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‎.gitignore‎

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__pycache__/
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output/
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data/
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checkpoint/
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.DS_Store
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.ipynb_checkpoints
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*/.ipynb_checkpoints/*
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.\ /
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docs/_build/
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt

‎LICENSE‎

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MIT License
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Copyright (c) 2021 David Liu
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md‎

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# A universal probabilistic spike count model reveals ongoing modulation of neural variability (NeurIPS 2021)
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## Overview
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This is the code repository for this [paper](https://www.biorxiv.org/content/10.1101/2021.06.27.450063v2).
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Models are implemented in Python with dependencies on libraries listed below at the end.
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We also include a neural data analysis library that was written for constructing scalable neural encoding models using a modern deep learning framework.
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The baseline models, along with the universal count model proposed in our work, are implemented in the library and can be used for analysis of other neural datasets.
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<p align="center">
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<img src="./plots/schematic.png" width="800"/>
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</p>
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## Reproducing the results in the paper
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#### 1. cd into ./scripts/
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Here is where all the code for fitting models, analysis and plotting is located.
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#### 2. (Optional) Run the synthetic_data notebook to generate data from synthetic populations
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This generates the two synthetic populations and saves them into ./data/, both generated spike counts and behaviour as well as the encoding models.
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Note that the population data used in the paper has been included in ./data/, running this script will overwrite those files!
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#### 3. Run the scripts to fit models
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##### Command line format
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Run commands based on the following formats into the command line:
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```
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python3 validation.py --cv -1 2 5 8 --gpu 0 --modes 0 --datatype 0 --ncvx 2 --lr 1e-2 --lr_2 1e-3 --batchsize 10000
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```
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This runs a model of mode 0 on synthetic data, with `--cv` indicating which cross-validation fold to leave out for validation (-1 indicates using all data) and `--gpu` indicating the GPU device to run on (if available).
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Line 188 in validation.py gives the definition of all modes (numbered 0 to 8), in particular the likelihood (1st element of tuple) and the input space (2d element of tuple) are specified.
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Note there is a 10-fold split of the data, hence the cv trial numbers can go from -1 to 9.
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`lr` and `lr_2` indicate the learning rates, with `lr_2` for kernel and variational standard deviations (lower for latent models as described in the paper).
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The flag `--ncvx` refers to the number of runs to do (selecting the best fit model after completion to save).
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One can also specify `--batchsize`, which can speed up training when larger depending on the memory capacity of the hardware used.
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For validation.py, the flag `--datatype` can be 0 (heteroscedastic Conway-Maxwell-Poisson) or 1 (modulated Poisson).
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```
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python3 HDC.py --cv -1 1 3 6 --gpu 0 --modes 0 --ncvx 2 --lr 1e-2 --lr_2 1e-3 --binsize 40
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```
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Similarly, this runs a model of mode 0 on head direction cell data, with head direction cell data binned into 40 ms bins set by `--binsize`.
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Line 108 in HDC.py gives the definition of all modes for the head direction cell models (numbered 0 to 11).
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All possible flags and their default values can be seen in the validation.py and HDC.py scripts.
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The file models.py defines the encoding models and uses the library code (neuroprob) to implement and run these probabilistic models.
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In terms of neural data, the synthetic populatio data used in the paper and the head direction cell data is included in the ./data/ folder.
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All required modes in the analysis notebooks can be seen in the code as it loads trained models.
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Note that there are separate notebooks for synthetic (validation) and real (HDC) datasets.
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All trained models are stored in the ./checkpoint/ folder.
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##### Experiments in the paper
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- Synthetic data
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`python3 validation.py --cv -1 2 5 8 --gpu 0 --modes 0 1 2 3 --datatype 0 --ncvx 2 --lr 1e-2` (regression models)
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`python3 validation.py --cv -1 2 5 8 --gpu 0 --modes 4 5 6 7 --datatype 0 --ncvx 3 --lr 1e-2 --lr_2 1e-3` (latent variable models)
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`python3 validation.py --cv -1 2 5 8 --gpu 0 --modes 0 2 8 --datatype 1 --ncvx 2 --lr 1e-2` (capturing noise correlations and single neuron variability)
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- Head direction cell data
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`python3 HDC.py --cv -1 1 2 3 5 6 8 --gpu 0 --modes 0 1 4 --ncvx 2 --lr 1e-2 --binsize 40` ( regression with different likelihoods)
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`python3 HDC.py --cv -1 1 2 3 5 6 8 --gpu 0 --modes 2 3 --ncvx 2 --lr 1e-2 --binsize 40` (regression with different regressors)
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`python3 HDC.py --cv -1 1 2 3 5 6 8 --gpu 0 --modes 5 6 7 8 --ncvx 3 --lr 1e-2 --lr_2 1e-3 --binsize 40` (joint latent-observed models)
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`python3 HDC.py --cv -1 1 2 3 5 6 8 --gpu 0 --modes 9 10 11 --ncvx 3 --lr 3e-2 --lr_2 5e-3 --binsize 100` (latent variable models)
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If you wish to run different modes or cross-validation runs grouped together above in parallel, run the command several times with only a single mode or cv trial each time.
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#### 4. Run the analysis notebooks to analyze the data
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By running the analysis notebooks, we reproduce the plotting data for figures in the paper.
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Intermediate files (pickled) will be stored in the ./checkpoint/ folder.
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#### 5. Run the plotting notebooks
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This loads the analysis results and plots paper figures in .pdf and .svg formats, exported to the ./output/ folder.
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## Neural data analysis library
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Here we present a short description of a preliminary version of the neural data analysis library used (called neuroprob) to facilitate constructing neural encoding models.
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The list below shows what has implemented for use so far, see the models.py file for an example of code utilizing the library.
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#### Primitives
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There are three kinds of objects that form the building blocks:
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1. Input group *p(X,Z)* and *q(Z)*
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2. Mapping *p(F|X,Z)*
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3. Likelihood *p(Y|F)*
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The overal generative model is specified along with the variational posterior through these primitives.
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Input groups can contain observed and latent variables, with different priors one can put onto the latent variables.
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#### Models implemented
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* Linear-nonlinear and GP mappings
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* RNNs
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* LVMs
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- Toroidal latent space priors ([Manifold GPLVM](https://arxiv.org/abs/2006.07429))
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- AR(1) temporal prior on latents
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* GLM filters
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- spike-history couplings
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- spike-spike couplings
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- stimulus history
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* Inhomogenenous renewal point processes
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- Gamma
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- Inverse Gaussian
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- Log Normal
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* Count process likelihoods
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- Poisson
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- Zero-inflated Poisson
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- Negative binomial
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- Conway-Maxwell-Poisson
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- Universal (this work)
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* Gaussian likelihoods
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- Univariate
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## Dependencies:
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- [PyTorch](https://pytorch.org/) version >= 1.7
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- [NumPy](https://numpy.org/)
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- [SciPy](https://scipy.org/)
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- [tqdm](https://tqdm.github.io/) for visualizing fitting/training progress
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- [Daft](https://docs.daft-pgm.org/en/latest/) to plot graphical model components

‎neuroprob/GP/__init__.py‎

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#! /usr/bin/env python
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# -*- coding: utf-8 -*-
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from . import kernels, linalg, means

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