As neural interfaces become more advanced, there has been an increase in the volume and complexity of neural data recordings. These interfaces capture rich information about neural dynamics that call for efficient, real-time processing algorithms to spontaneously extract and interpret patterns of neural dynamics. Moreover, being able to do so in a fully unsupervised manner is critical as patterns in vast streams of neural data might not be easily identifiable by the human eye. Formal Deep Neural Networks (DNNs) have come a long way in performing pattern recognition tasks for various static and sequential pattern recognition applications. However, these networks usually require large labeled datasets for training and have high power consumption preventing their future embedding in active brain implants. An alternative aimed at addressing these issues are Spiking Neural Networks (SNNs) which are neuromorphic and use more biologically plausible neurons with evolving membrane potentials. In this context, we introduce here a frugal single-layer SNN designed for fully unsupervised identification and classification of multivariate temporal patterns in continuous data with a sequential approach. We show that, with only a handful number of neurons, this strategy is efficient to recognize highly overlapping multivariate temporal patterns, first on simulated data, and then on Mel Cepstral representations of speech sounds and finally on multichannel neural data. This approach relies on several biologically inspired plasticity rules, including Spike-timing-dependent plasticity (STDP), Short-term plasticity (STP) and intrinsic plasticity (IP). These results pave the way towards highly frugal SNNs for fully unsupervised and online-compatible learning of complex multivariate temporal patterns for future embedding in dedicated very-low power hardware.

翻译：随着神经接口技术的日益先进，神经数据记录的体量与复杂性不断提升。这些接口捕获了关于神经动力学的丰富信息，亟需高效、实时的处理算法来自发提取并解读神经动力学模式。此外，由于海量神经数据流中的模式可能难以通过人眼识别，实现完全无监督的处理至关重要。形式化的深度神经网络（DNNs）在各类静态与序列模式识别任务中已取得长足进展。然而，这些网络通常需要大量标注数据进行训练，且功耗较高，阻碍了其在未来主动式脑植入设备中的嵌入应用。为解决这些问题，脉冲神经网络（SNNs）作为一种替代方案应运而生，其采用神经形态计算，使用更具生物合理性的神经元模型，并包含动态演化的膜电位。在此背景下，本文提出一种节俭的单层SNN，专为采用序列化方法对连续数据中的多元时序模式进行完全无监督的识别与分类而设计。我们证明，仅需少量神经元，该策略便能有效识别高度重叠的多元时序模式：首先在模拟数据上验证，随后应用于语音信号的梅尔倒谱表示，最终在多通道神经数据上进行测试。该方法依赖于多种受生物启发的可塑性规则，包括脉冲时序依赖可塑性（STDP）、短期可塑性（STP）以及内在可塑性（IP）。这些结果为开发高度节俭的SNNs铺平了道路，使其能够实现完全无监督且兼容在线学习的复杂多元时序模式识别，未来有望嵌入专用的超低功耗硬件中。