NPDS Matlab toolbox

NPDS Toolbox: Neural Population(De)Synchronization toolbox for Matlab

Authors

Mohammad Mahdi Moayeri, Mohammad Hemami, Jamal Amani Rad, Kourosh Parand

Welcome to NPDS’s documentation!

NPDS Toolbox is an open-source MATLAB toolbox for implementing phase distribution controls on different noise-free or noisy neural oscillator populations in order to desynchronize/synchronize them. This toolbox has a graphical userinterface which is designed using GUIDE in MATLAB. This toolbox is useful for researchers who want to study the theoretical aspects of neural dynamics ordesigning distribution controls for neural populations. This Matlab toolbox currently has the the following capabilities:

• Simulating the controlling the neural oscillators synchronization by phase distribution controls without any programming efforts.

• Using the proportional control, bang-bang or user-defined control inputs.

• Defining the deterministic or stochastic neural oscillator populations with Gaussian white noise.

• Defining various phase response curves (PRC) related to different neuralmodels such as Hodgkin-Huxley (HH), Fitzhugh-Nagumo (FHN), Rose-Hindmarsh (RH), and Thalamic.

• Investigating the dynamics of different neural models such as Hodgkin-Huxley (HH), Fitzhugh-Nagumo (FHN), Rose-Hindmarsh (RH), and Thalamic.

• Defining the user-defined distributions or using well-known distributionssuch as Von-Mises or uniform ones for initial and final distributions.

• Possibility of using different numerical simulation approaches for simula-tions such as finite-difference, meshless, spectral, or Fourier decompositionmethods.

• Exporting the results as figures or matrices.

Synchronization plays a very significant role in brain functions such as perception, memory, neural information exchange, cognitive processing, and movement. On the other hand, excessive neuronal synchrony is one of the causes of the generation of epileptic seizures; thus, a fine balance between synchronization and desynchronization is functionally and behaviourally important. The phase distribution control approach is one of the suitable choices in order to make the analysis of high-dimensional systems such as neural dynamics more convenient and makes the designing of the control systems experimentally more applicable. This toolbox is designed so that researchers can investigate the theories of dynamics and synchronization of neural models without any knowledge of programming and scientific computing.

Fig. 1 Screenshot of the NPDS toolbox

Quick Links

Documentation: https://npds.readthedocs.io

Mailing list: https://groups.google.com/g/npds-users

Source code: https://github.com/cmplab/npds-toolbox

Bug report: https://github.com/cmplab/npds-toolbox/issues

Documentation as a pdf file: https://media.readthedocs.org/pdf/npds/latest/npds.pdf

Maintainer: Mohammad Mahdi Moayeri, [mahdi.myr@gmail.com]; MohammadHemami, [gaslakh@gmail.com]

Organization: Shahid Beheshti University

Copyright: This document has been placed in the public domain.

License: NPDS is released under the BSD 3-Clause license.

Version: 1.0

Date: July 2, 2021

Contributors

This toolbox was developed by Mohammad Mahdi Moayeri and Mohammad Hemami. It is part of ongoing scientific work atShahid Beheshti University, in collaboration with Dr. Jamal Amani Rad and Prof. Kourosh Parand.

How to cite

If you used NPDS toolbox or some of its codes for your own research, please cite us (using the published paper DOI below):

https://doi.org/10.1016/j.neucom.2022.07.060

or zenodo DOI below:

Logo

We thank MahmoudReza Afra who designed and donated the logo for the NPDS toolbox.

Download

Source code is hosted at Github.

Download source code as a .zip file.

Download the documentation as a pdf file.

Acknowledgments

The authors are grateful to Jeff Moehlis, University of California, Santa Barbara, and Bharat Monga, Intel Corporation. Through their research in this field, they instilled in us the idea to realize the importance of package development in this field and to achieve it in the best possible way.

Feedback & Support

If you have problems installing the software or questions about usage and documentation, or something else related to NPDS toolbox, you can post to the Issues section of our repository. If you have any questions or would like to contribute, you can write us at cmplab@sbu.ac.ir.

References

• M. M. Moayeri, M. Hemami, J. A. Rad, K. Parand, “NPDS toolbox: Neural population (De) synchronization toolbox for MATLAB”, Neurocomputing, 506 (2022), 206-212.

• M. Hemami, J. A. Rad, K. Parand, “Phase distribution control of neural oscillator populations using local radial basis function meshfree technique with application in epileptic seizures: A numerical simulation approach”, Communications in Nonlinear Science and Numerical Simulation, 103 (2021), 105961.

• M. M. Moayeri, J. A. Rad, K. Parand, “Desynchronization of stochastically synchronized neural populations through phase distribution control: a numerical simulation approach”, Nonlinear Dyn., 104 (2021), 2363-2388.

• B. Monga and J. Moehlis, “Phase distribution control of a population of oscillators”, Physica D., 398 (2019), 115-129.

• B. Monga, G. Froyland and J. Moehlis, “Synchronizing and Desynchronizing Neural Populations through Phase Distribution Control”, 2018 Annual American Control Conference (ACC), (2019), 2808-2813.

• B. Monga, D. Wilson, T. Matchen and J. Moehlis, “Phase reduction and phase-based optimal control for biological systems: a tutorial”, Biol Cybern., 113 (2018), 11-46.

Contents

• 1. Getting started
  • 1.1. Dependecies
  • 1.2. Installation
  • 1.3. How to use the GUI
  • 1.4. The NPDS Toolbox panels
• 2. Problem Statement
  • 2.1. Phase density equation of decoupled and noise free neural oscillators
  • 2.2. Phase density equation of decoupled and noisy neural oscillators
• 3. Mathematical Background
  • 3.1. Finite difference method
  • 3.2. Spectral method
  • 3.3. Radial basis function method
  • 3.4. Radial basis function generated finite difference method
  • 3.5. Fourier decomposition method
  • 3.6. Runge-Kutta algorithm
    • 3.6.1. Fourth-order Runge-Kutta algorithm for ODEs
    • 3.6.2. Fourth-order Runge-Kutta algorithm for stochastic ODEs
• 4. Applications
  • 4.1. Neural populations (de)synchronization
    • 4.1.1. The display panels
    • 4.1.2. Control inputs
    • 4.1.3. Phase response curve (PRC)
    • 4.1.4. Initial and final distributions
  • 4.2. Neural dynamics
    • 4.2.1. The display panels
    • 4.2.2. Hodgkin-Huxley model
    • 4.2.3. Fitzhugh-Nagumo model
    • 4.2.4. Rose-Hindmarsh model
    • 4.2.5. Thalamic neuron model
• 5. Appendix
• 6. References