A Event-Based, Digital Time Difference Encoder Model for Neuromorphic Systems

The Time Difference Encoder (TDE) is a model which encodes the relative timing between two events into a burst of spikes.

Table of contents

• Introduction
• What is the TDE?
• How it works?
• Digital implementation
• Behavior overview
• Key features
• Neuromorphic applications
• Credits
• License
• Cite this work

What is the Time Difference Encoder?

The spiking version of the EMD model was proposed by Milde et al. in this paper.

How the TDE works?

The Time Difference Encoder (TDE) is a model which encodes the relative timing between two events into a burst of spikes.

Digital implementation

IP block overview.

Block diagram of the proposed model.

Behavior overview

Brief explanation about how the TDE works.

Key features

Next, we summarize the main features of the TDE digital implementation:

• Scalability
• Adaptability

Neuromorphic applications

List of open projects which are using this model.

Event-based Optical Flow estimation

Estimate the optical flow by using event-based cameras and TDE-based networks.

Event-based Sound Source Localization

Determine the localization of a sound source by using the Neuromorphic Auditory Sensor (NAS) and TDE-based networks.

Credits

We would like to thank and give credit to:

• Robotics and Technology of Computers Lab. from the University of Sevilla (Spain).
• Neuromorphic Behavin Systems, CITEC, Biëlefeld University (Germany)

License

This project is licensed under the GPL License - see the LICENSE.md file for details.

Copyright © 2020 Daniel Gutierrez-Galan
dgutierrez@atc.us.es

Cite this work

APA: Gutierrez-Galan, D., Schoepe, T., Dominguez-Morales, J. P., Jimenez-Fernandez, A., Chicca, E., & Linares-Barranco, A. (2021). An event-based digital time difference encoder model implementation for neuromorphic systems. IEEE Transactions on Neural Networks and Learning Systems.

ISO 690: GUTIERREZ-GALAN, Daniel, et al. An event-based digital time difference encoder model implementation for neuromorphic systems. IEEE Transactions on Neural Networks and Learning Systems, 2021.

MLA: Gutierrez-Galan, Daniel, et al. "An event-based digital time difference encoder model implementation for neuromorphic systems." IEEE Transactions on Neural Networks and Learning Systems (2021).

BibTeX: @article{gutierrez2021event, title={An event-based digital time difference encoder model implementation for neuromorphic systems}, author={Gutierrez-Galan, Daniel and Schoepe, Thorben and Dominguez-Morales, Juan P and Jimenez-Fernandez, Angel and Chicca, Elisabetta and Linares-Barranco, Alejandro}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, publisher={IEEE} }