https://github.com/bryanlimy/calciumgan
Synthesising Realistic Calcium Imaging Data of Neuronal Populations Using GAN.
https://github.com/bryanlimy/calciumgan
calcium-imaging calcium-signals gan spike-analysis spike-trains
Last synced: about 1 year ago
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Synthesising Realistic Calcium Imaging Data of Neuronal Populations Using GAN.
- Host: GitHub
- URL: https://github.com/bryanlimy/calciumgan
- Owner: bryanlimy
- Created: 2019-10-03T14:26:40.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-07-06T22:00:09.000Z (almost 3 years ago)
- Last Synced: 2025-03-22T02:02:40.726Z (about 1 year ago)
- Topics: calcium-imaging, calcium-signals, gan, spike-analysis, spike-trains
- Language: Python
- Homepage:
- Size: 6.09 MB
- Stars: 2
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Synthesising Realistic Calcium Imaging Data of Neuronal Populations Using GAN
Implementation of CalciumGAN, used to obtain the results detailed in [CalciumGAN: A Generative Adversarial Network Model for Synthesising Realistic Calcium Imaging Data of Neuronal Populations](https://arxiv.org/abs/2009.02707).
Citing this work
```
@article{li2020calciumgan,
title={CalciumGAN: A Generative Adversarial Network Model for Synthesising Realistic Calcium Imaging Data of Neuronal Populations},
author={Li, Bryan M and Amvrosiadis, Theoklitos and Rochefort, Nathalie and Onken, Arno},
journal={arXiv preprint arXiv:2009.02707},
year={2020}
}
```
### Table of content
- [1. Installation](#1-installation)
- [2. Dataset](#2-dataset)
- [3. Training model](#3-train-model)
- [4. Spike analysis](#4-spike-analysis)
- [5. Visualization and Profiling](#5-visualization-and-profiling)
- [6. Hyper-parameter Search](#6-hyper-parameters-search)
## 1. Installation
### 1.1 Requirements
- It is recommended to install the codebase in a virtual environment,
such as [conda](https://conda.io/en/latest/).
### 1.2 Quick install
- Create a new `conda` environment in Python 3.6
```bash
conda create -n calciumgan python=3.6
```
- Activate `calciumgan` virtual environment
```bash
conda activate calciumgan
```
- Install all dependencies and packages with `setup.sh` script, works on both Linus and macOS.
```bash
sh setup.sh
```
### 1.3 Manual setup
Install the following packages:
- [TensorFlow](https://tensorflow.org)
- [j-friedrich/OASIS](https://github.com/j-friedrich/OASIS)
- [Neo](https://github.com/NeuralEnsemble/python-neo)
- [Elephant](https://github.com/NeuralEnsemble/elephant)
- packages in `requirements.txt`
- code from [dg_python](https://github.com/mackelab/dg_python) are also being
used for the dichotomized Gaussian model
## 2. Dataset
- Navigate to `dataset`
```bash
cd dataset
```
### 2.1 Recorded Calcium Imaging Data
- Place all raw calcium imaging data under `dataset/raw_data`
- Apply OASIS to infer spike train
```bash
python spike_train_inference.py --input_dir raw_data
```
- Generate `TFRecords` from a specific pickle file `--input`, normalize the
data, preform segmentation and store the `TFrecords` in `output_dir`.
Use `--help` to see all available arguments.
```bash
python generate_tfrecords.py --input raw_data/signals.pkl --output_dir tfrecords/sl2048 --sequence_length 2048 --normalize
```
#### 2.2 Dichotomized Gaussian Artificial Data
- Generate artificial spike trains and calcium-like signals from the
Dichomotized Gaussian distribution with the mean and covariance of data in
`--input`, save the the output pickle file to `--output`. `TFRecords` in `--output_dir`. Use `--help` to
see all available arguments.
```bash
python generate_dg_data.py --input raw_data/signals.pkl --output dg.pkl
```
- Generate `TFRecords` from a specific pickle file `--input`, normalize the
data, preform segmentation and store the `TFrecords` in `output_dir`.
Use `--help` to see all available arguments.
```bash
python generate_tfrecords.py --input dg.pkl --output_dir tfrecords/sl2048_dg --sequence_length 2048 --normalize
```
## 3. Train model
- To train CalciumGAN on the recorded calcium imaging data with the default
hyper-parameters for 400 epochs. Checkpoints, generated data, model training
information are stored in `--output_dir`.
```bash
python main.py --input_dir dataset/tfrecords/sl2048 --output_dir runs/001 --epochs 400 --batch_size 128 --model calciumgan --algoirthm wgan-gp --noise_dim 32 --num_units 64 --kernel_size 24 --strides 2 --m 10 --layer_norm --mixed_precision --save_generated last
```
- Use `--help` to check all available arguments. Mixed precision compute,
TensorBoard profiling, hyper-parameter search are some of the features built
into this codebase.
- The training command applies to both recorded data and dischotomized
Gaussian artificial data.
## 4. Spike analysis
#### 4.1 Recorded Calcium Imaging Data
- Deconvolve the calcium signals to spike trains from generated data in
`--output_dir`, then compute various spikes statistics.
Use `--help` to check all available arguments.
```bash
python compute_metrics.py --output_dir runs/001
```
- All the plots can be found in `runs/001/metrics/plots`
#### 4.2 Dichotomized Gaussian Artificial Data
- Deconvolve the calcium signals to spike trains from generated data in
`--output_dir`, then compute various spikes statistics.
Use `--help` to check all available arguments.
```bash
python compute_dg_metrics.py --output_dir runs/002
```
- PLots of mean and covariance can be found in `diagrams/`
### 5. Visualization and Profiling
- Run `tensorboard`
```bash
tensorboard --logdir runs/001
```
- We have implemented profiling with
[TensorFlow Profiler](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras) support.
You can enable profiling with `--profile` flag when training the model with `main.py`.
### 6. Hyper-parameters Search
- We have incorporated the [Hyperparameter Turning with Keras](https://www.tensorflow.org/tensorboard/hyperparameter_tuning_with_hparams) feature.
Modify the hyper-parameters you would like to test in `seasrch.py` and run
```bash
python search.py --input_dir dataset/tfrecords/sl2048 --output_dir runs/hparams_search --epochs 400 --mixed_precision
```