Despite significant advancements in point cloud analysis, reducing energy consumption and improving robustness remain understudied, largely due to the inherent limitations of Convolutional Neural Networks (CNNs). To address this issue, we draw inspiration from the primary visual cortex and propose a Dendritic-Connected Continuous-Coupled Neural Network (DC-CCNN), a novel Brain-Inspired Neural Network (BINN) architecture for point cloud analysis. By combining discrete and continuous encoding, our design replaces traditional Multilayer Perceptrons (MLPs) with more efficient and robust BINNs. Building upon this framework, we further propose an extended model, DC-CCNN++, to improve robustness under complex corruption conditions. Specifically, we introduce a Neuro-Inspired Robust Modulation-and-Readout Module (NRMR) to enhance feature stability and decision robustness through global-context gain modulation and dual-code evidence integration. We also design a Cortically Inspired Progressive Variability Training (CPVT) strategy, which progressively exposes the model to structured environmental variability while preserving stable clean-sample anchors during training. Experimental results show that DC-CCNN++ improves the performance of brain-inspired networks on point cloud analysis while maintaining performance comparable to state-of-the-art methods. Compared with the original DC-CCNN, it achieves stronger results on both classification and part segmentation, and exhibits enhanced robustness against sparsity, occlusion, Gaussian noise, salt-and-pepper noise, and spatial transformations. With its efficiency, robustness, and biologically grounded design, DC-CCNN++ provides a promising alternative to traditional deep learning methods for point cloud analysis. Code is available at https://anonymous.4open.science/r/DC-CCNNpp-44E3.

翻译：尽管点云分析取得了显著进展，但降低能耗和提高鲁棒性仍是研究不足的领域，这主要源于卷积神经网络（CNN）的固有局限性。为解决这一问题，我们从初级视皮层中汲取灵感，提出了一种新型脑启发神经网络（BINN）架构——树突连接连续耦合神经网络（DC-CCNN），用于点云分析。通过融合离散编码与连续编码，我们的设计用更高效、更鲁棒的BINN替代了传统的多层感知机（MLP）。在此框架基础上，我们进一步提出了扩展模型DC-CCNN++，以提高在复杂污染条件下的鲁棒性。具体而言，我们引入了一种神经启发的鲁棒调制与读出模块（NRMR），通过全局上下文增益调制和双码证据整合来增强特征稳定性与决策鲁棒性。同时，我们设计了一种皮层启发的渐进式变异性训练策略（CPVT），该策略在训练过程中逐步让模型接触结构化的环境变异性，同时保留稳定的干净样本锚点。实验结果表明，DC-CCNN++在提升脑启发网络点云分析性能的同时，保持了与现有最优方法相当的性能。与原始DC-CCNN相比，它在分类和部件分割任务上均取得了更强结果，并在稀疏性、遮挡、高斯噪声、椒盐噪声及空间变换下展现出更强的鲁棒性。凭借其高效性、鲁棒性及生物启发的设计，DC-CCNN++为点云分析提供了传统深度学习方法的一种有前景的替代方案。代码可在 https://anonymous.4open.science/r/DC-CCNNpp-44E3 获取。