Total Spiking Probability Edges (TSPE) is a cross-correlation-based method for effective connectivity estimation of cortical spiking neurons.
Connectivity is a critical parameter in understanding the information flow within neuronal networks. Reconstructing network connectivity from recorded spike train data has been an area of active research, leading to the development of various estimation methods.
TSPE is a novel and computationally efficient algorithm designed for effective connectivity estimation. Its primary features include:
- Cross-Correlation: TSPE calculates a cross-correlation between pairs of spike trains to measure their relationship.
- Edge Filters: It applies filters on the resulting cross-correlograms to distinguish between excitatory and inhibitory connections.
- Performance: TSPE achieves a high true positive rate (approx. 99%) with a low false positive rate (1%) for low-density random networks, depending on the network topology and spike train duration.
- Efficiency: It processes large datasets quickly, taking less than three minutes on a high-performance computer to estimate connectivity from an hour-long dataset of 1000 spike trains.
- Distinction Capability: The algorithm effectively distinguishes between excitatory and inhibitory connections.
This repository provides the MATLAB implementation of TSPE as used in the original publication. While functional, this version may be outdated. For the latest implementation, refer to the Python version available in the Elephant
library.
- Description: This implementation reproduces the results described in the original TSPE publication.
- Status: No further updates will be made to this version.
-
Description: The most up-to-date implementation of TSPE is included in the
Elephant
library, offering improved usability and documentation. -
Documentation: Refer to the following link for detailed instructions and examples:
-
Clone the Repository:
git clone https://github.com/yourusername/tspe.git cd tspe
-
MATLAB Usage: Open the
.m
files in MATLAB and follow the provided examples. -
Python Usage: Use the
Elephant
library for the latest TSPE implementation.To install Elephant:
pip install elephant
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Further Information: Refer to the publication for theoretical details and performance analysis of the TSPE algorithm.
If you use TSPE in your research, please cite the original publication: De Blasi, S., Ciba, M., Bahmer, A., & Thielemann, C. (2019). Total spiking probability edges: A cross-correlation-based method for effective connectivity estimation of cortical spiking neurons. Journal of Neuroscience Methods, 312, 169–181. DOI: 10.1016/j.jneumeth.2018.11.013 Link to the publication
This project is distributed under the MIT License. See the LICENSE
file for details.
For inquiries or issues related to this repository, please contact:
Manuel Ciba
or
BioMEMS lab (University of Applied Sciences)