Sequence learning is a fundamental cognitive function of the brain. However, the ways in which sequential information is represented and memorized are not dealt with satisfactorily by existing models. To overcome this deficiency, this paper introduces a spiking neural network based on psychological and neurobiological findings at multiple scales. Compared with existing methods, our model has four novel features: (1) It contains several collaborative subnetworks similar to those in brain regions with different cognitive functions. The individual building blocks of the simulated areas are neural functional minicolumns composed of biologically plausible neurons. Both excitatory and inhibitory connections between neurons are modulated dynamically using a spike-timing-dependent plasticity learning rule. (2) Inspired by the mechanisms of the brain's cortical-striatal loop, a dependent timing module is constructed to encode temporal information, which is essential in sequence learning but has not been processed well by traditional algorithms. (3) Goal-based and episodic retrievals can be achieved at different time scales. (4) Musical memory is used as an application to validate the model. Experiments show that the model can store a huge amount of data on melodies and recall them with high accuracy. In addition, it can remember the entirety of a melody given only an episode or the melody played at different paces.

中文翻译：

---

基于脑启发脉冲神经网络的时序学习及其在音乐记忆中的应用

序列学习是大脑的基本认知功能。然而，现有模型并不能令人满意地处理表示和记忆顺序信息的方式。为了克服这一不足，本文引入了一种基于心理和神经生物学发现的多尺度尖峰神经网络。与现有方法相比，我们的模型有四个新颖的​​特点：（1）它包含几个协作子网络，类似于具有不同认知功能的大脑区域。模拟区域的各个构建块是由生物学上合理的神经元组成的神经功能微型柱。神经元之间的兴奋性和抑制性连接都是使用尖峰时间依赖的可塑性学习规则动态调节的。(2) 受大脑机制的启发 在皮层纹状体循环中，构建了一个依赖计时模块来编码时间信息，这在序列学习中是必不可少的，但传统算法并没有很好地处理。(3) 可以在不同的时间尺度上实现基于目标和情节的检索。(4) 音乐记忆用作验证模型的应用程序。实验表明，该模型可以存储大量关于旋律的数据，并可以高精度地回忆它们。此外，它可以记住仅给定一集或以不同速度播放的旋律的全部内容。(4) 音乐记忆用作验证模型的应用程序。实验表明，该模型可以存储大量关于旋律的数据，并可以高精度地回忆它们。此外，它可以记住仅给定一集或以不同速度播放的旋律的全部内容。(4) 音乐记忆用作验证模型的应用程序。实验表明，该模型可以存储大量关于旋律的数据，并可以高精度地回忆它们。此外，它可以记住仅给定一集或以不同速度播放的旋律的全部内容。