Scaled Hippocampus-inspired Neural Networks on Neuromorphic Memristive Hardware

2026-09-14

AI Summary

研究通过模仿海马体CA3区，开发了基于忆阻器硬件的神经网络，使用4目标函数方法缩小网络规模，并在FPGA/忆阻器平台上实现。

Abstract

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.

Problem

• hippocampus-inspired
• neuromorphic computing
• memristive hardware
• spiking neural network
• biologically-realistic

Innovation

• spiking neural network
• memristor hardware
• neuronal diversity
• resting state dynamics
• network downscaling