Abstract
We have developed the time derivative neuron to reduce the bandwidth needed to encode continuous-time input signals using spiking neuron models. Unlike conventional spiking neuron models, the time derivative neuron generates more spikes in regions of high change and is essentially invariant to DC shifts of the input. We describe the model and develop an efficient reconstruction algorithm to demonstrate its reconstruction accuracy and transmission bandwidth. The idea has both digital and analog hardware realizations and we show a SPICE transistor-level simulation of an efficient analog CMOS implementation. ©2008 IEEE.
Recommended Citation
J. Xu and J. G. Harris, "The Time Derivative Neuron," Proceedings IEEE International Symposium on Circuits and Systems, pp. 436 - 439, article no. 4541448, Institute of Electrical and Electronics Engineers, Sep 2008.
The definitive version is available at https://doi.org/10.1109/ISCAS.2008.4541448
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-142441684-4
International Standard Serial Number (ISSN)
0271-4310
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
19 Sep 2008
