We introduce a yat-product-powered neural network, the Neural Matter Network (NMN), a breakthrough in deep learning that achieves non-linear pattern recognition without activation functions. Our key innovation relies on the yat-product and yat-product, which naturally induces non-linearity by projecting inputs into a pseudo-metric space, eliminating the need for traditional activation functions while maintaining only a softmax layer for final class probability distribution. This approach simplifies network architecture and provides unprecedented transparency into the network's decision-making process. Our comprehensive empirical evaluation across different datasets demonstrates that NMN consistently outperforms traditional MLPs. The results challenge the assumption that separate activation functions are necessary for effective deep-learning models. The implications of this work extend beyond immediate architectural benefits, by eliminating intermediate activation functions while preserving non-linear capabilities, yat-MLP establishes a new paradigm for neural network design that combines simplicity with effectiveness. Most importantly, our approach provides unprecedented insights into the traditionally opaque "black-box" nature of neural networks, offering a clearer understanding of how these models process and classify information.

本研究解决了深度学习模型中激活函数的必要性问题，提出了基于yat积的神经网络——神经物质网络（NMN），该网络通过将输入投影到伪度量空间实现非线性模式识别，而无需传统激活函数。研究表明，NMN在多种数据集上表现优于传统多层感知器，展现了简化架构与有效性结合的新范式，并提高了对神经网络决策过程的透明度。

深度学习2.0：重要的人工神经元——拒绝相关性，拥抱正交性