Kondo Y, Sawada Y
Res. Inst. of Electr. Commun., Tohoku Univ., Sendai.
IEEE Trans Neural Netw. 1992;3(3):434-43. doi: 10.1109/72.129416.
The authors have studied the information processing ability of stochastic logic neural networks, which constitute one of the pulse-coded artificial neural network families. These networks realize pseudoanalog performance with local learning rules using digital circuits, and therefore suit silicon technology. The synaptic weights and the outputs of neurons in stochastic logic are represented by stochastic pulse sequences. The limited range of the synaptic weights reduces the coding noise and suppresses the degradation of memory storage capacity. To study the effect of the coding noise on an optimization problem, the authors simulate a probabilistic Hopfield model (Gaussian machine) which has a continuous neuron output function and probabilistic behavior. A proper choice of the coding noise amplitude and scheduling improves the network's solutions of the traveling salesman problem (TSP). These results suggest that stochastic logic may be useful for implementing probabilistic dynamics as well as deterministic dynamics.
作者研究了随机逻辑神经网络的信息处理能力，该网络是脉冲编码人工神经网络家族之一。这些网络利用数字电路通过局部学习规则实现伪模拟性能，因此适合硅技术。随机逻辑中神经元的突触权重和输出由随机脉冲序列表示。突触权重的有限范围降低了编码噪声并抑制了存储容量的退化。为了研究编码噪声对优化问题的影响，作者模拟了一个具有连续神经元输出函数和概率行为的概率性霍普菲尔德模型（高斯机）。适当选择编码噪声幅度和调度方式可改善网络对旅行商问题（TSP）的求解。这些结果表明，随机逻辑对于实现概率动力学以及确定性动力学可能是有用的。
