Signal Reconstruction From Spiking Neuron Models
Abstract
We describe a method for perfect signal reconstruction from spiking neuron models such as integrate-and-fire or leaky integrate-and-fire neurons. These neural models encode a single analog signal in the timing of asynchronous digital pulses. We show that using only the output firing times of these neurons, we can recover a bandlimited input signal to within machine precision. A major application of this work is for a replacement of conventional analog-to-digital converters in some applications where simpler analog hardware is traded off for more complex reconstruction on the part of the subsequent digital processor. Realistic SPICE simulations of CMOS spiking neurons show that accurate reconstruction with more than 12-bit precision can be achieved. The effects of frequency aliasing, noise, and temporal quantization are considered.
Recommended Citation
D. Wei and J. G. Harris, "Signal Reconstruction From Spiking Neuron Models," Proceedings IEEE International Symposium on Circuits and Systems, vol. 5, pp. V - 353, Institute of Electrical and Electronics Engineers, Sep 2004.
Department(s)
Electrical and Computer Engineering
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
06 Sep 2004
