论文标题
水疗中心：无监督SNN的系统平衡的随机概率调整
SPA: Stochastic Probability Adjustment for System Balance of Unsupervised SNNs
论文作者
论文摘要
尖峰神经网络（SNNS）由于其低功率硬件特征和类似脑的信号响应机制而受到广泛关注，但是当前，SNN的性能仍落后于人工神经网络（ANN）。我们构建了一个称为随机概率调整（SPA）系统的信息理论启发的系统，以减少此差距。水疗中心将SNN的突触和神经元映射到概率空间中，其中神经元和所有连接的预共生酶由群集表示。突触发射器之间不同簇之间的突触发射器的运动被建模为一个类似褐色的随机过程，其中发射机分布在不同的射击阶段具有自适应。我们尝试了广泛的现有无监督的SNN体系结构，并取得了一致的性能提高。分类精度的提高分别达到了MNIST和EMNIST数据集的1.99％和6.29％。
Spiking neural networks (SNNs) receive widespread attention because of their low-power hardware characteristic and brain-like signal response mechanism, but currently, the performance of SNNs is still behind Artificial Neural Networks (ANNs). We build an information theory-inspired system called Stochastic Probability Adjustment (SPA) system to reduce this gap. The SPA maps the synapses and neurons of SNNs into a probability space where a neuron and all connected pre-synapses are represented by a cluster. The movement of synaptic transmitter between different clusters is modeled as a Brownian-like stochastic process in which the transmitter distribution is adaptive at different firing phases. We experimented with a wide range of existing unsupervised SNN architectures and achieved consistent performance improvements. The improvements in classification accuracy have reached 1.99% and 6.29% on the MNIST and EMNIST datasets respectively.
