ALBERT

Beschreibung: In the study of sensorimotor systems, an important research goal has been to understand the way neural networks in the spinal cord and brain interact to control voluntary movement. Computational modeling has provided insight into the interaction between centrally generated commands, proprioceptive feedback signals and the biomechanical responses of the moving body. Research in this field is also driven by the need to improve and optimize rehabilitation after nervous system injury and to devise biomimetic methods of control in robotic devices. This research topic is focused on efforts dedicated to identify and model the neuromechanical control of movement. Neural networks in the brain and spinal cord are known to generate patterned activity that mediates coordinated activation of multiple muscles in both rhythmic and discrete movements, e.g. locomotion and reaching. Commands descending from the higher centres in the CNS modulate the activity of spinal networks, which control movement on the basis of sensory feedback of various types, including that from proprioceptive afferents. The computational models will continue to shed light on the central strategies and mechanisms of sensorimotor control and learning. This research topic demonstrated that computational modeling is playing a more and more prominent role in the studies of postural and movement control. With increasing ability to gather data from all levels of the neuromechanical sensorimotor systems, there is a compelling need for novel, creative modeling of new and existing data sets, because the more systematic means to extract knowledge and insights about neural computations of sensorimotor systems from these data is through computational modeling. While models should be based on experimental data and validated with experimental evidence, they should also be flexible to provide a conceptual framework for unifying diverse data sets, to generate new insights of neural mechanisms, to integrate new data sets into the general framework, to validate or refute hypotheses and to suggest new testable hypotheses for future experimental investigation. It is thus expected that neural and computational modeling of the sensorimotor system should create new opportunities for experimentalists and modelers to collaborate in a joint endeavor to advance our understanding of the neural mechanisms for postural and movement control. The editors would like to thank Professor Arthur Prochazka, who helped initially to set up this research topic, and all authors who contributed their articles to this research topic. Our appreciation also goes to the reviewers, who volunteered their time and effort to help achieve the goal of this research topic. We would also like to thank the staff members of editorial office of Frontiers in Computational Neuroscience for their expertise in the process of manuscript handling, publishing, and in bringing this ebook to the readers. The support from the Editor-in-Chief, Dr. Misha Tsodyks and Dr. Si Wu is crucial for this research topic to come to a successful conclusion. We are indebted to Dr. Si Li and Ms. Ting Xu, whose assistant is important for this ebook to become a reality. Finally, this work is supported in part by grants to Dr. Ning Lan from the Ministry of Science and Technology of China (2011CB013304), the Natural Science Foundation of China (No. 81271684, No. 61361160415, No. 81630050), and the Interdisciplinary Research Grant cross Engineering and Medicine by Shanghai Jiao Tong University (YG20148D09). Dr. Vincent Cheung is supported by startup funds from the Faculty of Medicine of The Chinese University of Hong Kong. Guest Associate Editors Ning Lan, Vincent Cheung, and Simon Gandevia

Beschreibung: In the Tropical Andes, a wide range of topographic and climatic settings results in a large diversity of glaciers distributed over mountain ranges and volcanoes. Previous studies, based on geodetic estimations, show that glaciers in this region have lost mass at a rate of -0.50 m w.e. yr〈sup〉-1〈/sup〉 during the last two decades. At the interannual time step, efforts to understand the mass balance variability as a direct response to the Andean climate, have been constrained by the number of available observations and a variety of interpolation approaches used to compute the glaciological mass balances. Thanks to the availability of point mass balance on seven tropical glaciers, we assess the annual signal of the surface mass balances during the 1990-2020 period. Thus, glaciers in the inner tropics are subjected to a less pronounced mass loss of -0.35 m w.e. yr〈sup〉-1〈/sup〉, while glaciers in the outer tropics exhibit a mass balance of -0.55 m w.e. yr〈sup〉-1〈/sup〉. Our results suggest that this contrasting behavior may be related to the latitudinal location and elevation of glaciers. The interannual cycles captured by the point mass balance correlate with climate variability and periodical patterns as El Niño Southern Oscillation. Although its behavior depends on the climatic conditions at the regional scale and the local topography, on the whole, glaciers are shrinking throughout the region with negative consequences in terms of water availability for Andean countries.

Beschreibung: The mass balance observations have been carried out on Chhota Shigri Glacier since 2002. Due to difficult glacier accessibility, dynamics, and involvement of different researchers, the measurement network differs in space over time. Further, almost all glaciological mass balance series are affected by the systematic biases due to specifically designed point mass balance network, and lack of basal and internal mass loss measurements. The goal of this study is to re-analyze the mass balances on Chhota Shigri Glacier and provide a homogenized series. We applied the nonlinear model of Vincent and others (2018) to capture the spatiotemporal changes in point mass balances and re-estimated the annual glacier-wide mass balances over 2002-2020. The nonlinear model also allows to detect the erroneous point measurements which are either corrected from the field notebooks or removed from the analysis. The mass balances are then homogenized applying the areal changes estimated using SPOT5 (2005) and Pléiades images (2014 and 2020), assuming a linear areal change. Finally, the mass balances are adjusted using the geodetic mass balances estimated by differencing ASTER and Pléiades DEMs (Oct 2003–Sept 2014) and Pléiades DEMs (Sept 2014–Sept 2020). The reanalyzed mean annual glacier-wide mass balance is estimated to be –0.47 ± 0.18 m w.e. a〈sup〉−1〈/sup〉 (equivalent to a cumulative mass loss of –8.40 m w.e.) with a standard deviation of 0.66 m w.e. a〈sup〉−1〈/sup〉 over 2002-2020. The glacier lost maximum mass of –1.29 ± 0.16 m w.e. in 2017/18 while gained a mass of 0.49 ± 0.19 m w.e. in 2004/05.

Beschreibung: Precipitation in the tropics is dominated by variability on seasonal, intraseasonal and diurnal scales. However, at the finest spatial scales, this variability can be strongly modulated by numerous local factors that represent a range of physical processes. One aspect of this modulation that operates across multiple scales is the orientation of coastal and inland topography relative to the background wind. This is particularly relevant where complex topography occurs in the vicinity of tropical coastlines, which can cause local changes in moisture flux convergence. Another aspect is the coupling of the diurnal cycle to changes in surface heating, surface moisture, column moisture, background wind and cloudiness. This link between clouds, radiation and diurnally forced convection forges a two-way interaction between the intraseasonal scales and mesoscale variability. Further to these modulating factors, propagating disturbances from neighbouring coastlines, and interactions between coastal processes across adjacent seas introduces a non-local mesoscale source of variability, particularly amidst the complex archipelago of the Maritime Continent. These processes also occur outside the deep tropics, where the added complexity of mid-latitude interactions contributes to the overall variation in precipitation. Multiple spatial and temporal scales are impacted by these competing sources of variability, which presents a challenge in quantifying their representation and impact in weather and climate models. This has implications for weather prediction and down-scaling of climate change projections. This talk will examine some of the local manifestations of scale-interactions in tropical Queensland (Australia) and the Maritime Continent, using diverse observational datasets and high-resolution numerical modelling.