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Network features found in the brain may help implement more efficient and robust neural networks. Spiking neural networks (SNNs) process spikes in the spatiotemporal domain and can offer better energy efficiency than deep neural networks. However, most SNN implementations rely on simple point neurons that neglect the rich neuronal and dendritic dynamics. Herein, a bio‐inspired columnar learning network (CLN) structure that employs feedforward, lateral, and feedback connections to make robust classification with sparse data is proposed. CLN is inspired by the mammalian neocortex, comprising cortical columns each containing multiple minicolumns formed by interacting pyramidal neurons. A column continuously processes spatiotemporal signals from its sensor, while learning spatial and temporal correlations between features in different regions of an object along with the sensor's movement through sensorimotor interaction. CLN can be implemented using memristor crossbars with a local learning rule, spiking timing‐dependent plasticity (STDP), which can be natively obtained in second‐order memristors. CLN allows inputs from multiple sensors to be simultaneously processed by different columns, resulting in higher classification accuracy and better noise tolerance. Analysis of networks implemented on memristor crossbars shows that the system can operate at very low power and high throughput, with high accuracy and robustness to noise. [ABSTRACT FROM AUTHOR]

Memristive devices have demonstrated rich switching behaviors that closely resemble synaptic functions and provide a building block to construct efficient neuromorphic systems. It is demonstrated that resistive switching effects are controlled not only by the external field, but also by the dynamics of various internal state variables that facilitate the ionic processes. The internal temperature, for example, works as a second‐state variable to regulate the ion motion and provides the internal timing mechanism for the native implementation of timing‐ and rate‐based learning rules such as spike timing dependent plasticity (STDP). In this work, it is shown that the 2nd state‐variable in a Ta2O5‐based memristor, its internal temperature, can be systematically engineered by adjusting the material properties and device structure, leading to tunable STDP characteristics with different time constants. When combined with an artificial post‐synaptic neuron, the 2nd‐order memristor synapses can spontaneously capture the temporal correlation in the input streaming events. [ABSTRACT FROM AUTHOR]

Memristors have emerged as transformative devices to enable neuromorphic and in‐memory computing, where success requires the identification and development of materials that can overcome challenges in retention and device variability. Here, high‐entropy oxide composed of Zr, Hf, Nb, Ta, Mo, and W oxides is first demonstrated as a switching material for valence change memory. This multielement oxide material provides uniform distribution and higher concentration of oxygen vacancies, limiting the stochastic behavior in resistive switching. (Zr, Hf, Nb, Ta, Mo, W) high‐entropy‐oxide‐based memristors manifest the "cocktail effect," exhibiting comparable retention with HfO2‐ or Ta2O5‐based memristors while also demonstrating the gradual conductance modulation observed in WO3‐based memristors. The electrical characterization of these high‐entropy‐oxide‐based memristors demonstrates forming‐free operation, low device and cycle variability, gradual conductance modulation, 6‐bit operation, and long retention which are promising for neuromorphic applications. [ABSTRACT FROM AUTHOR]

The development of smart, scalable, and power efficient computers relies on innovative technologies that can distribute memory alongside processing, such as emerging neuromorphic computing and in-memory logic technologies. Here, a type of vertical 3-terminal oxide based nanoionic device capable of implementing heterosynaptic plasticity and nonvolatile Boolean logic simultaneously is demonstrated. The heterosynaptic plasticity endows the devices with facilely tunable synaptic kinetics via tailoring modulatory signals, which is shown to be crucial for achieving optimized learning scheme, therefore offering promising building blocks for neuromorphic computing. Furthermore, it is demonstrated that these heterosynaptic devices can simultaneously be utilized to implement nonvolatile Boolean logic with improved efficiency compared with existing approaches, therefore implying in-memory computing potentialities. The heterosynaptic devices with such multifunctionality thus hold great potential for next-generation non-von Neumann computing applications. [ABSTRACT FROM AUTHOR]

