One of the modern trends in the design of human-machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the "winner takes all" principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

中文翻译：

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尖刺神经网络中的竞争性学习：迈向智能模式分类器。

人机界面（HMI）设计的现代趋势之一是将所谓的尖峰神经元网络（SNN）参与信号处理。可以通过简单高效的生物学启发算法来训练SNN。特别是，我们已经显示，SNN输入层中的感觉神经元可以同时基于尖峰频率速率和生成尖峰的潜伏期，同时对输入信号进行编码。在这种混合时间速率编码的情况下，SNN应该为两种编码实现正确的学习工作。基于此，我们研究了如何以纯速率和时间模式训练单个神经元，然后构建使用混合编码训练的通用SNN。特别是，我们在时间和速率编码问题的背景下研究SNN中的Hebbian和竞争性学习。我们表明，通过基于对和基于三重态的依赖于时序的可塑性（STDP）规则进行的Hebbian学习可用于时间编码，但不能用于速率编码。需要突触竞争诱导不良使用的突触的抑制，以确保速率编码中的神经选择性。这种竞争可以通过依赖于神经元活动的所谓的遗忘功能来实现。我们表明，基于三联体的STDP和具有遗忘功能的突触竞争的连贯使用足以进行速率编码。接下来，我们提出了一种能够使用无监督学习程序对肌电（EMG）模式进行分类的SNN。通过侧向抑制实现的神经元竞争确保了分类器神经元中“赢者通吃”的原则。SNN还根据肌肉的收缩强度提供逐渐的输出反应。此外，我们修改了SNN，以与网络输入同步地基于对目标分类器神经元的刺激来实施监督学习方法。在区分三种EMG模式的问题中，具有监督学习的SNN显示中值准确度为99.5％，与通过错误算法的反向传播学习的多层感知器所显示的结果相近。