论文标题
用于解决多标签分类任务的最小尖峰神经元
Minimal spiking neuron for solving multi-label classification tasks
论文作者
论文摘要
多峰值速度（MST）是一个功能强大的单个尖峰神经元模型，可以解决复杂的监督分类任务。虽然功能强大，但它也具有内部复杂的评估，计算上的昂贵，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，我们介绍了一个广义神经元模型（GNM）家族，这是尖峰响应模型的特殊情况，比MST更简单，更便宜。我们发现，在广泛的参数上，GNM至少可以和MST一样学习。我们将膜电位的时间自相关确定为GNM的最重要成分，使其能够对多个时空模式进行分类。我们还将GNM解释为一种化学系统，因此在概念上通过神经网络进行了分子信息处理桥接计算。我们通过提出针对GNM的替代培训方法来结束本文，包括错误跟踪学习和错误反向传播。
The Multi-Spike Tempotron (MST) is a powerful single spiking neuron model that can solve complex supervised classification tasks. While powerful, it is also internally complex, computationally expensive to evaluate, and not suitable for neuromorphic hardware. Here we aim to understand whether it is possible to simplify the MST model, while retaining its ability to learn and to process information. To this end, we introduce a family of Generalised Neuron Models (GNM) which 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. We identify the temporal autocorrelation of the membrane potential as the single most important ingredient of the GNM which enables it to classify multiple spatio-temporal patterns. We also interpret the GNM as a chemical system, thus conceptually bridging computation by neural networks with molecular information processing. We conclude the paper by proposing alternative training approaches for the GNM including error trace learning and error backpropagation.
