemos.glm module

Classes for creating Generalized Linear Models (GLMs) for both single neurons and neural populations.

GLM	Generalized Linear Model (GLM) for neural activity data.
PopulationGLM	Population Generalized Linear Model.

The nemos.basis module

Provides basis function classes to construct and transform features for model inputs. Basis can be grouped according to the mode of operation into basis that performs convolution and basis that operates as non-linear maps.

The Abstract Classes:

These classes are the building blocks for the concrete basis classes.

Basis

Abstract base class for defining basis functions for feature transformation.

SplineBasis

SplineBasis class inherits from the Basis class and represents spline basis functions.

Bases For Convolution:

MSplineConv	M-spline basis functions for modeling and data transformation.
BSplineConv	B-spline 1-dimensional basis functions.
CyclicBSplineConv	B-spline 1-dimensional basis functions for cyclic splines.
RaisedCosineLinearConv	Represent linearly-spaced raised cosine basis functions.
RaisedCosineLogConv	Represent log-spaced raised cosine basis functions.
OrthExponentialConv	Set of 1D basis decaying exponential functions numerically orthogonalized.
HistoryConv	Basis for history effects.

Bases For Non-Linear Mapping:

MSplineEval	M-spline basis functions for modeling and data transformation.
BSplineEval	B-spline 1-dimensional basis functions.
CyclicBSplineEval	B-spline 1-dimensional basis functions for cyclic splines.
RaisedCosineLinearEval	Represent linearly-spaced raised cosine basis functions.
RaisedCosineLogEval	Represent log-spaced raised cosine basis functions.
OrthExponentialEval	Set of 1D basis decaying exponential functions numerically orthogonalized.
IdentityEval	Identity basis function.

Composite Bases:

AdditiveBasis	Class representing the addition of two Basis objects.
MultiplicativeBasis	Class representing the multiplication (external product) of two Basis objects.

Basis As ``scikit-learn`` Tranformers:

TransformerBasis

Basis as scikit-learn transformers.

The nemos.observation_models module

Statistical models to describe the distribution of neural responses or other predicted variables, given inputs.

Observations	Abstract observation model class for neural data processing.
PoissonObservations	Model observations as Poisson random variables.
GammaObservations	Model observations as Gamma random variables.

The nemos.regularizer module

Implements various regularization techniques to constrain model parameters, which helps prevent overfitting.

Regularizer	Abstract base class for regularized solvers.
UnRegularized	Regularizer class for unregularized models.
Ridge	Regularizer class for Ridge (L2 regularization).
Lasso	Regularizer class for Lasso (L1 regularization).
GroupLasso	Regularizer class for Group Lasso (group-L1) regularized models.

The nemos.simulation module

Utility functions for simulating spiking activity in recurrently connected neural populations.

simulate_recurrent	Simulate neural activity using the GLM as a recurrent network.
difference_of_gammas	Generate coupling filter as a Gamma pdf difference.
regress_filter	Approximate scipy.stats.gamma based filters with basis function.

The nemos.convolve module

Utility functions for running convolution over the sample axis.

create_convolutional_predictor	Create a convolutional predictor by convolving a basis matrix with a time series.
tensor_convolve	Apply a convolution on the given array with the evaluation basis and reshapes the result.

The nemos.identifiability_constraints module

Functions to apply identifiability constraints to rank-deficient feature matrices, ensuring the uniqueness of model solutions.

apply_identifiability_constraints	Apply identifiability constraints to a design matrix X.
apply_identifiability_constraints_by_basis_component	Apply identifiability constraint to a design matrix for each component of an additive basis.

The nemos.pytrees.FeaturePytree class

Class for storing the input arrays in a dictionary. Keys are usually variable names. These objects can be provided as input to nemos GLM methods.

FeaturePytree

Pytree to represent GLM features.