Open research questions in Advanced Memory and Neural Computing

What the literature leaves open

• The lack of development of computing architectures based on neuromorphic materials.

  Trigger-Based Systems as a Promising Foundation for the Development of Computing Architectures Based on Neuromorphic Materials · 2025 · DOI

• However, a circuit-level design framework linking defect dynamics with conduction behavior remains lacking.

  Modelling and Emulation of Afferent-Like Excitatory–Inhibitory Spiking Using Ovonic Threshold Devices for Neuromorphic Oscillatory Computing · 2026 · DOI

• Low-frequency negative capacitance (NC) and resistive switching (RS) are prominent dynamic phenomena in halide-perovskite memristors, yet how the slow conductance dynamics underlying RS are encoded in NC remains unclear.

  Unveiling negative capacitance as a fingerprint of delayed dynamic conductance in light-modulated halide-perovskite memristors · 2026 · DOI

• Yet, the interplay of distinct resistive switching mechanisms remains poorly understood.

  Polarity‐Controlled Coexistence of Electrochemical Metallization and Valence Change Mechanisms in a Unified Metal Halide Perovskite Memristor · 2026 · DOI

• However, the resistance switching mechanisms remain unclear as direct experimental evidence is still lacking.

  Electroresistance mechanism of memristive Cu2S thin films · 2025 · DOI

• However, a method to produce ultrathin single crystalline α-MoO3 with high transferability for functional device architecture is lacking.

  Thinner 2D α-MoO3 makes setting up memristors easier · 2024 · DOI

• The paper does not mention any specific limitations of the study. However, it can be inferred that the study is limited to simulated experiments and does not involve real-world data or applications.

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

• The development of scalable and reliable 2D material-based neuromorphic devices is a potential future research direction. The exploration of various mechanisms for artificial synaptic devices is a potential future research direction. The application of neuromorphic computing in AIoT systems and real-time information processing is a potential future research direction.

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

• The development of artificial neurons and synapses is key to implementing neuromorphic computing architecture. 2D materials have risen to prominence in recent years due to their atomic-scale thickness and rich tunable physicochemical properties. The paper aims to address the research gap in the development of dedicated artificial neuron and synapse devices based on 2D materials.

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

• 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.

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

• 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

• The study only explores a limited number of memristor devices. The switching speed is limited by the instrument used. The study does not explore the scalability of the memristors.

  Coupling atomic layer design with switching dynamics in sub-2 nanometer oxide memristors · 2026 · DOI

• The lack of explicit modeling of feature importance or the global state of the network in traditional STDP methods. The challenge of improving classification accuracy and mitigating vanishing and exploding gradients in SNNs.

  EWSbp-SNN: Error-Weighted STDP With Backpropagation for Data Classification in Spiking Neural Networks · 2026 · DOI

• Achieving high uniformity and long-term reliability for large-scale array fabrication on flexible substrates remains a challenge. The device has limited integration density and poor energy efficiency compared to other hardware implementations.

  Flexible organic-inorganic hybrid charge-trap synapse for physical reservoir computing · 2026 · DOI

• A substantial gap remains between the abstract formulations of Bayesian computation and the biological mechanisms underlying neural activity. The gap is due to the lack of understanding of how neural mechanisms perform abstract operations.

  A spiking neural network implementation of Gaussian belief propagation · 2026 · DOI

• The challenges include the limited weight resolution and device variability of ferroelectric memristive synaptic devices. The constraints of latency and power in brain-computer interfaces are challenges. The non-stationary nature of neural signals is a challenge.

  Personalized spiking neural networks with ferroelectric synapses for EEG signal processing · 2026 · DOI

• The cost of High Bandwidth Memory (HBM4) remains a barrier to mass-market adoption.

  Artificial Intelligence in Semi-Conductor Chips · 2026 · DOI

• Adding AI-driven logic that can re-route signals around manufacturing defects to increase wafer yield. Exploring Gallium Nitride (GaN) and other wide-bandgap materials for extreme environment AI.

  Artificial Intelligence in Semi-Conductor Chips · 2026 · DOI

• Real-time perception of dynamic visual scenes requires efficient extraction of spatiotemporal features.

