油藏计算机神经程序设计语言
A Neural Programming Language for the Reservoir Computer
摘要：
从逻辑推理到心理模拟，生物和人工神经系统拥有令人难以置信的计算能力。这种神经计算机通过连续地表示数据并以原生并行和分布式方式处理信息，提供了一种全新的计算范式。为了利用这种计算，以前的工作已经开发了广泛的训练技术来理解现有的神经网络。然而，缺乏具体的和低级的神经网络编程语言，使我们无法充分利用神经计算框架。在这里，我们提供了这样一种使用水库计算的编程语言-一个简单的递归神经网络-并缩小了我们如何概念化和实现神经计算机和硅计算机之间的差距。通过将水库的内部表示和动态分解为其输入的符号基础，我们定义了一个低级神经机器代码，我们使用该代码来编程水库以求解复杂的方程并将混沌动力系统存储为随机存取存储器（dRAM）。使用这种表示，我们提供了一个完全分布式的神经实现的软件虚拟化和逻辑电路，甚至程序的一个可玩的乒乓球游戏内的水库计算机。总之，我们定义了一个具体的，实用的，完全可推广的实现神经计算。
英文摘要：
From logical reasoning to mental simulation, biological and artificial neural systems possess an incredible capacity for computation. Such neural computers offer a fundamentally novel computing paradigm by representing data continuously and processing information in a natively parallel and distributed manner. To harness this computation, prior work has developed extensive training techniques to understand existing neural networks. However, the lack of a concrete and low-level programming language for neural networks precludes us from taking full advantage of a neural computing framework. Here, we provide such a programming language using reservoir computing -- a simple recurrent neural network -- and close the gap between how we conceptualize and implement neural computers and silicon computers. By decomposing the reservoir's internal representation and dynamics into a symbolic basis of its inputs, we define a low-level neural machine code that we use to program the reservoir to solve complex equations and store chaotic dynamical systems as random access memory (dRAM). Using this representation, we provide a fully distributed neural implementation of software virtualization and logical circuits, and even program a playable game of pong inside of a reservoir computer. Taken together, we define a concrete, practical, and fully generalizable implementation of neural computation.
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