Spiking neural networks possess computational advantages over traditional artificial neural networks
Spiking neural networks offer significant computational and efficiency advantages, particularly in processing temporal dynamics and event-based data through spike timing and sparse representations.
The retrieved papers consistently support the claim that spiking neural networks possess distinct computational advantages, such as the ability to simulate Turing machines, efficiently process temporal data, leverage spike timing, and achieve high energy efficiency compared to traditional neural networks. None of the papers refute this claim.
Wolfgang Maass. Lower Bounds for the Computational Power of Networks of Spiking Neurons. 1996. https://doi.org/10.1162/neco.1996.8.1.1
Paper [0] proves that spiking neural networks can simulate arbitrary threshold circuits and Turing machines, demonstrating broad computational power.
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Yuanyuan Zhu, Xiang Wan, Jie Yan, Li Zhu, Run Li, Cheeleong Tan, Z. Yu, Liuyang Sun, Shanchen Yan, Yong Xu, Huabin Sun. Leaky Integrate‐and‐Fire Neuron Based on Organic Electrochemical Transistor for Spiking Neural Networks with Temporal‐Coding. 2024. https://doi.org/10.1002/aelm.202300565
Paper [3] highlights that spiking neural networks process temporal dynamics and time-varying inputs by exploiting precise spike timings.
Ziqi Yu, Pengfei Sun, Dan Goodman. Beyond rate coding: surrogate gradients enable spike timing learning in spiking neural networks. 2025. https://doi.org/10.1088/2634-4386/ae46d5
Paper [5] shows that spiking neural networks can leverage temporal structure and spike timing information to solve complex sensory processing tasks.
Adrien Fois, Bernard Girau. Enhanced representation learning with temporal coding in sparsely spiking neural networks. 2023. https://doi.org/10.3389/fncom.2023.1250908
Paper [7] demonstrates that temporal coding in spiking neural networks enhances representation learning and achieves significantly higher sparsity and efficiency.
AbdelQader AlKilany, Dan F. M. Goodman. Neuromodulation enhances the capability and efficiency of spiking neural networks. 2025. https://doi.org/10.1101/2025.07.25.666748
Paper [10] notes that spiking neural networks underlie extreme energy efficiency and achieve superior performance in sensory processing tasks with fewer spikes.
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