Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks

In the rapid evolution of next-generation brain-inspired artificial intelligence and increasingly sophisticated electromagnetic environment, the most bionic characteristics and anti-interference performance of spiking neural networks show great potential in terms of computational speed, real-time information processing, and spatio-temporal information processing. Data processing. Spiking neural network is one of the cores of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure and information transfer mode of biological neural networks. This paper summarizes the strengths, weaknesses and applicability of five neuronal models and analyzes the characteristics of five network topologies; then reviews the spiking neural network algorithms and summarizes the unsupervised learning algorithms based on synaptic plasticity rules and four types of supervised learning algorithms from the perspectives of unsupervised learning and supervised learning; finally focuses on the review of brain-like neuromorphic chips under research at home and abroad. This paper is intended to provide learning concepts and research orientations for the peers who are new to the research field of spiking neural networks through systematic summaries

Tailoring microcombs with inverse-designed, meta-dispersion microresonators

Nonlinear-wave mixing in optical microresonators offers new perspectives to generate compact optical-frequency microcombs, which enable an ever-growing number of applications. Microcombs exhibit a spectral profile that is primarily determined by their microresonator's dispersion; an example is the $\operatorname{sech}^2$ spectrum of dissipative Kerr solitons under anomalous group-velocity dispersion. Here, we introduce an inverse-design approach to spectrally shape microcombs, by optimizing an arbitrary meta-dispersion in a resonator. By incorporating the system's governing equation into a genetic algorithm, we are able to efficiently identify a dispersion profile that produces a microcomb closely matching a user-defined target spectrum, such as spectrally-flat combs or near-Gaussian pulses. We show a concrete implementation of these intricate optimized dispersion profiles, using selective bidirectional-mode hybridization in photonic-crystal resonators. Moreover, we fabricate and explore several microcomb generators with such flexible `meta' dispersion control. Their dispersion is not only controlled by the waveguide composing the resonator, but also by a corrugation inside the resonator, which geometrically controls the spectral distribution of the bidirectional coupling in the resonator. This approach provides programmable mode-by-mode frequency splitting and thus greatly increases the design space for controlling the nonlinear dynamics of optical states such as Kerr solitons.Comment: 16 pages, includes S

Sensitivity of Landsat NDVI to subpixel vegetation and topographic components in glacier forefields: assessment from high-resolution multispectral UAV imagery

International audienceRecently, deglaciated landscapes are ideal natural arenas to investigate ecological succession processes. However, ground data acquisition remains complicated as glacier forefields are often difficult to access and fieldwork possibilities remain limited. Remote sensing offers an opportunity to bypass this issue and increase spatial and temporal coverage of ecological parameters. The Landsat satellites (5 to 8) provide reflectance data for the past 40 years, which align with recent phenomena of glacier retreat and related ecological and geomorphological dynamics in glacier forefields. Difficulties remain as information retrieved from 30-m Landsat pixels are the result of a mixture of objects influencing reflectance signals. Here, we used a submeter multispectral unmanned aerial vehicle (UAV) image of the Glacier noir foreland, France, to assess the sensitivity of Landsat normalized difference vegetation index (NDVI) to subpixel vegetation and topographic components. We found a twofold linear relationship (a ¼ 0.456) and high sensitivity between fractional vegetation cover (FVC) and Landsat NDVI with detection of low vegetation changes (FVC > 5%) at low NDVI values (<0.1) (F-score ¼ 0.75). We also showed that vegetation height and subpixel topographic heterogeneity leads to misestimation of vegetation cover as quantified by Landsat NDVI. Overall, our comparative analysis using very-high resolution UAV imagery provides support for the use of widely available Landsat imagery for investigating vegetation dynamics in glacier forefields

Shrub growth in the Alps diverges from air temperature since the 1990s

International audienceIn the European Alps, air temperature has increased almost twice as much as the global average over the last century and, as a corollary, snow cover duration has decreased substantially. In the Arctic, dendroecological studies have evidenced that shrub growth is highly sensitive to temperature-this phenomenon has often been linked to shrub expansion and ecosystem greening. Yet, the impacts of climate change on mountain shrub radial growth have not been studied with a comparable level of detail so far. Moreover, dendroecological studies performed in mountain environments did not account for the potential modulation and/or buffering of global warmin

Local environmental context drives heterogeneity of early succession dynamics in alpine glacier forefields

Abstract. Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. While plant survey-based approaches applied along chronosequences provide invaluable information on plant communities, the “space-for-time” approach assumes environmental uniformity and equal ecological potential across sites and does not account for spatial variability in initial site conditions. Remote sensing provides a promising avenue for assessing plant colonisation dynamics using a so-called “real-time” approach. Here, we combined 36 years of Landsat imagery with extensive field sampling along chronosequences of deglaciation for eight glacier forefields in the south-western European Alps to investigate the heterogeneity of early plant succession dynamics. Based on the two complementary and independent approaches, we found strong variability in the time lag between deglaciation and colonisation by plants and in subsequent growth rates, and in the composition of early plant succession. All three parameters were highly dependent on the local environmental context, i.e., local vegetation surrounding the forefields and energy availability linked to temperature and snowmelt gradients. Potential geomorphological disturbance did not emerge as a strong predictor of succession parameters, perhaps due to insufficient spatial resolution of predictor variables. Notably, elapsed time since deglaciation showed no consistent relationship to plant assemblages, i.e., we did not identify a consistent order of successional species across forefields as a function of time. Overall, both approaches converged towards the conclusion that early plant succession is not stochastic as previous authors have suggested but rather deterministic. We discuss the importance of scale in deciphering the unique complexity of plant succession in glacier forefields and provide recommendations for improving botanical field surveys and using Landsat time series in glacier forefields systems. Our work demonstrates complementarity between remote sensing and field-based approaches for both understanding and predicting future patterns of plant succession in glacier forefields

Local environmental context drives heterogeneity of early succession dynamics in alpine glacier forefields

Abstract. Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. While plant survey-based approaches applied along chronosequences provide invaluable information on plant communities, the “space-for-time” approach assumes environmental uniformity and equal ecological potential across sites and does not account for spatial variability in initial site conditions. Remote sensing provides a promising avenue for assessing plant colonisation dynamics using a so-called “real-time” approach. Here, we combined 36 years of Landsat imagery with extensive field sampling along chronosequences of deglaciation for eight glacier forefields in the south-western European Alps to investigate the heterogeneity of early plant succession dynamics. Based on the two complementary and independent approaches, we found strong variability in the time lag between deglaciation and colonisation by plants and in subsequent growth rates, and in the composition of early plant succession. All three parameters were highly dependent on the local environmental context, i.e., local vegetation surrounding the forefields and energy availability linked to temperature and snowmelt gradients. Potential geomorphological disturbance did not emerge as a strong predictor of succession parameters, perhaps due to insufficient spatial resolution of predictor variables. Notably, elapsed time since deglaciation showed no consistent relationship to plant assemblages, i.e., we did not identify a consistent order of successional species across forefields as a function of time. Overall, both approaches converged towards the conclusion that early plant succession is not stochastic as previous authors have suggested but rather deterministic. We discuss the importance of scale in deciphering the unique complexity of plant succession in glacier forefields and provide recommendations for improving botanical field surveys and using Landsat time series in glacier forefields systems. Our work demonstrates complementarity between remote sensing and field-based approaches for both understanding and predicting future patterns of plant succession in glacier forefields