We develop a theory of percolation with plasticity media (PWPs) rendering properties of interest for neuromorphic computing. Unlike the standard percolation, they have multiple ( N 1) interfaces and exponentially large number ( N !) of conductive pathways between them. These pathways consist of non-ohmic random resistors that can undergo bias induced nonvolatile modifications (plasticity). The neuromorphic properties of PWPs include: multi-valued memory, high dimensionality and nonlinearity capable of transforming input data into spatiotemporal patterns, tunably fading memory ensuring outputs that depend more on recent inputs, and no need for massive interconnects. A few conceptual examples of functionality here are random number generation, matrix-vector multiplication, and associative memory. Understanding PWP topology, statistics, and operations opens a field of its own calling upon further theoretical and experimental insights.

中文翻译：

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神经形态系统的可塑性渗透

我们开发了一种具有可塑性介质（PWP）的渗滤理论，使神经形态计算感兴趣。与标准渗滤不同，它们具有多个（N1）接口和它们之间的导电通路数量成倍增加（N！）。这些路径由非欧姆随机电阻组成，这些电阻会受到偏置引起的非易失性变化（可塑性）的影响。PWP的神经形态特性包括：多值内存，高维度和非线性功能，能够将输入数据转换为时空模式，内存逐渐衰减，从而确保输出更多地依赖于最新输入，并且不需要大规模互连。这里的功能的一些概念性示例是随机数生成，矩阵向量乘法和关联存储器。了解PWP拓扑，统计数据和操作会打开自己的领域，需要进一步的理论和实验见解。