Abstract

The use of memristors as modulators of synaptic connections is a promising direction in the development of neuromorphic computing systems (NCS), including those that use reinforcement learning. To implement the latter, spike-timing-dependent plasticity (STDP), depending on the time of arrival of pulses with dopamine-like modulation, can be used. Using an example of a memristor array based on a nanocomposite (Co–Fe–B)_x(LiNbO₃)_100–x the possibility of changing the conductivity of memristor devices according to the STDP rules with dopamine-like modulation is studied, and the variation in the characteristics of the array memristors from cycle to cycle (C2C) and from device to device (D2D) is assessed. It is established that the D2D variation, compared to the C2C variation, has a greater impact on the STDP window, which must be taken into account when modeling and creating neural networks capable of reinforcement learning to solve complex cognitive tasks.

中文翻译：

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(Co–Fe–B)x(LiNbO3)100–x 纳米复合忆阻器特性色散对突触可塑性类多巴胺调节的影响

摘要

使用忆阻器作为突触连接的调节器是神经形态计算系统（NCS）（包括使用强化学习的系统）发展的一个有前途的方向。为了实现后者，可以使用尖峰时序相关可塑性（STDP），该可塑性取决于具有类多巴胺调制的脉冲的到达时间。以基于纳米复合材料 (Co–Fe–B) _x (LiNbO ₃ ) _{100– x}的忆阻器阵列为例，研究了根据类多巴胺调制的 STDP 规则改变忆阻器器件电导率的可能性，并且评估了阵列忆阻器的特性在不同周期（C2C）和不同器件（D2D）之间的变化。已经确定，与 C2C 变化相比，D2D 变化对 STDP 窗口具有更大的影响，在建模和创建能够强化学习以解决复杂认知任务的神经网络时必须考虑到这一点。