In the nervous system, dendrites, branches of neurons that transmit signals between synapses and soma, play a critical role in processing functions, such as nonlinear integration of postsynaptic signals. The lack of these critical functions in artificial neural networks compromises their performance, for example in terms of flexibility, energy efficiency and the ability to handle complex tasks. Here, by developing artificial dendrites, we experimentally demonstrate a complete neural network fully integrated with synapses, dendrites and soma, implemented using scalable memristor devices. We perform a digit recognition task and simulate a multilayer network using experimentally derived device characteristics. The power consumption is more than three orders of magnitude lower than that of a central processing unit and 70 times lower than that of a typical application-specific integrated circuit chip. This network, equipped with functional dendrites, shows the potential of substantial overall performance improvement, for example by extracting critical information from a noisy background with significantly reduced power consumption and enhanced accuracy.

在神经系统中，树突是在突触和躯体之间传递信号的神经元分支，在处理功能（例如突触后信号的非线性整合）中起关键作用。人工神经网络缺乏这些关键功能会损害其性能，例如在灵活性，能效和处理复杂任务的能力方面。在这里，通过开发人工树突，我们通过实验证明了一个完整的神经网络，该网络与突触，树突和躯体完全集成，并使用可伸缩忆阻器设备实现。我们执行数字识别任务，并使用实验得出的设备特性模拟多层网络。功耗比中央处理单元低三个数量级以上，比典型的专用集成电路芯片低70倍。配备有功能性树枝状晶体的该网络显示出显着提高整体性能的潜力，例如，通过从嘈杂的背景中提取关键信息来显着降低功耗并提高准确性。