The multispike tempotron (MST) is a powersul, single spiking neuron model that can solve complex supervised classification tasks. It is also internally complex, computationally expensive to evaluate, and unsuitable for neuromorphic hardware. Here we aim to understand whether it is possible to simplify the MST model while retaining its ability to learn and process information. To this end, we introduce a family of generalized neuron models (GNMs) that are a special case of the spike response model and much simpler and cheaper to simulate than the MST. We find that over a wide range of parameters, the GNM can learn at least as well as the MST does. We identify the temporal autocorrelation of the membrane potential as the most important ingredient of the GNM that enables it to classify multiple spatiotemporal patterns. We also interpret the GNM as a chemical system, thus conceptually bridging computation by neural networks with molecular information processing. We conclude the letter by proposing alternative training approaches for the GNM, including error trace learning and error backpropagation.

中文翻译：

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用于解决多标签分类任务的最小尖峰神经元

Multispike tempotron (MST) 是一种强大的单脉冲神经元模型，可以解决复杂的监督分类任务。它内部也很复杂，评估的计算成本很高，并且不适合神经形态硬件。在这里，我们旨在了解是否可以简化 MST 模型，同时保留其学习和处理信息的能力。为此，我们引入了一系列广义神经元模型 (GNM)，它们是尖峰响应模型的一个特例，比 MST 更简单、更便宜。我们发现，在广泛的参数范围内，GNM 至少可以像 MST 一样学习。我们将膜电位的时间自相关确定为 GNM 最重要的成分，使其能够对多个时空模式进行分类。我们还将 GNM 解释为一个化学系统，从而在概念上将神经网络的计算与分子信息处理联系起来。我们通过提出 GNM 的替代训练方法来结束这封信，包括错误跟踪学习和错误反向传播。