缩放的海马启发神经网络在神经形态忆阻硬件上

中文摘要 AI 辅助

海马体是学习和记忆的关键脑区，展现出丰富的结构多样性、稀疏的通信和具有惊人能效的稳健动力学。它为新型计算能力提供了有前景的见解，特别是当与新兴硬件技术协同设计时。在这项工作中，我们从啮齿动物CA3海马亚区汲取灵感，开发了首个在忆阻器硬件上展示神经元多样性和生物现实静息态动力学的脉冲神经网络。我们提出了一种利用四管齐下的目标函数的网络缩小方法，并展示了一个小规模的CA3启发网络，包含179个Izhikevich模型神经元、3种神经元类型和17,996个突触，其静息态动力学与规模大几个数量级的全尺寸网络相似。该小规模网络通过贪心算法映射到FPGA/忆阻器平台，使用了18,316个忆阻器。得益于忆阻器噪声，硬件实现表现出连续的周期性行为，优于模拟硬件。这项工作展示了生物现实算法在新兴硬件上用于神经形态计算的潜力。

英文摘要

The hippocampus, a key brain region for learning and memory, exhibits rich structural diversity, sparse communication, and robust dynamics with incredible energy efficiency. It offers promising insights for novel computing capabilities, particularly when co-designed with emerging hardware technologies. In this work, we draw inspiration from the rodent CA3 hippocampal subregion to develop the first spiking neural network with neuronal diversity and biologically-realistic resting state dynamics demonstrated on memristor hardware. We propose a network downscaling methodology utilizing a 4-prong objective function and demonstrate a small-scale CA3-inspired network with 179 Izhikevich-modeled neurons, 3 neuronal types and 17,996 synapses with similar resting-state dynamics as the orders-of-magnitude larger full-scale network. The small-scale network is mapped to an FPGA/memristor platform using a greedy algorithm and 18,316 memristors. Benefiting from memristor noise, the hardware implementation shows continuous periodic behavior, outperforming simulated hardware. This work showcases the potential of biologically-realistic algorithms on emerging hardware for neuromorphic computing.

发表机构

• George Washington University（乔治华盛顿大学）
• George Mason University（乔治梅森大学）

机构由 AI 辅助整理，请以论文原文为准。