Human-like Working Memory from Artificial Intrinsic Plasticity Neurons

• URL: http://arxiv.org/abs/2512.15829v1
• Date: Wed, 17 Dec 2025 17:24:37 GMT
• Title: Human-like Working Memory from Artificial Intrinsic Plasticity Neurons
• Authors: Jingli Liu, Huannan Zheng, Bohao Zou, Kezhou Yang,
• Abstract summary: IPNet is a neuromorphic architecture realizing human-like working memory via neuronal intrinsic plasticity.<n>For autonomous driving, IPNet reduces steering prediction error by 14.4% compared to ResNet-LSTM.
• Score: 0.03110995905282904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 14.4% 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.

Related papers

• Algorithm-hardware co-design of neuromorphic networks with dual memory pathways [8.49692039836696]
  Spiking neural networks excel at event-driven sensing.<n>Maintaining task-relevant context over long timescales remains a core challenge in the field.<n>We address this challenge through novel algorithm- hardware co-design effort.
  arXiv  Detail & Related papers  (2025-12-08T14:50:26Z)
• Computing with Canonical Microcircuits [0.0]
  We present a computational architecture based on canonical microcircuits (CMCs)<n>We implement these circuits as neural ODEs comprising spiny stellate, inhibitory, and pyramidal neurons.<n>Experiments show that even a single CMC node achieves 97.8 percent accuracy on MNIST.
  arXiv  Detail & Related papers  (2025-07-25T11:10:13Z)
• Topology Optimization of Random Memristors for Input-Aware Dynamic SNN [44.38472635536787]
  We introduce pruning optimization for input-aware dynamic memristive spiking neural network (PRIME) Signal representation-wise, PRIME employs leaky integrate-and-fire neurons to emulate the brain's inherent spiking mechanism. For reconfigurability, inspired by the brain's dynamic adjustment of computational depth, PRIME employs an input-aware dynamic early stop policy.
  arXiv  Detail & Related papers  (2024-07-26T09:35:02Z)
• Dynamic neural network with memristive CIM and CAM for 2D and 3D vision [57.6208980140268]
  We propose a semantic memory-based dynamic neural network (DNN) using memristor. The network associates incoming data with the past experience stored as semantic vectors. We validate our co-designs, using a 40nm memristor macro, on ResNet and PointNet++ for classifying images and 3D points from the MNIST and ModelNet datasets.
  arXiv  Detail & Related papers  (2024-07-12T04:55:57Z)
• Efficient and accurate neural field reconstruction using resistive memory [52.68088466453264]
  Traditional signal reconstruction methods on digital computers face both software and hardware challenges. We propose a systematic approach with software-hardware co-optimizations for signal reconstruction from sparse inputs. This work advances the AI-driven signal restoration technology and paves the way for future efficient and robust medical AI and 3D vision applications.
  arXiv  Detail & Related papers  (2024-04-15T09:33:09Z)
• Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model [55.116403765330084]
  Current AIGC methods, such as score-based diffusion, are still deficient in terms of rapidity and efficiency. We propose a time-continuous and analog in-memory neural differential equation solver for score-based diffusion. We experimentally validate our solution with 180 nm resistive memory in-memory computing macros.
  arXiv  Detail & Related papers  (2024-04-08T16:34:35Z)
• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• Pruning random resistive memory for optimizing analogue AI [54.21621702814583]
  AI models present unprecedented challenges to energy consumption and environmental sustainability. One promising solution is to revisit analogue computing, a technique that predates digital computing. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning.
  arXiv  Detail & Related papers  (2023-11-13T08:59:01Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.