Working memory enables the brain to integrate transient information for rapid decision-making. Artificial networks typically replicate this via recurrent or parallel architectures, yet incur high energy costs and noise sensitivity. Here we report IPNet, a hardware-software co-designed neuromorphic architecture realizing human-like working memory via neuronal intrinsic plasticity. Exploiting Joule-heating dynamics of Magnetic Tunnel Junctions (MTJs), IPNet physically emulates biological memory volatility. The memory behavior of the proposed architecture shows similar trends in n-back, free recall and memory interference tasks to that of reported human subjects. Implemented exclusively with MTJ neurons, the architecture with human-like working memory achieves 99.65% accuracy on 11-class DVS gesture datasets and maintains 99.48% on a novel 22-class time-reversed benchmark, outperforming RNN, LSTM, and 2+1D CNN baselines sharing identical backbones. For autonomous driving (DDD-20), IPNet reduces steering prediction error by 14.4% compared to ResNet-LSTM. Architecturally, we identify a 'Memory-at-the-Frontier' effect where performance is maximized at the sensing interface, validating a bio-plausible near-sensor processing paradigm. Crucially, all results rely on raw parameters from fabricated devices without optimization. Hardware-in-the-loop validation confirms the system's physical realizability. Separately, energy analysis reveals a reduction in memory power of 2,874x compared to LSTMs and 90,920x versus parallel 3D-CNNs. This capacitor-free design enables a compact ~1.5um2 footprint (28 nm CMOS): a >20-fold reduction over standard LIF neurons. Ultimately, we demonstrate that instantiating human-like working memory via intrinsic neuronal plasticity endows neural networks with the dual biological advantages of superior dynamic vision processing and minimal metabolic cost.

翻译：工作记忆使大脑能够整合瞬时信息以进行快速决策。人工网络通常通过循环或并行架构来复现这一功能，但会带来高能耗和噪声敏感性问题。本文报道了IPNet——一种通过神经元内在可塑性实现类人工作记忆的软硬件协同设计神经形态架构。该架构利用磁性隧道结（MTJ）的焦耳热动力学特性，在物理层面模拟了生物记忆的易失性。所提出架构在n-back、自由回忆和记忆干扰任务中表现出的记忆行为趋势，与已报道的人类被试数据相似。该架构完全采用MTJ神经元实现，在11类DVS手势数据集上达到99.65%的准确率，并在新颖的22类时间反转基准测试中保持99.48%的准确率，优于具有相同骨干网络的RNN、LSTM和2+1D CNN基线模型。在自动驾驶任务（DDD-20）中，IPNet相较于ResNet-LSTM将转向预测误差降低了14.4%。在架构层面，我们发现了“前沿记忆”效应——在传感界面处性能达到最优，这验证了生物合理的近传感器处理范式。关键的是，所有结果均基于制造器件的原始参数，未经过优化。硬件在环验证证实了该系统的物理可实现性。能量分析显示，其记忆功耗相比LSTM降低2,874倍，相比并行3D-CNN降低90,920倍。这种无电容器设计实现了约1.5μm²（28 nm CMOS）的紧凑面积：比标准LIF神经元缩小超过20倍。最终，我们证明通过神经元内在可塑性实现类人工作记忆，赋予神经网络双重生物学优势：卓越的动态视觉处理能力和极低的代谢成本。