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nemos

NEural MOdelS, a statistical modeling framework for neuroscience.

https://github.com/flatironinstitute/nemos

Science Score: 52.0%

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Repository

NEural MOdelS, a statistical modeling framework for neuroscience.

Basic Info
Statistics
  • Stars: 101
  • Watchers: 4
  • Forks: 11
  • Open Issues: 124
  • Releases: 12
Created about 3 years ago · Last pushed 6 months ago
Metadata Files
Readme Contributing License Code of conduct Citation Codeowners

README.md

License: MIT Python version Project Status: Active – The project has reached a stable, usable state and is being actively developed. PyPI - Version codecov Documentation Status nemos CI

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).

We provide a Poisson GLM for analyzing spike counts, and a Gamma GLM for calcium or voltage imaging traces.

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.

Overview

At his core, NeMoS consists of two primary modules: the basis and the glm module.

The basis module focuses on designing model features (inputs) for the GLM. It includes a suite of composable feature constructors that accept time-series data as inputs. These inputs can be any observed variables, such as presented stimuli, head direction, position, or spike counts.

The basis objects can perform two types of transformations on the inputs:

  1. Non-linear Mapping: This process transforms the input data through a non-linear function, allowing it to capture complex, non-linear relationships between inputs and neuronal firing rates. Importantly, this transformation preserves the properties that makes GLM easy to fit and guarantee a single optimal solution (e.g. convexity).

  2. Convolution: This applies a convolution of the input data with a bank of filters, designed to capture linear temporal effects. This transformation is particularly useful when analyzing data with inherent time dependencies or when the temporal dynamics of the input are significant.

Both transformations produce a vector of features X that changes over time, with a shape of (n_time_points, n_features).

On the other hand, the glm module maps the feature to spike counts. It is used to learn the GLM weights, evaluating the model performance, and explore its behavior on new input.

Examples

Here's a brief demonstration of how the basis and glm modules work together within NeMoS.

Poisson GLM for features analysis

In this example, we'll construct a time-series of features using the basis objects, applying a non-linear mapping (default behavior):

Feature Representation

```python import nemos as nmo

Instantiate the basis

basis1 = nmo.basis.MSplineEval(nbasisfuncs=5) basis2 = nmo.basis.CyclicBSplineEval(nbasisfuncs=6) basis3 = nmo.basis.MSplineEval(nbasis_funcs=7)

basis = basis1 * basis2 + basis_3

Generate the design matrix starting from some raw

input time series, i.e. LFP phase, position, etc.

X = basis.computefeatures(input1, input2, input3) ```

GLM

```python

Fit the model mapping X to the spike count

time-series y

glm = nmo.glm.GLM().fit(X, y)

Inspect the learned coefficients

print(glm.coef, glm.intercept)

compute the rate

firing_rate = glm.predict(X)

compute log-likelihood

ll = glm.score(X, y) ```

Poisson GLM for neural population

This second example demonstrates feature construction by convolving the simultaneously recorded population spike counts with a bank of filters, utilizing the basis in conv mode. The figure above show the GLM scheme for a single neuron, however in NeMoS you can fit jointly the whole population with the PopulationGLM object.

Feature Representation

```python import nemos as nmo

assume that the population spike counts time-series is stored

in a 2D array spikecounts of shape (nsamples, n_neurons).

generate 5 basis functions of 100 time-bins,

and convolve the counts with the basis.

X = nmo.basis.RaisedCosineLogConv(5, windowsize=100 ).computefeatures(spike_counts) ```

Population GLM

```python

fit a GLM to the first neuron counts time-series

glm = nmo.glm.PopulationGLM().fit(X, spike_counts)

compute the rate

firing_rate = glm.predict(X)

compute log-likelihood

ll = glm.score(X, spike_counts) ```

For a deeper dive, see our Quickstart guide and consider using pynapple for data exploration and preprocessing. When initializing the GLM object, you may optionally specify an observation model and a regularizer.

