SpikeSense: Neurophysiological Data Processing Workflow

This repository contains the codebase for an efficient workflow designed for processing electrophysiological data. The workflow is specifically tailored for analyzing in silico generated data from MEArec.

Functionalities

• Data Processing: The workflow facilitates seamless processing of electrophysiological data originating from MEArec. This includes preprocessing steps to prepare data for further analysis.

• AI-based Spike Detection: The core of this project features a powerful Transformer model customized for spike detection in neurophysiological time series data. This allows advanced analysis and identification of relevant events in the data.

• Evaluation and visualization: Evaluation and visualization functions for further investigation of the development and analyzing process.

• Import: Handling different file formats in order to apply the trained model.

Usage

Installation

The project was developed under Python 3.9 and Linux Ubuntu 22.04 lts. For machine learning tasks a GPU is recommended.

To use the workflow, follow these steps:

1. Clone the repository: git clone https://github.com/tivenide/SpikeSense
2. Install the required dependencies: pip install -r requirements.txt

Please install MEArec separately and follow their instructions on: https://mearec.readthedocs.io/en/latest/installation.html

Data Processing (pretrained Transformer)

Execute data processing with the following command:

python cli/spike_detection.py path/to/MEArec_recording_input_data.h5 path/to/spike_trains_output_data

Quick start

Only for demonstration purposes to better understand the workflow and check the installation. Please adjust your MEArec data according to your requirement.

Data generation with MEArec

1. Use data_generation_MEArec/MEArec_data_generation_quick.py to generate some demo files.
2. Put the recordings for training into quick/MEArec_training_data_recordings.

Model development

Execute development (quick) with the following command:

python cli/quick_cli.py develop

Model application

Execute data processing with the following command:

python  cli/quick_cli.py process path/to/MEArec_recording_input_data.h5 path/to/spike_trains_output_data

Future Work

• choosing license

License

Copyright 2023 tivenide. All rights reserved.