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Abstract Our study is to build an aftershock catalog with a low magnitude of completeness for the 2020 Mw 6.5 Stanley, Idaho, earthquake. This is challenging because of the low signal-to-noise ratios for recorded seismograms. Therefore, we apply convolutional neural networks (CNNs) and use 2D time–frequency feature maps as inputs for aftershock detection. Another trained CNN is used to automatically pick P-wave arrival times, which are then used in both nonlinear and double-difference earthquake location algorithms. Our new one-month-long catalog has 4644 events and a completeness magnitude (Mc) 1.9, which has over seven times more events and 0.9 lower Mc than the current U.S. Geological Survey National Earthquake Information Center catalog. The distribution and expansion of these aftershocks improve the resolution of two north-northwest-trending faults with different dip angles, providing further support for a central stepover region that changed the earthquake rupture trajectory and induced sustained seismicity. 

Emerging research has begun investigating the neural underpinnings of the biological and psychological differences that drive political ideology, attitudes, and actions. Here, we explore the neurological roots of politics through conducting a large sample, whole-brain analysis of functional connectivity (FC) across common fMRI tasks. Using convolutional neural networks, we develop predictive models of ideology using FC from fMRI scans for nine standard task-based settings in a novel cohort of healthy adults (n = 174, age range: 18 to 40, mean = 21.43) from the Ohio State University Wellbeing Project. Our analyses suggest that liberals and conservatives have noticeable and discriminative differences in FC that can be identified with high accuracy using contemporary artificial intelligence methods and that such analyses complement contemporary models relying on socio-economic and survey-based responses. FC signatures from retrieval, empathy, and monetary reward tasks are identified as important and powerful predictors of conservatism, and activations of the amygdala, inferior frontal gyrus, and hippocampus are most strongly associated with political affiliation. Although the direction of causality is unclear, this study suggests that the biological and neurological roots of political behavior run much deeper than previously thought.

 

Previously, symmetry of network models has been proposed to account for interocular grouping during binocular rivalry. Here, we construct and analyze generalized rivalry network models with different types of symmetry (based on different kinds of excitatory coupling) to derive predictions of possible perceptual states in 12 experiments with four retinal locations. Percepts in binocular rivalry involving more than three locations have not been empirically investigated due to the difficulty in reporting simultaneous percepts at multiple locations. Here, we develop a novel reporting procedure in which the stimulus disappears when the subject is cued to report the simultaneously perceived colors in all four retinal locations. This procedure ensures that simultaneous rather than sequential percepts are reported. The procedure was applied in 12 experiments with six binocular rivalry stimulus configurations, all consisting of dichoptic displays of red and green squares at four locations. We call configurations with an even or odd number of red squares even or odd configurations, respectively. In experiments using even stimulus configurations, we found that even percepts were more frequently observed than odd percepts, whereas in experiments using odd stimulus configurations even and odd percepts were observed with equal probability. The generalized rivalry network models in which couplings depend on stimulus features and spatial configurations was in better agreement with the empirical results. We conclude that the excitatory coupling strength in the horizontal and vertical configurations are different and the coupling strengths between the same color and between different colors are different. NEW & NOTEWORTHY Wilson network models of interocular groupings during binocular rivalry are constructed by considering features that indicate equal coupling strengths. Network symmetries, based on equal couplings, predict percepts. For a four-location rivalry experiment with red or green squares at each location, we analyze different possible Wilson networks. In our experiments we develop a novel reporting procedure and show that networks in which stimulus features and spatial configurations are distinguished best agree with experiments.