In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the proposed SNN, the number of neurons, neuron models, encoding method, and learning algorithm design are described in a correct and pellucid fashion. It is also discussed that optimizing the SNN parameters based on physiology, and maximizing the information they pass leads to a more robust network. In this paper, inspired by the “center-surround” structure of the receptive fields in the retina, and the amount of overlap that they have, a robust SNN is implemented. It is based on the Integrate-and-Fire (IF) neuron model and uses the time-to-first-spike coding to train the network by a newly proposed method. The Iris and MNIST datasets were employed to evaluate the performance of the proposed network whose accuracy, with 60 input neurons, was 96.33% on the Iris dataset. The network was trained in only 45 iterations indicating its reasonable convergence rate. For the MNIST dataset, when the gray level of each pixel was considered as input to the network, 600 input neurons were required, and the accuracy of the network was 90.5%. Next, 14 structural features were used as input. Therefore, the number of input neurons decreased to 210, and accuracy increased up to 95%, meaning that an SNN with fewer input neurons and good skill was implemented. Also, the ABIDE1 dataset is applied to the proposed SNN. Of the 184 data, 79 are used for healthy people and 105 for people with autism. One of the characteristics that can differentiate between these two classes is the entropy of the existing data. Therefore, Shannon entropy is used for feature extraction. Applying these values to the proposed SNN, an accuracy of 84.42% was achieved by only 120 iterations, which is a good result compared to the recent results.

中文翻译：

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用于模式识别的具有集成和发射神经元模型的脉冲神经网络的设计与实现

与之前的人工神经网络 (ANN) 相比，脉冲神经网络 (SNN) 基于时间编码方法工作。在所提出的 SNN 中，神经元的数量、神经元模型、编码方法和学习算法设计以正确而清晰的方式进行了描述。还讨论了基于生理学优化 SNN 参数，并最大化它们传递的信息会导致更健壮的网络。在本文中，受视网膜中感受野的“中心-环绕”结构以及它们所具有的重叠量的启发，实现了一个稳健的 SNN。它基于 Integrate-and-Fire (IF) 神经元模型，并使用 time-to-first-spike 编码通过一种新提出的方法来训练网络。Iris 和 MNIST 数据集用于评估所提出网络的性能，其在 Iris 数据集上具有 60 个输入神经元的准确度为 96.33%。该网络仅经过 45 次迭代训练，表明其合理的收敛速度。对于 MNIST 数据集，当每个像素的灰度级作为网络的输入时，需要 600 个输入神经元，网络的准确率为 90.5%。接下来，使用 14 个结构特征作为输入。因此，输入神经元的数量减少到 210 个，准确率提高了 95%，这意味着实现了一个输入神经元较少且技巧好的 SNN。此外，将 ABIDE1 数据集应用于提议的 SNN。在 184 个数据中，79 个用于健康人，105 个用于自闭症患者。可以区分这两类的特征之一是现有数据的熵。因此，香农熵用于特征提取。将这些值应用于提出的 SNN，仅通过 120 次迭代就实现了 84.42% 的准确度，与最近的结果相比，这是一个很好的结果。