A Multi-Loop Neuromodulation Chipset Network with Frequency-Interleaving Front-End and Explainable AI for Memory Studies in Freely Behaving Monkeys

📋 论文概要

本文提出一种用于记忆研究的多环路神经调节芯片组网络，采用频率交叉前端和可解释AI，旨在通过电刺激海马体增强记忆来治疗阿尔茨海默病。该芯片组网络实现了多通道协调刺激与实时神经信号处理，为脑机接口和神经疾病治疗提供了新方案。

💡 主要创新点

• 提出频率交叉前端架构，支持多频段神经信号同时采集和刺激，提高干扰抑制和信号保真度。
• 集成可解释AI模块，实时分析神经活动模式并优化刺激参数，增强治疗可解释性和适应性。
• 构建多芯片网络协同工作，实现大规模海马体区域覆盖与同步刺激，提升记忆调制效果。

🏷 关键词

📄 原文摘要

of dementia, affects over 30 million people worldwide and accounts for more than 1% of the global GDP [1]. Given that age is a significant risk factor, the number of AD patients is projected to double in the next two decades. While there is currently no cure for AD, increasing evidence suggests that electrical brain stimulation is a potential treatment [2]. The hippocampus (HC) is a critical brain region that exhibits specific rhythmic neural activities during memory encoding and consolidation. In-phase stimulation has the potential to entrain and amplify these rhythms, thereby enhancing memory formation and retrieval. Consequently, innovative stimulation protocols are being developed to treat AD by modulating the HC and its associated brain regions.

👥 作者与机构

Yuhan Hou1, Yi Zhu1, Xiao Wu1, Yinfei Li1, Timothy Lucas2, Andrew Richardson3, Xilin Liu1

University of Toronto, Toronto, Canada Ohio State University, Columbus, OH 3 University of Pennsylvania, Philadelphia, PA 1 2 Alzheimer’s disease (AD), a common cause