Implementation of circuit for reconfigurable memristive chaotic neural network and its application in associative memory

Abstract

Chaotic neural networks is widely used in associative memory because of its abundant chaotic behavior. The bridge synaptic circuit of the memristor has been mostly used in artificial neural networks, because of its synapse-like and non-volatile properties, but the weight addition circuit has a complicated structure, the high power consumption and the high complexity of the network, so the associative memory neural network circuit is still less implemented. In this paper, the memory characteristics of the threshold memristor is used to build the synaptic circuit, on the one hand, when the continuous voltage is applied to the memristor to alter its memristance, it can realize continuous synaptic weights from - 1 to 1. Synaptic weight circuit has simple structure and low energy consumption, due to the configurability of the threshold memristor, and different weights can be obtained in the same circuits to achieve the function of associative memory. On the other hand, we can realize self-associative memory, hetero-associative memory, the separation of superimposed patterns, many-to-many associative memory and application in the three-view drawing, through simulation experiments. Because of the nanoscale characteristics of memristor, the hardware implementation of large-scale chaotic neural network will has simplified structure and be integrated easily.