论文标题
进化储层计算网络中的功能差异
Functional differentiations in evolutionary reservoir computing networks
论文作者
论文摘要
我们提出了一台扩展的储层计算机，显示神经元的功能分化。储层计算机的开发是为了使用进化动力学启用内部储层，我们称其为进化储层计算机。为了开发神经元单元以显示特异性，取决于输入信息，应控制内部动力学以在扩展动态后产生收缩动力学。扩展动力学会放大输入信息的差异，同时收缩动态有助于形成输入信息群，从而产生多个吸引子。两种动态的同时出现表明存在混乱。相反，在有限时间间隔内这些动力学的顺序外观可能会引起功能区分。在本文中，我们展示了在进化储层计算机中如何产生特定的神经元单元。
We propose an extended reservoir computer that shows the functional differentiation of neurons. The reservoir computer is developed to enable changing of the internal reservoir using evolutionary dynamics, and we call it an evolutionary reservoir computer. To develop neuronal units to show specificity, depending on the input information, the internal dynamics should be controlled to produce contracting dynamics after expanding dynamics. Expanding dynamics magnifies the difference of input information, while contracting dynamics contributes to forming clusters of input information, thereby producing multiple attractors. The simultaneous appearance of both dynamics indicates the existence of chaos. In contrast, sequential appearance of these dynamics during finite time intervals may induce functional differentiations. In this paper, we show how specific neuronal units are yielded in the evolutionary reservoir computer.
