Publication: Temporal Pattern Coding in Deep Spiking Neural Networks
Temporal Pattern Coding in Deep Spiking Neural Networks
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Rueckauer, B., & Liu, S.-C. (2021, July 18). Temporal Pattern Coding in Deep Spiking Neural Networks. 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen. https://doi.org/10.1109/ijcnn52387.2021.9533837
Abstract
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Deep Artificial Neural Networks (ANNs) employ a simplified analog neuron model that mimics the rate transfer function of integrate-and-fire neurons. In Spiking Neural Networks (SNNs), the predominant information transmission method is based on rate codes. This code is inefficient from a hardware perspective because the number of transmitted spikes is proportional to the encoded analog value. Alternate codes such as temporal codes that are based on single spikes are difficult to scale up for large networks due to their sensitivity to s
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- Rueckauer, Bodo
- Liu, Shih-Chii
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Rueckauer, B., & Liu, S.-C. (2021, July 18). Temporal Pattern Coding in Deep Spiking Neural Networks. 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen. https://doi.org/10.1109/ijcnn52387.2021.9533837
