In this article, we design a memristive competitive neural network circuit based on the winner-take-all (WTA) mechanism and the online Hebbian learning rule. Each synapse of the network contains two memristors whose terminals of signal inputs are opposite. However, only one memristor participates in the calculation each time, and that one is determined by the original input signal. The competitive neural network circuit includes two parts: forward calculation and weight update. In this article, the forward calculation part of the circuit is designed based on the WTA mechanism. The combination of the leaky-integrate-and-fire (LIF) model and pMOS realizes the lateral inhibition of neurons. The design of the weight updating part is based on Hebbian learning rules. In each cycle, only synaptic memristors connected to the winner output neuron in forward calculation can be adjusted. The voltage used for synaptic memristor adjustment comes from the membrane voltage of the winner output neuron. The whole neural network circuit does not need the participation of a central processing unit (CPU) or a field-programmable gate array (FPGA) and really realizes parallel calculation, the saving of area, power consumption, and a certain extent computing-in-memory. Based on the circuit designed in PSPICE, we simulated the classification of 5×35\times3 pixel pictures. The changing trend of weights in the training phase and the high recognition accuracy in the recognition phase prove that the network can learn and recognize different patterns. The competitive neural network can be applied to the neuromorphic system of visual pattern recognition.

中文翻译：

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基于赢者通吃机制和在线赫布学习规则的竞争神经网络电路


在本文中，我们设计了一种基于赢者通吃（WTA）机制和在线赫布学习规则的忆阻竞争神经网络电路。该网络的每个突触包含两个忆阻器，其信号输入端子相反。但每次只有一个忆阻器参与计算，并且该忆阻器由原始输入信号决定。竞争神经网络电路包括前向计算和权重更新两部分。本文基于WTA机制设计了电路的前向计算部分。 LIF模型与pMOS的结合实现了神经元的侧向抑制。权重更新部分的设计基于Hebbian学习规则。在每个周期中，只有连接到前向计算中获胜输出神经元的突触忆阻器可以被调整。用于突触忆阻器调节的电压来自获胜者输出神经元的膜电压。整个神经网络电路不需要中央处理器（CPU）或现场可编程门阵列（FPGA）的参与，真正实现了并行计算，节省了面积、功耗，并在一定程度上实现了计算内嵌。记忆。基于PSPICE中设计的电路，对5×35×3像素图片的分类进行了仿真。训练阶段权重的变化趋势和识别阶段的高识别精度证明网络可以学习和识别不同的模式。竞争神经网络可以应用于视觉模式识别的神经形态系统。