Spiking neural networks (SNNs), inspired by the neural circuits of the brain,
are promising in achieving high computational efficiency with biological
fidelity. Nevertheless, it is quite difficult to optimize SNNs because the
functional roles of their modelling components remain unclear. By designing and
evaluating several variants of the classic model, we systematically investigate
the functional roles of key modelling components, leakage, reset, and
recurrence, in leaky integrate-and-fire (LIF) based SNNs. Through extensive
experiments, we demonstrate how these components influence the accuracy,
generalization, and robustness of SNNs. Specifically, we find that the leakage
plays a crucial role in balancing memory retention and robustness, the reset
mechanism is essential for uninterrupted temporal processing and computational
efficiency, and the recurrence enriches the capability to model complex
dynamics at a cost of robustness degradation. With these interesting
observations, we provide optimization suggestions for enhancing the performance
of SNNs in different scenarios. This work deepens the understanding of how SNNs
work, which offers valuable guidance for the development of more effective and
robust neuromorphic models.

通过设计和评估经典模型的几个变体，系统地研究了渗漏，重置和循环等关键模式组件在漏电集成火模型的 SNNs 中的功能角色。通过广泛的实验，我们展示了这些组件如何影响 SNNs 的准确性，泛化性能和鲁棒性。通过这些有趣的观察结果，我们提供了增强 SNNs 在不同场景下性能的优化建议，从而深化了对 SNNs 工作方式的理解，为更有效和鲁棒的神经形态模型的发展提供了有价值的指导。

理解脉冲神经网络中建模组件的功能角色

Understanding the Functional Roles of Modelling Components in Spiking  Neural Networks

We apply topological data analysis (TDA) to speech classification problems
and to the introspection of a pretrained speech model, HuBERT. To this end, we
introduce a number of topological and algebraic features derived from
Transformer attention maps and embeddings. We show that a simple linear
classifier built on top of such features outperforms a fine-tuned
classification head. In particular, we achieve an improvement of about $9\%$
accuracy and $5\%$ ERR on four common datasets; on CREMA-D, the proposed
feature set reaches a new state of the art performance with accuracy $80.155$.
We also show that topological features are able to reveal functional roles of
speech Transformer heads; e.g., we find the heads capable to distinguish
between pairs of sample sources (natural/synthetic) or voices without any
downstream fine-tuning. Our results demonstrate that TDA is a promising new
approach for speech analysis, especially for tasks that require structural
prediction. Appendices, an introduction to TDA, and other additional materials
are available here - this https URL

本论文介绍了一种利用拓扑数据分析法（TDA）分析语音的新方法，通过引入一系列从 Transformer 注意力映射和嵌入中得出的拓扑和代数特征，展示了建立在这些特征之上的简单线性分类器优于微调分类头的表现，并展示了拓扑特征能够揭示语音 Transformer 头的功能角色。

拓扑数据分析在语音处理中的应用

Topological Data Analysis for Speech Processing

There is broad agreement that Artificial Intelligence (AI) systems,
particularly those using Machine Learning (ML), should be able to "explain"
their behavior. Unfortunately, there is little agreement as to what constitutes
an "explanation." This has caused a disconnect between the explanations that
systems produce in service of explainable Artificial Intelligence (XAI) and
those explanations that users and other audiences actually need, which should
be defined by the full spectrum of functional roles, audiences, and
capabilities for explanation. In this paper, we explore the features of
explanations and how to use those features in evaluating their utility. We
focus on the requirements for explanations defined by their functional role,
the knowledge states of users who are trying to understand them, and the
availability of the information needed to generate them. Further, we discuss
the risk of XAI enabling trust in systems without establishing their
trustworthiness and define a critical next step for the field of XAI to
establish metrics to guide and ground the utility of system-generated
explanations.

本研究探讨了可解释性人工智能（XAI）中说明的特征和其效用评估，强调了说明的功能角色、用户知识状态以及生成说明所需的信息的要求，并定义了 XAI 领域的重要下一步：建立指导和基础系统生成说明效用的度量标准，以避免 XAI 可能带来的对系统的信任而没有建立其可靠性。