Artificial neural networks and computational neuroscience models have made tremendous progress, allowing computers to achieve impressive results in artificial intelligence (AI) applications, such as image recognition, natural language processing, or autonomous driving. Despite this remarkable progress, biological neural systems consume orders of magnitude less energy than today's artificial neural networks and are much more agile and adaptive. This efficiency and adaptivity gap is partially explained by the computing substrate of biological neural processing systems that is fundamentally different from the way today's computers are built. Biological systems use in-memory computing elements operating in a massively parallel way rather than time-multiplexed computing units that are reused in a sequential fashion. Moreover, activity of biological neurons follows continuous-time dynamics in real, physical time, instead of operating on discrete temporal cycles abstracted away from real-time. Here, we present neuromorphic processing devices that emulate the biological style of processing by using parallel instances of mixed-signal analog/digital circuits that operate in real time. We argue that this approach brings significant advantages in efficiency of computation. We show examples of embodied neuromorphic agents that use such devices to interact with the environment and exhibit autonomous learning.

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空间和时间对神经形态代理信号处理的重要性：开发与环境交互的低功耗自主代理的挑战

人工神经网络和计算神经科学模型取得了巨大进步，使计算机在人工智能 (AI) 应用中取得了令人瞩目的成果，例如图像识别、自然语言处理或自动驾驶。尽管取得了这一显着进步，但生物神经系统消耗的能量比当今的人工神经网络少几个数量级，并且更加灵活和适应性强。这种效率和适应性差距的部分原因是生物神经处理系统的计算基础与当今计算机的构建方式有着根本的不同。生物系统使用以大规模并行方式运行的内存计算元素，而不是以顺序方式重用的时间复用计算单元。而且，生物神经元的活动遵循真实物理时间中的连续时间动态，而不是在从实时抽象出来的离散时间周期上运行。在这里，我们展示了神经形态处理设备，该设备通过使用实时运行的混合信号模拟/数字电路的并行实例来模拟处理的生物风格。我们认为这种方法在计算效率方面具有显着优势。我们展示了使用此类设备与环境交互并展示自主学习的具身神经形态代理的示例。我们展示了神经形态处理设备，通过使用实时运行的混合信号模拟/数字电路的并行实例来模拟生物处理方式。我们认为这种方法在计算效率方面具有显着优势。我们展示了使用此类设备与环境交互并展示自主学习的具身神经形态代理的示例。我们展示了神经形态处理设备，通过使用实时运行的混合信号模拟/数字电路的并行实例来模拟生物处理方式。我们认为这种方法在计算效率方面具有显着优势。我们展示了使用此类设备与环境交互并展示自主学习的具身神经形态代理的示例。