Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or "learning-to-learn". The resulting bio-inspired DNN is then trained to play the video game Atari Pong, a complex reinforcement learning task in a dynamic environment. Our analysis shows that the energy consumption of the DNN with multi-functional memristive synapses decreases by about two orders of magnitude as compared to a pure GPU implementation. Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications.

翻译：生物神经网络不仅包含人工神经网络中常见的长期记忆与权重乘法能力，还包括短时记忆、短时可塑性及元可塑性等更复杂功能，这些功能均集中在每个突触内。本文展示了基于SrTiO₃的忆阻纳米器件，该类器件能够固有地模拟上述所有突触功能。这些忆阻器在非细丝型、低电导状态下运行，可实现稳定且高能效的操作。在利用长、短时突触动力学并具备元学习（即“学会如何学习”）能力的仿生深度神经网络（DNN）中，它们可作为多功能硬件突触。随后，该仿生DNN被训练用于处理动态环境中的复杂强化学习任务——电子游戏“雅达利碰碰弹”。分析表明，与纯图形处理器（GPU）实现相比，采用多功能忆阻突触的DNN能耗降低约两个数量级。基于此发现，我们认为能够更精确模拟突触功能的忆阻器件不仅可拓展神经形态计算的适用性，还有望提升特定人工智能应用的性能与能效。