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Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs' operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the integration of more biologically plausible mechanisms, such as synaptic plasticity, to improve these networks' capabilities. Moreover, we delve into the potential advantages and challenges accompanying this approach. In this review, we pinpoint promising avenues for future research in this rapidly advancing field, which could bring us closer to understanding the essence of intelligence. [ABSTRACT FROM AUTHOR]

This paper reports an all-optical vector magnetometer enhanced by a machine learning model. Using a dual probing beam setup, spin projections in both probe directions are simultaneously detected. Vector information is directly obtained from the measured phases of spin projection signals. To enhance the measurement accuracy and mitigate the dead zone effect, we introduce an artificial neural network (ANN) to link the phase signals to the field direction. With the addition of amplitude information to the ANN input, the average angle error is reduced to less than 0.3 ° within a hemisphere. Furthermore, this configuration demonstrates a field angle sensitivity of better than 30 μ rad / Hz 1 / 2 . [ABSTRACT FROM AUTHOR]

The wide diversity of species and the remarkable variation in morphological features that allow plants to adapt to a wide range of terrestrial environments is a fact that highlights the fundamental and crucial role of plants in the field of biodiversity studies. Currently, research on leaf classification is limited and in its early stages. A novel classification system based on laser-induced breakdown spectroscopy (LIBS) technology was proposed in this paper, integrated with machine learning for real-time, in situ detection and analysis of leaves. Four representative leaf samples—Ilex chinensis, Camellia japonica, Cinnamomum camphora, and Osmanthus fragrans—were subjected to spectral analysis and machine learning techniques. Spectral analysis revealed distinct spectral lines corresponding to elements such as Ca, Al, Mg, Na, and Fe, alongside common elements including C, N, and O. Principal component analysis (PCA) was employed to reduce the dimensionality of the spectral data, and the first 13 principal components used in this study captured 98.76% of the total variance. Following this, support vector machine (SVM), backpropagation artificial neural network and convolutional neural network (CNN) algorithms were applied for machine learning on the principal components to develop leaf recognition classification models. Through comparison, the CNN algorithm, boasting a classification accuracy of up to 94.44%, was ultimately selected. The models established by SVM and back propagation artificial neural network achieved accuracy of only 83.33% and 90.00%, respectively. The results suggest that integrating LIBS with machine learning is an effective and precise approach for leaf classification, offering promising applications in biodiversity research. [ABSTRACT FROM AUTHOR]

The co-location of memory and processing is a core principle of neuromorphic computing. A local memory device for synaptic weight storage has long been recognized as an enabling element for large-scale, high-performance neuromorphic hardware. In this work, we demonstrate programmable superconducting synapses with integrated memories for use in superconducting optoelectronic neural systems. Superconducting nanowire single-photon detectors and Josephson junctions are combined into programmable synaptic circuits that exhibit single-photon sensitivity, memory cells with more than 400 internal states, leaky integration of input spike events, and 0.4 fJ programming energies (including cooling power). These results are attractive for implementing a variety of supervised and unsupervised learning algorithms and lay the foundation for a new hardware platform optimized for large-scale spiking network accelerators. [ABSTRACT FROM AUTHOR]

Despite significant advances in deep neural networks across diverse domains, challenges persist in safety-critical contexts, including domain shift sensitivity and unreliable uncertainty estimation. To address these issues, this study investigates Bayesian learning for uncertainty handling in modern neural networks. However, the high-dimensional, non-convex nature of the posterior distribution poses practical limitations for epistemic uncertainty estimation. The Laplace approximation, as a cost-efficient Bayesian method, offers a practical solution by approximating the posterior as a multivariate normal distribution but faces computational bottlenecks in precise covariance matrix computation and storage. This research employs subnetwork inference, utilizing only a subset of the parameter space for Bayesian inference. In addition, a Kronecker-factored and low-rank representation is explored to reduce space complexity and computational costs. Several corrections are introduced to converge the approximated curvature to the exact Hessian matrix. Numerical results demonstrate the effectiveness and competitiveness of this method, whereas qualitative experiments highlight the impact of Hessian approximation granularity and parameter space utilization in Bayesian inference on mitigating overconfidence in predictions and obtaining high-quality uncertainty estimates. [ABSTRACT FROM AUTHOR]

Memristor crossbar arrays are expected to achieve highly energy-efficient neuromorphic computing via implementing parallel vector–matrix multiplication (VMM) in situ. The similarities between memristors and neural synapses offer opportunities for realizing hardware-based brain-inspired computing, such as spike neural networks. However, the nonlinear I–V characteristics of the memristors limit the implementation of parallel VMM on passive memristor crossbar arrays. In our work, we propose to utilize differential conductance as a synaptic weight to implement linear VMM operations on a passive memristor array in parallel. We fabricated a TiO2/HfO2 memristor crossbar array, in which differential-conductance-based synaptic weight exhibits plasticity, nonvolatility, multi-states, and tunable ON/OFF ratio. The noise-dependent accuracy performance of VMM operations based on the proposed approach was evaluated, offering an optimization guideline. Furthermore, we demonstrated a spike neural network circuit capable of processing small spiking signals through the differential-conductance-based synapses. The experimental results showcase effective space-coded and time-coded spike pattern recognition. Importantly, our work opens up new possibilities for the development of passive memristor arrays, leading to increased energy and area efficiency in brain-inspired chips. [ABSTRACT FROM AUTHOR]