Context-sensitive two-point layer 5 pyramidal cells (L5PCs) were discovered as long ago as 1999. However, the potential of this discovery to provide useful neural computation has yet to be demonstrated. Here we show for the first time how a transformative L5PCs-driven deep neural network (DNN), termed the multisensory cooperative computing (MCC) architecture, can effectively process large amounts of heterogeneous real-world audio-visual (AV) data, using far less energy compared to best available 'point' neuron-driven DNNs. A novel highly-distributed parallel implementation on a Xilinx UltraScale+ MPSoC device estimates energy savings up to 245759 $ \times $ 50000 $\mu$J (i.e., 62% less than the baseline model in a semi-supervised learning setup) where a single synapse consumes $8e^{-5}\mu$J. In a supervised learning setup, the energy-saving can potentially reach up to 1250x less (per feedforward transmission) than the baseline model. The significantly reduced neural activity in MCC leads to inherently fast learning and resilience against sudden neural damage. This remarkable performance in pilot experiments demonstrates the embodied neuromorphic intelligence of our proposed cooperative L5PC that receives input from diverse neighbouring neurons as context to amplify the transmission of most salient and relevant information for onward transmission, from overwhelmingly large multimodal information utilised at the early stages of on-chip training. Our proposed approach opens new cross-disciplinary avenues for future on-chip DNN training implementations and posits a radical shift in current neuromorphic computing paradigms.

“除非并且直到我们的社会认识到网络欺凌行为，否则成千上万的沉默受害者的苦难将继续。”〜安娜·玛丽亚·查韦斯（Anna Maria Chavez）。关于网络欺凌的一系列研究无法为网络欺凌提供可靠的解决方案。在这项研究工作中，我们能够通过开发能够以92％精度检测和拦截欺凌传入和传出消息的模型来为此提供永久解决方案。我们还开发了一个聊天机器人自动化消息系统，以测试我们的模型，从而使用多项式幼稚贝叶斯（MNB）的机器学习算法和优化的线性支持向量机（SVM）开发人工智能供电的反周期欺凌系统。我们的模型能够检测并拦截欺凌和传入的欺凌信息并立即采取行动。