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Open research questions in Advanced Memory and Neural Computing

27 unresolved questions extracted from the limitations and future-work sections of 298 Advanced Memory and Neural Computing papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Critical synaptic functions of the honey-CNT memristor, including spike-rate-dependent plasticity, spike voltage dependent plasticity, learn-forget-relearn, and supralinear spatial summation are revealed, which have not been reported by honey based memristive devices before.

    Honey-CNT memristive artificial synaptic device for sustainable neuromorphic computing system · 2026 · DOI

  • Future research could focus on optimizing training algorithms to improve convergence speed and accuracy while maintaining low power consumption. , et al (2019) Advances and open problems in federated learning, arXiv.

    Federated training of spiking neural networks on edge hardware for audio processing · 2026 · DOI

  • https://doi.org/10.1007/s40820-026-02191-y e-ISSN 2150-5551 CN 31-2103/TB In‑Sensor‑Memory Computing for Post‑Von Neumann Intelligence: A Perspective Hongyu Tang1,2 *, Ninghai Yu1, Pengsheng Min1, Ruiqian Guo1 *, Guoqi…

    In-Sensor-Memory Computing for Post-Von Neumann Intelligence: A Perspective · 2026 · DOI

  • Zero-power optoelectronic synapses mark a key inflection point in the evolution of neuromorphic electronics, providing a foundation for devices that perceive, learn, and adapt using light as their sole energy source to minimize power consumption during system operation. Recent advances have demonstrated that built-in potential and PTE effect can drive synaptic functions without external electrical bias, highlighting the potential for unprecedented energy efficiency. However, realizing fully autonomous, deformable, and intelligent systems requires overcoming multiple challenges, including photon management, bidirectional learning, operational stability, mechanical adaptability, and scalable system-level integration. Addressing these challenges demands cross-disciplinary efforts that connect materials chemistry, device physics, and circuit and architectural design, rather than focusing on isolated device improvements. To Na et al. Soft Sci. 2026, 6, 25 Page 17 of 22 address limited light utilization, future research should focus on enhancing light-matter interactions through advanced photon-management strategies, including high absorption photoactive materials and vertical structure. Insufficient adaptive learning capability may be overcome by designing wavelength-selective optoelectronic synapses, particularly through heterojunction engineering that enables controllable bidirectional charge carrier transport and reversible weight modulation without external electrical bias. Device instability and variability can be addressed by an encapsulation process using hydrophobic transparent materials to prevent environmental defects and by achieving a precise and uniform film morphology. Achieving system-level integration requires extending device-level advances toward array-based architectures, incorporating optical waveguides, passive optical interconnects, and wavelength-division to suppress crosstalk and enable scalable in-sensor computing. Developing intrinsically soft electronic materials and mechanically reliable interfaces will be as crucial as designing architectures that preserve neuromorphic fidelity under dynamically changing environmental and physiological conditions. Furthermore, integrating optical interconnects and scalable fabrication strategies will be essential for translating device-level concepts into practical systems capable of hardware-based image recognition and vector-matrix computation. In the end, the convergence of zero-power operation, mechanical compliance, and neuromorphic intelligence will define the next phase of wearable technology. Future systems are expected to evolve beyond discrete sensing modules into fully integrated, adaptive networks capable of continuous perception, energy regulation, and physiological interpretation within body-interfaced environments. By autonomously managing energy flow and decoding multimodal biosignals, these platforms could function as intelligent companions for long-term health monitoring and responsive therapeutic control. Ultimately, this shift toward zero-power, soft, and cognitively capable electronics will not only advance wearable systems but also redefine how human-machine interfaces enable sustainable and lifelong interaction with the body, environment, and technology. DECLARATIONS Authors’ contributions Conceived the topic: Na, M.; Park, J.; Sim, K. Original draft writing: Na, M.; Park, J.; Sim, K. Supervised and reviewed the manuscript: Sim, K.

    Recent advances in zero-power optoelectronic synapses with potential for wearable neuromorphic platforms · 2026 · DOI

  • The switching rate of memristive devices remains slow relative to silicon-based transistors, which may render the architecture inappropriate for AI use cases with very high processing needs, including high-definition video streaming or real-time data analytics in large-scale cloud environments.