  Non-pixelated in-materia retinomorphic sensor via photocarrier dynamics for precise spatiotemporal perception · 2026 · DOI

• Developing innovative technologies based on novel materials - Overcoming persistent computing-related challenges such as the von Neumann bottleneck and the scaling limits imposed by Moore's…

  Polarity‐Controlled Coexistence of Electrochemical Metallization and Valence Change Mechanisms in a Unified Metal Halide Perovskite Memristor · 2026 · DOI

• Limitations of ground-fabricated devices in terms of operating voltage and stability - Need for a new pathway for producing neuromorphic electronic devices…

  Neuromorphic Electronics Beyond Earth: Opportunities in Microgravity Manufacturing · 2026 · DOI

• The need for sustainable and environmentally friendly artificial synaptic devices, - The lack of demonstration of critical synaptic functions in honey based memristive…

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

• An important consideration for future work concerns the exploitation of the temporal processing capabilities inherent to SNNs. The current implementation uses rate encoding with 25 timesteps (S), which treats each input frame largely independently and uses spike frequency to encode pixel intensities.

  Spiking Neural Networks with Continual Learning for Steering Angle Regression: A Sustainable AI Perspective · 2026 · DOI

• Further research on the use of Spark for solving complex control problems. Investigation of the potential of spiking neural networks for efficient hardware implementations.

  Spark: modular spiking neural networks · 2026 · DOI

• Exploring the scalability of the memristors. Investigating the functionality of other materials in memristors. Developing memristors with even higher switching speeds and on/off ratios.

  Coupling atomic layer design with switching dynamics in sub-2 nanometer oxide memristors · 2026 · DOI

Most-cited papers in Advanced Memory and Neural Computing

• The missing memristor found · Nature · 2008 · 10,744 citations
• A million spiking-neuron integrated circuit with a scalable communication network and interface · Science · 2014 · 3,677 citations
• Training and operation of an integrated neuromorphic network based on metal-oxide memristors · Nature · 2015 · 2,806 citations
• Fully hardware-implemented memristor convolutional neural network · Nature · 2020 · 2,104 citations
• SLIM: Simultaneous Logic-in-Memory Computing Exploiting Bilayer Analog OxRAM Devices · Scientific Reports · 2020 · 1,428 citations
• Deep learning in spiking neural networks · Neural Networks · 2019 · 1,246 citations
• Ultrafast machine vision with 2D material neural network image sensors · Nature · 2020 · 1,157 citations
• Opportunities for neuromorphic computing algorithms and applications · Nature Computational Science · 2022 · 1,067 citations
• Neuromorphic spintronics · Nature Electronics · 2020 · 983 citations
• Neuro-inspired computing chips · Nature Electronics · 2020 · 890 citations

Most recent work

• Monolithic cell-on-memristor architecture enables wafer-scale integration of oscillatory chemoreceptors for bio-realistic gustatory chips · Nature Materials · 2026
• In-sensor polychromatic visual information processing enabled by all-optical controlled neuromorphic devices · Nano Energy · 2026
• Biologically Plausible Memristive Decision-Making Circuit for Adaptive Control in Industrial Autonomous Navigation · IEEE Transactions on Industrial Informatics · 2026
• A Memristor-Based Neural Network Circuit With Classical Conditioning and Fear Generalization · IEEE Transactions on Consumer Electronics · 2026
• Twelve-inch electrically anisotropic boridene for optoelectronic computing · Nature Nanotechnology · 2026
• A Second-Order Memristor Method to Construct Memristive Neural Networks With Multi-Butterfly and Multi-Scroll Dynamics · IEEE Transactions on Circuits and Systems I Regular Papers · 2026
• Maya-Meta P1: The Bhaya Quiescence Law and Buddhi S-Curve Determinism — Two Empirical Constants Confirmed Across 19 Substrates in Affective Neuromorphic Computing · Zenodo (CERN European Organization for Nuclear Research) · 2026
• Interface-engineered Gd₂O₃/ZrO₂ bilayer memristor for emulating synaptic plasticity in neuromorphic systems · Journal of Colloid and Interface Science · 2026
• Spiking neural networks with fatigue spike-timing-dependent plasticity learning using hybrid memristor arrays · Nature Electronics · 2026
• Multisensory Neuromorphic Devices: From Physics to Integration · Nano-Micro Letters · 2026

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