Memristors have attracted broad interest as a promising candidate for future memory and computing applications. Particularly, it is believed that memristors can effectively implement synaptic functions and enable efficient neuromorphic systems. Most previous studies, however, focus on implementing specific synaptic learning rules by carefully engineering external programming parameters instead of focusing on emulating the internal cause that leads to the apparent learning rules. Here, it is shown that by taking advantage of the different time scales of internal oxygen vacancy ( VO) dynamics in an oxide-based memristor, diverse synaptic functions at different time scales can be implemented naturally. Mathematically, the device can be effectively modeled as a second-order memristor with a simple set of equations including multiple state variables. Not only is this approach more biorealistic and easier to implement, by focusing on the fundamental driving mechanisms it allows the development of complete theoretical and experimental frameworks for biologically inspired computing systems. [ABSTRACT FROM AUTHOR]

Near infrared (NIR) synaptic devices offer a remote-control approach to implement neuromorphic computing for data safety and artificial retinal system applications. In upconverting nanoparticles (UCNPs)-mediated optogenetics biosystems, NIR regulation of membrane ion channels allows remote and selective control of the Ca2+ flux to modulate synaptic plasticity behaviors. Inspired by the upconversion optogenetics, we proposed a NIR artificial synapse based on a UCNPs-MoS 2 floating gate phototransistor in which MoS 2 acts as light-sensitive ion channels to reabsorb the visible light emitted from UCNPs under NIR illumination. As a result, the synaptic device exhibits stable persistent photocurrent (PPC) effect up to 353 K and ultrahigh photogain (~108 electrons per photon), ensuring the long-term potentiation (LTP) behavior. Simulations using the handwritten digit data sets indicate good recognition accuracy of the light-controlled artificial neuron network. Overall, this design concept combining biology, optics and electronics opens up a new avenue for developing optogenetics-inspired neuromorphic technology in the future. Image 1 • We design an upconversion optogenetics-inspired NIR phototransistor synapse based on UCNPs-sensitized MoS 2. • Enormously lengthened lifetime of excitons and accelerated charge carrier generation are obtained. • The synaptic device exhibits stable persistent photocurrent effect at high temperature up to 353 K. • The single-layer perception artificial neuromorphic network (ANN) is emulated to perform the supervised learning. [ABSTRACT FROM AUTHOR]

Advances in the understanding of nanoscale ionic processes in solid‐state thin films have led to the rapid development of devices based on coupled ionic–electronic effects. For example, ion‐driven resistive‐switching (RS) devices have been extensively studied for future memory applications due to their excellent performance in terms of switching speed, endurance, retention, and scalability. Recent studies further suggest that RS devices are more than just resistors with tunable resistance; instead, they exhibit rich and complex internal ionic dynamics that equip them with native information‐processing capabilities, particularly in the temporal domain. RS effects induced by the migration of different types of ions, often driven by an electric field, are discussed. It is shown that, by taking advantage of the different state variables controlled by the ionic processes, important synaptic functions can be faithfully implemented in solid‐state devices and networks. Recent efforts on improving the controllability of ionic processes to optimize device performance are also discussed, along with new opportunities for material design and engineering enabled by the ability to control ionic processes at the atomic scale. [ABSTRACT FROM AUTHOR]

Memristors have been considered as a leading candidate for a number of critical applications ranging from nonvolatile memory to non-Von Neumann computing systems. Feature extraction, which aims to transform input data from a high-dimensional space to a space with fewer dimensions, is an important technique widely used in machine learning and pattern recognition applications. Here, we experimentally demonstrate that memristor arrays can be used to perform principal component analysis, one of the most commonly used feature extraction techniques, through online, unsupervised learning. Using Sanger's rule, that is, the generalized Hebbian algorithm, the principal components were obtained as the memristor conductances in the network after training. The network was then used to analyze sensory data from a standard breast cancer screening database with high classification success rate (97.1%). [ABSTRACT FROM AUTHOR]