Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.

听觉皮层用于表征复杂听觉刺激（如自然口语词汇）的编码本质，迄今仍存在诸多争议。本文提出，这类表征由被称为多群放电组（polychronous groups，PGs）的细胞集群内稳定的时空放电模式所编码。我们构建了一个基于生理学原理、具备局部且符合生物学真实性的脉冲时序依赖可塑性（spike-time dependent plasticity，STDP）学习机制的无监督听觉脑区脉冲神经网络模型，结果表明，该模型的可塑性皮层层所形成的PGs，相较于忽略精确脉冲时序的发放率编码，能够传递显著更多关于两种自然口语听觉刺激的说话人无关身份信息。我们进一步证实，仅当输入至模型可塑性皮层脑区的时空脉冲模式相对稳定时，这类携带有效信息的PGs才能够形成。