@inproceedings{9343993ac63d4395a809450f7cfbdf2b,
title = "Noisy softplus: A biology inspired activation function",
abstract = "The Spiking Neural Network (SNN) has not achieved the recognition/classification performance of its non-spiking competitor, the Artificial Neural Network(ANN), particularly when used in deep neural networks. The mapping of a well-trained ANN to an SNN is a hot topic in this field, especially using spiking neurons with biological characteristics. This paper proposes a new biologically-inspired activation function, Noisy Softplus, which is well-matched to the response function of LIF (Leaky Integrate-and-Fire) neurons. A convolutional network (ConvNet) was trained on the MNIST database with Noisy Softplus units and converted to an SNN while maintaining a close classification accuracy. This result demonstrates the equivalent recognition capability of the more biologically-realistic SNNs and bring biological features to the activation units in ANNs.",
keywords = "Activation function, Biologically-inspired, LIF neurons, Noisy softplus, Spiking neural network",
author = "Qian Liu and Steve Furber",
year = "2016",
doi = "10.1007/978-3-319-46681-1\_49",
language = "English",
isbn = "9783319466804",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "405--412",
booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",
address = "United States",
note = "23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",
}