论文标题
受生物学上合理的学习规则和连接启发的基于无监督的基于STDP的尖峰神经网络
An Unsupervised STDP-based Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connections
论文作者
论文摘要
反向传播算法促进了深度学习的快速发展，但它依赖大量标记的数据，并且仍然与人类学习方式有很大的差距。人脑可以以自组织和无监督的方式快速学习各种概念知识，并通过协调人脑中的各种学习规则和结构来实现。依赖尖峰依赖性可塑性（STDP）是大脑中的一般学习规则，但是单独接受STDP训练的尖峰神经网络（SNN）效率低下且性能较差。在本文中，从短期突触可塑性中汲取灵感，我们设计了一种自适应突触过滤器，并引入自适应尖峰阈值作为神经元可塑性，以丰富SNN的表示能力。我们还引入了自适应的横向抑制连接，以动态调整尖峰平衡，以帮助网络学习更丰富的功能。为了加快和稳定无监督的尖峰神经网络的训练，我们设计了一个样本临时批量STDP（STB-STDP），该样本根据多个样本和时刻更新了权重。通过整合上述三种自适应机制和STB-STDP，我们的模型极大地加速了无监督的尖峰神经网络的训练，并提高了无监督的SNN在复杂任务上的性能。我们的模型实现了MNIST和FashionMnist数据集中无监督的基于STDP的SNN的最新性能。此外，我们对更复杂的CIFAR10数据集进行了测试，结果充分说明了算法的优势。我们的模型也是第一项将基于不受监督的SNN的SNN应用于CIFAR10的工作。同时，在小样本学习方案中，它将使用相同的结构远远超过受监督的ANN。
The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn. The human brain can quickly learn various conceptual knowledge in a self-organized and unsupervised manner, accomplished through coordinating various learning rules and structures in the human brain. Spike-timing-dependent plasticity (STDP) is a general learning rule in the brain, but spiking neural networks (SNNs) trained with STDP alone is inefficient and perform poorly. In this paper, taking inspiration from short-term synaptic plasticity, we design an adaptive synaptic filter and introduce the adaptive spiking threshold as the neuron plasticity to enrich the representation ability of SNNs. We also introduce an adaptive lateral inhibitory connection to adjust the spikes balance dynamically to help the network learn richer features. To speed up and stabilize the training of unsupervised spiking neural networks, we design a samples temporal batch STDP (STB-STDP), which updates weights based on multiple samples and moments. By integrating the above three adaptive mechanisms and STB-STDP, our model greatly accelerates the training of unsupervised spiking neural networks and improves the performance of unsupervised SNNs on complex tasks. Our model achieves the current state-of-the-art performance of unsupervised STDP-based SNNs in the MNIST and FashionMNIST datasets. Further, we tested on the more complex CIFAR10 dataset, and the results fully illustrate the superiority of our algorithm. Our model is also the first work to apply unsupervised STDP-based SNNs to CIFAR10. At the same time, in the small-sample learning scenario, it will far exceed the supervised ANN using the same structure.
