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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Hardware Friendly Probabilistic Spiking Neural Network With Long-Term and Short-Term Plasticity
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Hardware Friendly Probabilistic Spiking Neural Network With Long-Term and Short-Term Plasticity

机译：具有长期和短期可塑性的硬件友好型概率峰值神经网络

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摘要

This paper proposes a probabilistic spiking neural network (PSNN) with unimodal weight distribution, possessing long- and short-term plasticity. The proposed algorithm is derived by both the arithmetic gradient decent calculation and bioinspired algorithms. The algorithm is benchmarked by the Iris and Wisconsin breast cancer (WBC) data sets. The network features fast convergence speed and high accuracy. In the experiment, the PSNN took not more than 40 epochs for convergence. The average testing accuracy for Iris and WBC data is 96.7% and 97.2%, respectively. To test the usefulness of the PSNN to real world application, the PSNN was also tested with the odor data, which was collected by our self-developed electronic nose (e-nose). Compared with the algorithm (K-nearest neighbor) that has the highest classification accuracy in the e-nose for the same odor data, the classification accuracy of the PSNN is only 1.3% less but the memory requirement can be reduced at least 40%. All the experiments suggest that the PSNN is hardware friendly. First, it requires only nine-bits weight resolution for training and testing. Second, the PSNN can learn complex data sets with a little number of neurons that in turn reduce the cost of VLSI implementation. In addition, the algorithm is insensitive to synaptic noise and the parameter variation induced by the VLSI fabrication. Therefore, the algorithm can be implemented by either software or hardware, making it suitable for wider application.
机译：提出了一种具有长短期可塑性的单峰态权重分布的概率峰值神经网络（PSNN）。该算法是通过算术梯度体面计算和生物启发算法得到的。该算法以虹膜和威斯康星州乳腺癌（WBC）数据集为基准。该网络具有收敛速度快，精度高的特点。在实验中，PSNN的收敛时间不超过40个纪元。 Iris和WBC数据的平均测试准确性分别为96.7％和97.2％。为了测试PSNN在现实世界中的实用性，还使用气味数据对PSNN进行了测试，该气味数据是由我们自行开发的电子鼻（电子鼻）收集的。与相同气味数据的电子鼻中分类精度最高的算法（K近邻算法）相比，PSNN的分类精度仅低1.3％，但存储需求至少可降低40％。所有实验都表明PSNN是硬件友好的。首先，它仅需要九位的权重分辨率即可进行培训和测试。其次，PSNN可以学习带有少量神经元的复杂数据集，从而减少了VLSI实施的成本。另外，该算法对突触噪声和由VLSI制造引起的参数变化不敏感。因此，该算法可以通过软件或硬件来实现，使其适合更广泛的应用。

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