    Neuromorphic Computing Architectures Using Memristive Devices for Energy-Efficient Artificial Intelligence · 2026 · DOI

  • Abstract Wearable near/in‐sensor neuromorphic computing is driving next‐generation human‐artificial intelligence (AI) interface, the Internet of Things, and intelligent robots, with reservoir computing (RC) playing a pivotal role in advancing AI hardware, yet its potential remains underexplored.

    All‐Polymer Organic Electrochemical Synaptic Transistor With Controlled Ionic Dynamics for High‐Performance Wearable and Sustainable Reservoir Computing · 2024 · DOI

  • However, effective learning algorithms for spiking networks remain elusive, although it is suspected that effective plasticity mechanisms could alleviate the problem of data efficiency.

    Spark: modular spiking neural networks · 2026 · DOI

  • The construction of a co-designed hardware-software computing architecture necessitates coordinated progress spanning the controllable preparation and process optimization of high-quality 2D materials, innovations in device design, and advances in high-density heterogeneous integration.

    Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient Neuromorphic Application · 2026 · DOI

  • The integrated neuromorphic hardware systems require careful consideration across multiple domains, including dynamic range matching, timing alignment, system-level power consumption, and signal crosstalk.

    Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient Neuromorphic Application · 2026 · DOI

  • The optimization of reconfigurable devices requires setting precise switching thresholds for different modes and employing multi-port/multi-dimensional heterostructures.

    Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient Neuromorphic Application · 2026 · DOI

  • The physical switching mechanisms of functional materials are often constrained by their intrinsic kinetic processes, leading to undesirable operational delays.

    Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient Neuromorphic Application · 2026 · DOI

  • The correlation mechanisms between material properties and desired neuronal functionalities lack systematic investigation; further screening of 2D materials with intrinsic threshold characteristics and fast dynamics can be achieved by integrating theoretical calculations.

    Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient Neuromorphic Application · 2026 · DOI

  • Research on synaptic devices has now established a relatively diverse materials and regulatory strategies, but the development of neuronal devices still lags in terms of performance and 2D material exploration.

    Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient Neuromorphic Application · 2026 · DOI

  • Increased resistance drift occurs due to integration of larger memristive crossbar arrays to represent larger networks, which affects system performance and stability in the long run.

    Neuromorphic Computing Architectures Using Memristive Devices for Energy-Efficient Artificial Intelligence · 2026 · DOI

  • Device variability in memristive systems is a major limitation, as manufacturing processes are very sensitive and can cause changes in resistance and discrepancies in storing and updating weights, leading to decreased reliability particularly when scaling to more sophisticated neural networks.

    Neuromorphic Computing Architectures Using Memristive Devices for Energy-Efficient Artificial Intelligence · 2026 · DOI

  • A fruitful direction for future research would be to compare the performance of iTDS to related approaches (including Bayesian learning, predictive alignment, slow feature encoding, attention mechanisms, Deep Boltzmann machines, and Helmholtz machines) using standardized dataset benchmarks.

    Intrinsic stabilization of synaptic plasticity improves learning and robustness in artificial neural networks · 2026 · DOI

  • Slow feature encoding presents a potential trade-off between learning and stability where smaller temporal decay is associated with increased training accuracy but may result in synaptic instability.

    Intrinsic stabilization of synaptic plasticity improves learning and robustness in artificial neural networks · 2026 · DOI

  • Predictive alignment requires that weights within the reservoir are trained using a separate plasticity rule, providing no explicit trade-off between bottom-up and top-down learning.

    Intrinsic stabilization of synaptic plasticity improves learning and robustness in artificial neural networks · 2026 · DOI

  • However, the state-of-the-art all-in-one array integration technologies with simultaneous broadband spectrum image capture (sensory), image memory (storage) and image processing (computation) functions are still insufficient.

    Non-volatile rippled-assisted optoelectronic array for all-day motion detection and recognition · 2024 · DOI

  • Abstract The crossmodal interaction of different senses, which is an important basis for learning and memory in the human brain, is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception, but related researches are scarce.

    Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual-Olfactory Crossmodal Perception · 2024 · DOI

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27 open questions have been extracted from the limitations and future-work passages of 298 Advanced Memory and Neural Computing papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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