We investigate how internal representations emerge across hierarchical processing systems by introducing a neuroscience-inspired framework for analyzing deep spiking neural networks (SNN) through the lens of functional connectivity. Drawing on concepts from systems neuroscience and information theory, we form the first-order functionally-connected (1FC) group of a neuron based on its statistically significant pairwise correlations with neurons from the previous layer of a trained SNN architecture. We then track its response properties during inference under various conditions. Our analysis shows that several principles of functional connectivity previously observed in biological cortex are preserved in spiking ResNet architectures. These 1FC ensembles display interesting properties: their aggregate cofiring reliably predicts downstream neuronal responses through a robust, ReLU-like input-output relationship, whose gain scales systematically with ensemble size. Reliable encoding of the presented class emerges only during high 1FC cofiring events, which themselves occur infrequently, indicating that informative representations are concentrated in rare but highly coordinated activity patterns. Under uniform random noise or adversarial perturbations, these response profiles are disrupted, particularly in early and intermediate layers. This enables a targeted high-resolution interrogation at specific nodes and pathways. We showed that the functional connectivity structure is shaped by learning and this structure breaks under weight permutation. These establish 1FC ensembles as a functionally meaningful substrate for input encoding and information transfer, with potential implications in designing targeted fine-grained diagnostics on the information flow.

翻译：我们通过引入一种受神经科学启发的框架，从功能连接性的角度分析深度脉冲神经网络（SNN），研究了内部表征如何在层级处理系统中涌现。借鉴系统神经科学和信息论的概念，我们基于训练好的SNN架构中某一神经元与前一层的神经元之间具有统计显著性的成对相关性，构建了该神经元的一阶功能连接组。随后，我们在各种条件下追踪其在推理过程中的响应特性。我们的分析表明，先前在生物皮层中观察到的一些功能连接原则，在脉冲ResNet架构中得以保留。这些一阶功能连接集合展现出有趣的性质：其群体共发放能通过一种稳健的、类ReLU的输入-输出关系可靠地预测下游神经元响应，且其增益随集合大小呈系统性缩放。对呈现类别的可靠编码仅出现在高共发放事件期间，而此类事件本身发生频率较低，这表明信息性表征集中于罕见但高度协调的活动模式中。在均匀随机噪声或对抗性扰动下，这些响应轮廓被破坏，尤其是在早期和中间层。这使我们能够对特定节点和通路进行高分辨率定向探查。我们揭示了功能连接结构由学习塑造，并且该结构在权重置换下发生瓦解。这些发现确立了一阶功能连接集合作为输入编码和信息传递的功能上有意义的基质，并对设计针对信息流的高分辨率精细诊断方法具有潜在启示。