Designing suitable material systems to construct artificial synapses and exploring novel synaptic functions is a crucial step toward the realization of efficient large-scale bioinspired neuromorphic systems. In this work, flexible and insoluble bio-memristor devices are fabricated by precisely engineering the molecular structures of wool keratin. This flexible Ag/ keratin/indium tin oxide-polyethylene naphthalate synaptic device possesses enhanced mechanical resistance, which is achieved by photocross-linking keratin molecules, and can withstand a bending radius of up to 1.2 mm. This device is promising for implantable applications because it is water-resistant. When modulated by triangle-wave DC voltages and pulsed voltages, this flexible electronic device emulates typical memristor characteristics and synaptic functions, including potentiation/depression, spike timing dependent plasticity, and long-term/short-term plasticity. Simulation results indicate that a memristor network made by this woolkeratin based device has ≈95.8% memory learning accuracy and capability for pattern learning. Combined, these features prove that the cross-linked wool-keratin based device has potential in wearable and flexible neuron computing systems.

Biomaterial-based memristors (bio-memristors) are often adopted to emulate biological synapse functions and applied to construct neural computing networks in brain-inspired chip systems. However, the randomness of conductive filament formation in biomemristors inhibits their switching performance by causing the dispersion of the device-switching parameters. In this case, a facile porous silk fibroin (p-SF) memristor was obtained through a protein surface reconstruction strategy, in which the size of the hole can be adjusted by the density of hybrid nanoseeds. The porous SF memristors exhibit greatly enhanced electrical characteristics, including uniform I−V cycles, centralized distribution of the switching voltages, and both high and low resistances, compared to devices without pores. The results of three-dimensional (3D) simulations based on classical density functional theory (cDFT) suggest that the reconstructed pores in the SF layers guide the formation and fracture of Ag filaments under an electric field and enhance the overall conductivity by separating Ag + ion and electron diffusion pathways. Ag + ions are predicted to preferentially diffuse through pores, whereas electrons diffuse through the SF network. Interestingly, the device conductance can be bidirectionally modulated gradually by positive and negative voltages, can faithfully simulate short-term and long-term plasticity, and can even realize the triplet-spike-timing-dependent plasticity (triplet-STDP) rule, which can be used for pattern recognition in biological systems. The simulation results reveal that a memristor network of this type has an accuracy of ∼95.78% in memory learning and the capability of pattern learning. This work provides a facile technology route to improve the performance of bionic-material memristors.

The development of conductive bridging random access memory (CBRAM) as an artificial synaptic device is an important step in the realization of an efficient biomimetic neural morphology computing system. In...

With the development of technology, the learning and memory functions of artificial memristor synapses are necessary for realizing artificial neural networks and neural neuromorphic computing. Owing to their high scalability performance, nanosheet materials have been widely employed in cellular-level learning, but the behaviors of nociceptor based on nanosheet materials have rarely been studied. Here, we present a memristor with an Al/TiO 2 /Pt structure. After electroforming, the memristor device showed a gradual conductance regulation and could simulate synaptic functions such as the potentiation and depression of synaptic weights. We also designed a new scheme that verifies the pain sensitization, desensitization, allodynia, and hyperalgesia behaviors of real nociceptors in the fabricated memristor. Memristors with these behaviors can significantly improve the quality of intelligent electronic devices. Data fitting showed that the high resistance and low resistance states were consistent with the hopping conduction mechanism. This work promises the application of TiO 2-based devices in next-generation neuromorphological systems.

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