NEMS0 is an archived version of the Neural Encoding Model System (NEMS). This repository is being phased out and replaced by a "lite" and more user-friendly version of NEMS at https://github.com/LBHB/NEMS.
However, we are maintaining this repository to support analysis of some published data.
NEMS is the Neural Encoding Model System. It 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.
Installing NEMS requires python (tested with version 3.7) and git. We recommend using conda to create an environment specifically for NEMS. More recent versions of python are likely to work, but the Quick Install below may not work out of the box, as you may need to make sure that the various dependencies have compatible versions.
conda create -n nems python=3.7
Once you have python and git installed, download NEMS:
git clone https://github.com/lbhb/NEMS0
If using conda, make sure you have activated your NEMS environment. Then add the NEMS0 library via pip (where ./NEMS is the installation directory and -e means editable mode):
pip install -e ./NEMS
NEMS libraries should now be loadable. See next section for how to try it out!
You may test if everything is working by telling NEMS to download some sample auditory stimulus-response data, use a simple linear-nonlinear model (which should taking about 2 minutes to fit), and then save the results locally:
cd NEMS0/scripts ipython In [1]: run demo_script.py
Or open demo_script.py in an editor to work through each step of
the fit.
If you have Jupyter installed:
cd NEMS/notebooks jupter notebook
Click on demo_xforms.ipynb and give it a whirl!