A full-function memristive associative memory neural network circuit based on multi-frequency SRDP rule.

Abstract

Previous studies on memristive associative neural network circuits have not sufficiently incorporated the effects of spike-rate-dependent plasticity (SRDP) together with long-term potentiation (LTP) and long-term depression (LTD) mechanisms. To address this limitation, based on the SRDP rule, this paper presents the design of a full-function memristive associative memory neural network circuit. The circuit adopts an improved leaky integrate-and-fire (LIF) neuron to generate graded pulse signals, which are subsequently classified into three frequency ranges by a frequency recognition module. Based on the identified frequency, the long-term potentiation or depression module is activated to execute SRDP operations: High-frequency signals induce neurons to produce LTP, which increases the rate of learning and decreases the rate of forgetting; Medium-frequency signals induce neurons to produce LTD, which decreases the rate of learning and increases the rate of forgetting; Low-frequency signals do not affect the rate of change of synaptic weights in associative memory. The updated synaptic weights are then routed to an associative memory module, where the circuit implements learning and forgetting (ring-only and food-only patterns). The circuit further incorporates a consolidation learning module to counteract natural forgetting and stabilize synaptic weights for long-term memory retention. By integrating these modules, the circuit simulates learning, forgetting, and long-term memory formation in memristive associative neural networks. The functionality and effectiveness of the circuit is verified by PSPICE simulation.