Toward Building Human-Like Sequential Memory Using Brain-Inspired Spiking Neural Models

Malu Zhang, Xiaoling Luo, Jibin Wu*, Ammar Belatreche, Siqi Cai*, Yang Yang, Haizou Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Downloads (Pure)

Abstract

The brain is able to acquire and store memories of everyday experiences in real-time. It can also selectively forget information to facilitate memory updating. However, our understanding of the underlying mechanisms and coordination of these processes within the brain remains limited. This study introduces a brain-inspired spiking neural model that integrates the learning and forgetting processes of sequential memory. The proposed model closely mimics the distributed and sparse temporal coding observed in the biological neural system. It employs one-shot online learning for memory formation and uses biologically plausible mechanisms of neural oscillation and phase precession to retrieve memorized sequences reliably. Additionally, an active forgetting mechanism is integrated into the spiking neural model, enabling memory removal, flexibility, and updating. The proposed memory model not only enhances our understanding of human memory processes, but also provides a
robust framework for addressing temporal modeling tasks.
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Early online date14 Mar 2025
DOIs
Publication statusE-pub ahead of print - 14 Mar 2025

Keywords

  • Dendritic spiking neuron
  • neural mini-column
  • sequential memory
  • spiking neural networks

Cite this