PyNEMS 0.0.3

Fit and evaluate models of sensory encoding and decoding.

NEMS

The Neural Encoding Model System (NEMS) is helpful for fitting a mathematical model to time series data, plotting the model predictions, and comparing the predictive accuracy of multiple models. We use it to develop and test computational models of how sound is encoded in the brains of behaving mammals, but it will work with many different types of timeseries data.

Note: this is a refactor of a former version of NEMS that is now archived at LBHB/NEMS0. This refactor is still very much a work in progress, and is likely to contain bugs, missing documentation, and a lot of TODOs..

Examples

Build a standard linear-nonlinear spectrotemporal receptive field (LN-STRF) model.

from nems import Model
from nems.layers import FiniteImpulseResponse, DoubleExponential

model = Model()
model.add_layers(
    FiniteImpulseResponse(shape=(15, 18)),  # 15 taps, 18 spectral channels
    DoubleExponential(shape=(1,))           # static nonlinearity, 1 output
)

Or use the customizable keyword system for faster scripting and prototyping.

from nems import Model

same_model = Model.from_keywords('fir.15x18-dexp.1')

Fit the model to (fake) evoked neural activity (in this case, in response to a sound represented by a spectrogram).

import numpy as np

spectrogram = np.random.rand(1000, 18)  # 1000 time bins, 18 channels
response = np.random.rand(1000, 1)      # 1 neural response

fitted_model = model.fit(spectrogram, response)

Predict the response to a different stimulus.

test_spectrogram = np.random.rand(1000, 18)
prediction = fitted_model.predict(test_spectrogram)

Score the prediction

from nems.metrics import correlation
print(correlation(prediction, response))
# OR
print(model.score(test_spectrogram, response, metric='correlation'))

Try the above examples with real data:

import nems

nems.download_demo()
training_dict, test_dict = nems.load_demo()

# Each dictionary contains a 100 hz natural sound spectrogram and
# the PSTH / firing rate of the recorded spiking response.
spectrogram = training_dict['spectrogram']
response = training_dict['response']

Installation instructions

Recommended: Intall from source.

NEMS is still under rapid development, so this is the best way to ensure you're using the most up-to-date version.

1. Download source code

git clone https://github.com/lbhb/nems

2a. Create and activate a new virtual environment using your preferred environment manager (example for venv below).

python -m venv ./nems-env
./nems-env/scripts/activate

2b. Install frozen dependencies. This will install the exact versions used during development.

pip install -r .\NEMS\requirements.txt

2c. Alternatively, use conda to replace both step 2a and step 2b.

conda env create -f NEMS/environment.yml
conda activate nems-env

3. Install NEMS in editable mode along with optional development tools.

pip install -e NEMS[dev]

4. Run tests to ensure proper installation. We recommend repeating this step after making changes to the source code.

pytest NEMS

Alternative: PyPI (pip)

Create a new environment using your preferred environment manager, then use pip install.

conda create -n nems-env python=3.9 pip  # note that python=3.9 is currently required
pip install PyNEMS                       # note the leading Py

Alternative: conda install.

Coming soon.

Note: the mkl library for numpy does not play well with tensorflow. If using conda to install dependencies manually, and you want to use the tensorflow backend, use conda-forge for numpy (which uses openblas instead of mkl):

conda install -c conda-forge numpy

(See: https://github.com/conda-forge/numpy-feedstock/issues/84)

Upcoming features/updates

Roughly in order of priority:

• Add core pre-processing and scoring from nems0.
• Set up readthedocs.
• Other core features (like jackknifed fits, cross-validation, etc.).
• Enable Travis build (.travis.yml is already there, but not yet tested).
• Publish through conda.
• Backwards-compatibility tools for loading nems0 models.
• Implement Jax back-end.
• ... (see issues tracker).