This work revisits SNN robustness from an information-theoretic perspective and reveals the pivotal role of temporal characteristics, and proposes a Temporal Mutual Information (TMI) regularizer that explicitly exploits temporal characteristics to enhance robustness.
This study revisits SNN robustness through the lens of temporal granularity, emphasizing the distinct behaviors that occur at individual time steps and proposes a Temporal Granularity Regu-larization term that constrains the TSV across all time steps, thereby improving the model’s overall robustness.
This work proposes a noisy group neuron (NGN) model, which incorporates population-level synchronous resetting and neural stochasticity as fundamental computational mechanisms, and develops the NGN method as a framework that combines the NGN model with backpropagation learning based on mean-field dynamics.
Yajie Zhai, Yanmei Kang, Meng Li et al.· 0 citations
This study identifies a critical vulnerability in SNNs on recently established bit-based codes: consistent performance degradation when temporal spike encoding orders are reversed, and measures the per-timestep class-mutual-information profile of six encodings directly and shows that the resulting concordance ordering...
N. T. Luu, Trung Duong Trung Luu, N. Pham et al.· Neuromorphic Computing and E...· 0 citations
Spiking neural networks (SNNs) have garnered significant attention in reinforcement learning tasks for their low power consumption. However, traditional spiking reinforcement learning (SRL) methods, which rely on local-connected encoding and fixed-threshold learning, struggle to capture the inter-dimensional correlatio...
Rong Xiao, Zhi-Yuan Hu, Ping He et al.· IEEE Transactions on Image P...· 0 citations
This thesis investigates stable learning and compute-resource efficiency on spiking neural networks and hybrid classical-quantum neural networks, which have become more complex architectures such as spiking neural networks and quantum neural networks.
The proposed parallel multi-compartment spiking neuron (PMSN) presents a promising solution to harness the computational advantages of detailed biological neurons, enabling high-performance and efficient temporal processing on neuromorphic computing systems.
Xinyi Chen, Jibin Wu, Chenxiang Ma et al.· IEEE Transactions on Neural...· 16 citations· ⚡4
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