Note: Multi-epoch Convolution

If your data is formatted as a pynapple time-series, the convolution performed by the basis objects will be executed epoch-by-epoch, avoiding the risk of introducing artifacts from gaps in your time-series.

Installation

Run the following pip command in your virtual environment.

For macOS/Linux users: bash pip install nemos

For Windows users: python -m pip install nemos

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

Disclaimer

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.

Getting help and getting in touch

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.

Support

This package is supported by:

  • The Center for Computational Neuroscience, in the Flatiron Institute of the Simons Foundation.
  • The NIH BRAIN Initiative (1RF1MH133778).

Flatiron Center for Computational Neuroscience logo.

Owner

  • Name: Flatiron Institute
  • Login: flatironinstitute
  • Kind: organization
  • Location: New York City

@SimonsFoundation

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it using the preferred-citation metadata."
title: ""NEural MOdelS, a statistical modeling framework for neuroscience."
authors:
  - family-names: Balzani
    given-names: Edoardo
    orcid: "https://orcid.org/0000-0002-3702-5856"
  - family-names: Broderick
    given-names: William
    orcid: "https://orcid.org/0000-0002-8999-9003"
  - family-names: Viejo
    given-names: Guillaume
    orcid: "https://orcid.org/0000-0002-2450-7397"
  - family-names: Williams
    given-names: Alex
    orcid: "https://orcid.org/0000-0001-5853-103X"
repository-code: "https://github.com/flatironinstitute/nemos"
url: "https://nemos.readthedocs.io/"
license: MIT
keywords:
  - neuroscience
  - Poisson GLM
  - spike counts modeling

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 145
  • Total pull requests: 195
  • Average time to close issues: 4 months
  • Average time to close pull requests: 17 days
  • Total issue authors: 15
  • Total pull request authors: 11
  • Average comments per issue: 0.99
  • Average comments per pull request: 1.33
  • Merged pull requests: 149
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 43
  • Pull requests: 115
  • Average time to close issues: 3 months
  • Average time to close pull requests: 7 days
  • Issue authors: 9
  • Pull request authors: 8
  • Average comments per issue: 0.63
  • Average comments per pull request: 1.49
  • Merged pull requests: 91
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • BalzaniEdoardo (89)
  • billbrod (49)
  • sjvenditto (9)
  • bagibence (5)
  • EricThomson (5)
  • clewis7 (3)
  • ahwillia (2)
  • samdeoxys1 (2)
  • FlyingFordAnglia (2)
  • macari216 (1)
  • ZhexinXu (1)
  • wulfdewolf (1)
  • arturoptophys (1)
  • gviejo (1)
  • RobertoDF (1)
Pull Request Authors
  • BalzaniEdoardo (162)
  • billbrod (28)
  • bagibence (10)
  • gviejo (6)
  • clewis7 (5)
  • sjvenditto (5)
  • pcrespo2017 (5)
  • camila-maura (3)
  • pranmod01 (3)
  • EricThomson (3)
  • arnabiswas (2)
  • ahwillia (1)
Top Labels
Issue Labels
documentation (3) enhancement (2) bug (2) good first issue (1)
Pull Request Labels
enhancement (3) bug (2)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 181 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 11
  • Total maintainers: 1
pypi.org: nemos

NEural MOdelS, a statistical modeling framework for neuroscience.

  • Documentation: https://nemos.readthedocs.io/
  • License: MIT License Copyright (c) 2023 nemos authors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  • Latest release: 0.2.3
    published 10 months ago
  • Versions: 11
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 181 Last month
Rankings
Dependent packages count: 9.8%
Average: 38.8%
Dependent repos count: 67.9%
Maintainers (1)
edoardoBalzani
Last synced: 6 months ago

Dependencies

.github/workflows/ci.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
  • re-actors/alls-green v1.2.2 composite
.github/workflows/connect.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
  • mxschmitt/action-tmate v3 composite
pyproject.toml pypi
  • jax >=0.4
  • jaxopt >=0.6
  • matplotlib >=3.7
  • numpy >1.20
  • scikit-learn >=1.2
  • scipy >=1.10
  • typing_extensions >=4.6