Annual sea level variations off Atlantic Canada from satellite altimetry

Annual cycle of sea level off Atlantic Canada has been investigated based on a merged satellite altimetry dataset and a monthly temperature and salinity dataset. The altimetric results were compared with coastal tide-gauge data and steric height calculated from the temperature and salinity dataset. There was a general north-south variation in the amplitude of the altimetric annual cycle, increasing from 4 cm in the Labrador Sea to 15 cm in the Gulf Stream and the North Atlantic Current Region. The annual cycle in the deep ocean can approximately be accounted for by the steric height variability relative to 700 m, in which the thermosteric effect was the dominant contributor. The halosteric effect over the continental slope, especially over the northern Labrador Slope was also important. While the thermosteric effect occurred dominantly at the top 100 m water column, there was substantial halosteric variation in the 100–300 m water column. The annual sea level cycle along the Canadian Atlantic coast showed a complicated pattern in amplitude, but the phase was highly coherent with the highest sea level in fall. The steric height accounts for a substantial portion of the coastal annual cycle, but other factors such as wind forcing may be equally important

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated $11\times$, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.Comment: Accepted in Science Advances (https://www.science.org/doi/10.1126/sciadv.adi1480

Theoretical foundations of studying criticality in the brain

Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information processing capacities in the brain. While considerable evidence generally supports this hypothesis, non-negligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, i.e., ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistic techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions