Spiking neural networks (SNNs) are bio-inspired neural networks that - to an extent - mimic the workings of our brains. In a similar fashion, event-based vision sensors try to replicate a biological eye as closely as possible. In this work, we integrate both technologies for the purpose of classifying micro-particles in the context of label-free flow cytometry. We follow up on our previous work in which we used simple logistic regression with binary labels. Although this model was able to achieve an accuracy of over 98%, our goal is to utilize the system for a wider variety of cells, some of which may have less noticeable morphological variations. Therefore, a more advanced machine learning model like the SNNs discussed here would be required. This comes with the challenge of training such networks, since they typically suffer from vanishing gradients. We effectively apply the surrogate gradient method to overcome this issue achieving over 99% classification accuracy on test data for a four-class problem. Finally, rather than treating the neural network as a black box, we explore the dynamics inside the network and make use of that to enhance its accuracy and sparsity.

中文翻译：

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在基于事件的细胞计数数据集上训练的尖峰网络中的替代梯度学习

尖峰神经网络 (SNN) 是受生物启发的神经网络，在一定程度上模仿我们大脑的运作。以类似的方式，基于事件的视觉传感器试图尽可能地复制生物眼睛。在这项工作中，我们整合了这两种技术，以便在无标记流式细胞术的背景下对微粒进行分类。我们继续之前的工作，其中我们使用带有二进制标签的简单逻辑回归。尽管该模型能够实现超过 98% 的准确度，但我们的目标是将该系统用于更广泛的细胞种类，其中一些细胞可能具有不太明显的形态变化。因此，需要更先进的机器学习模型，例如这里讨论的 SNN。这伴随着训练此类网络的挑战，因为它们通常会遇到梯度消失的问题。我们有效地应用代理梯度方法来克服这个问题，在四类问题的测试数据上实现了超过 99% 的分类准确率。最后，我们不是将神经网络视为黑匣子，而是探索网络内部的动态，并利用它来提高其准确性和稀疏性。