Spiking Neural Network-Based Bidirectional Associative Learning Circuit for Efficient Multibit Pattern Recall in Neuromorphic Systems

Abstract

Associative learning is a fundamental neural mechanism in human memory and cognition. It has attracted considerable attention in neuromorphic system design owing to its multimodal integration, fault tolerance, and energy efficiency. However, prior studies mostly focused on single inputs, with limited attention to multibit pairs or recall under non-orthogonal input patterns. To address these issues, this study proposes a bidirectional associative learning system using paired multibit inputs. It employs a synapse-neuron structure based on spiking neural networks (SNNs) that emulate biological learning, with simple circuits supporting synaptic operations and pattern evaluation. Importantly, the update and read functions were designed by drawing inspiration from the operational characteristics of emerging synaptic devices, thereby ensuring future compatibility with device-level implementations. The proposed system was verified through Cadence-based simulations using CMOS neurons and Verilog-A synapses. The results show that all patterns are reliably recalled under intact synaptic conditions, and most patterns are still robustly recalled under biologically plausible conditions such as partial synapse loss or noisy initial synaptic weight states. Moreover, by avoiding massive data converters and relying only on basic digital gates, the proposed design achieves associative learning with a simple structure. This provides an advantage for future extension to large-scale arrays.