Tate Naoya, Miyata Yuki, Sakai Shun-Ichi, Nakamura Akihiro, Shimomura Suguru, Nishimura Takahiro, Kozuka Jun, Ogura Yusuke, Tanida Jun
Opt Express. 2022 Apr 25;30(9):14669-14676. doi: 10.1364/OE.450132.
The echo state property, which is related to the dynamics of a neural network excited by input driving signals, is one of the well-known fundamental properties of recurrent neural networks. During the echo state, the neural network reveals an internal memory function that enables it to remember past inputs. Due to the echo state property, the neural network will asymptotically update its condition from the initial condition and is expected to exhibit temporally nonlinear input/output. As a physical neural network, we fabricated a quantum-dot network that is driven by sequential optical-pulse inputs and reveals corresponding outputs, by random dispersion of quantum-dots as its components. In the network, the localized optical energy of excited quantum-dots is allowed to transfer to neighboring quantum-dots, and its stagnation time due to multi-step transfers corresponds to the hold time of the echo state of the network. From the experimental results of photon counting of the fluorescence outputs, we observed nonlinear optical input/output of the quantum-dot network due to its echo state property. Its nonlinearity was quantitatively verified by a correlation analysis. As a result, the relation between the nonlinear input/outputs and the individual compositions of the quantum-dot network was clarified.
回声状态特性与由输入驱动信号激发的神经网络动力学相关，是循环神经网络著名的基本特性之一。在回声状态期间，神经网络展现出一种内部记忆功能，使其能够记住过去的输入。由于回声状态特性，神经网络将从初始状态渐近地更新其状态，并有望展现出时间上的非线性输入/输出。作为一种物理神经网络，我们构建了一个量子点网络，该网络由顺序光脉冲输入驱动，并通过作为其组件的量子点的随机分散呈现出相应的输出。在该网络中，受激量子点的局域光能被允许转移到相邻量子点，并且由于多步转移导致的其停滞时间对应于网络回声状态的保持时间。从荧光输出的光子计数实验结果来看，我们观察到量子点网络因其回声状态特性而呈现出非线性光学输入/输出。通过相关性分析对其非线性进行了定量验证。结果，明确了量子点网络的非线性输入/输出与各个组成部分之间的关系。
