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[2512.15829] Emergent human-like working memory from artificial neurons with intrinsic plasticity
Summary
The paper presents IPNet, a neuromorphic architecture that mimics human-like working memory through intrinsic plasticity, achieving high accuracy and energy efficiency in various tasks.
Why It Matters
This research is significant as it demonstrates a novel approach to artificial intelligence that closely replicates human cognitive functions, potentially leading to advancements in machine learning efficiency and performance, particularly in real-time applications like autonomous driving.
Key Takeaways
- IPNet achieves human-like working memory using intrinsic plasticity in artificial neurons.
- The architecture shows superior performance in dynamic vision tasks with minimal energy consumption.
- IPNet outperforms traditional models like RNNs and LSTMs in accuracy and efficiency.
- The design allows for a compact footprint, significantly reducing power requirements.
- Hardware validation confirms the practical applicability of the proposed system.
Computer Science > Emerging Technologies arXiv:2512.15829 (cs) [Submitted on 17 Dec 2025 (v1), last revised 14 Feb 2026 (this version, v2)] Title:Emergent human-like working memory from artificial neurons with intrinsic plasticity Authors:Jingli Liu, Huannan Zheng, Bohao Zou, Kezhou Yang View a PDF of the paper titled Emergent human-like working memory from artificial neurons with intrinsic plasticity, by Jingli Liu and 3 other authors View PDF Abstract: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....
