High accuracy implementation of Adaptive Exponential integrated and fire neuron model
Document Type
Conference Proceeding
Publication Date
10-31-2016
Publication Title
Proceedings of the International Joint Conference on Neural Networks
Volume
2016-October
First Page
192
Keywords
Adaptive Exponential integrated and fire model (AdEx), FPGA, High accuracy, Neuromorphic, Pipelining
Last Page
197
Abstract
It is expensive to simulate large-scale neural networks on hardware while ensuring a high resemblance to the original neurons' behavior. This paper introduces a novel technique to facilitate digital implementation and computer simulation of neuron models that contain an exponential term. This technique is applied to a biologically realistic neuron model called Adaptive Exponential integrated and fire (AdEx). Hardware synthesis and physical implementations show that the resulting model can reproduce precise neural behavior with high performance and considerably lower implementation costs compared with the original AdEx model.
DOI
10.1109/IJCNN.2016.7727198
ISBN
9781509006199
Recommended Citation
Makhlooghpour, Aliasghar; Soleimani, Hamid; Ahmadi, Arash; Zwolinski, Mark; and Saif, Mehrdad. (2016). High accuracy implementation of Adaptive Exponential integrated and fire neuron model. Proceedings of the International Joint Conference on Neural Networks, 2016-October, 192-197.
https://scholar.uwindsor.ca/electricalengpub/296