A novel memristive Hopfield neural network with application in associative memory

Memristor is a nanoscale electronic device that exhibits the synaptic characteristics in artificial neural network. Some valuable memristor-based synaptic circuits have been presented. However, the circuitry implementations of some simple neural network are still rarely involved before. This paper contributes to construct a novel memristive Hopfield neural network circuit. On one hand, an improved memristor bridge circuit is employed to realize synaptic operation which better performs zero, positive and negative synaptic weights without requiring any switches and inverters, and Pspice implementation scheme is also considered. On the other hand, the proposed bridge circuit greatly simplifies the structure of neural network, and reduces the conversion process between current and voltage signal. Furthermore, the associative memory in binary and color images is demonstrated on the basis of the proposed memristive network. A series of numerical simulations are designed to verify associative memory capability, and experimental results demonstrate the effectiveness of the proposed neural network via the cases of single-associative memory and multi-associative memory.