A Neurodynamic Model of Saliency Prediction in V1

Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex.

Understanding Workplace Interpersonal Mistreatment in the NZ Military: Structure, Antecedents and Consquences

<p>This thesis describes the development and testing of a theoretically plausible model of antecedents and consequences of workplace interpersonal mistreatment using archival data (n = 10697) of civilian and military employees. The sample was split into calibration and validation samples. Principle component and confirmatory factor analysis revealed a complex structure for the workplace interpersonal mistreatment construct across three types of behaviour, and across observed and experienced mistreatment. Furthermore, a total of 17 robust factors were identified in the survey, of which a subset of eight factors was used for developing the model of antecedents and consequences of WIM. The model was tested and refined using regression and structural equation modelling in two samples and validated in a third sample. Individual (seniority), workplace (directive leadership, equity and diversity climate, and health and safety climate) and organisational features (aligned-cohesive culture, service culture) all predict mistreatment. Outcomes of mistreatment include stress, organisational commitment, job satisfaction and leaving intentions. The model showed good fit in the validation sample and is therefore likely to generalise to the population. Implications for organisations and recommendations for future research are discussed.</p>

Understanding Workplace Interpersonal Mistreatment in the NZ Military: Structure, Antecedents and Consquences

<p>This thesis describes the development and testing of a theoretically plausible model of antecedents and consequences of workplace interpersonal mistreatment using archival data (n = 10697) of civilian and military employees. The sample was split into calibration and validation samples. Principle component and confirmatory factor analysis revealed a complex structure for the workplace interpersonal mistreatment construct across three types of behaviour, and across observed and experienced mistreatment. Furthermore, a total of 17 robust factors were identified in the survey, of which a subset of eight factors was used for developing the model of antecedents and consequences of WIM. The model was tested and refined using regression and structural equation modelling in two samples and validated in a third sample. Individual (seniority), workplace (directive leadership, equity and diversity climate, and health and safety climate) and organisational features (aligned-cohesive culture, service culture) all predict mistreatment. Outcomes of mistreatment include stress, organisational commitment, job satisfaction and leaving intentions. The model showed good fit in the validation sample and is therefore likely to generalise to the population. Implications for organisations and recommendations for future research are discussed.</p>

A deep learning approach for complex microstructure inference

Automated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing. This, along with deep learning’s seemingly intransparent decision-making process, hampers its breakthrough in this field. We apply a multidisciplinary deep learning approach, devoting equal attention to specimen preparation and imaging, and train distinct U-Net architectures with 30–50 micrographs of different imaging modalities and electron backscatter diffraction-informed annotations. On the challenging task of lath-bainite segmentation in complex-phase steel, we achieve accuracies of 90% rivaling expert segmentations. Further, we discuss the impact of image context, pre-training with domain-extrinsic data, and data augmentation. Network visualization techniques demonstrate plausible model decisions based on grain boundary morphology.

Neural network models for spinal implementation of muscle synergies

Muscle synergies have been proposed as functional modules to simplify the complexity of body motor control; however, their neural implementation is still unclear. Converging evidence suggests that output projections of the spinal premotor interneurons (PreM-INs) underlie the formation of muscle synergies, but they exhibit a substantial variation across neurons and exclude standard models assuming a small number of unitary "modules" in the spinal cord. Here we compared neural network models for muscle synergies to seek a biologically plausible model that reconciles previous clinical and electrophysiological findings. We examined three neural network models: one with random connections (non-synergy model), one with a small number of spinal synergies (simple synergy model), and one with a large number of spinal neurons representing muscle synergies with a certain variation (population synergy model). We found that the simple and population synergy models emulate the robustness of muscle synergies against cortical stroke observed in human stroke patients. Furthermore, the size of the spinal variation of the population synergy matched well with the variation in spinal PreM-INs recorded in monkeys. These results suggest that a spinal population with moderate variation is a biologically plausible model for the neural implementation of muscle synergies.

A Biologically Plausible Model for Continual Learning using Synaptic Weight Attractors

The human brain readily learns tasks in sequence without forgetting previous ones. Artificial neural networks (ANNs), on the other hand, need to be modified to achieve similar performance. While effective, many algorithms that accomplish this are based on weight importance methods that do not correspond to biological mechanisms. Here we introduce a simple, biologically plausible method for enabling effective continual learning in ANNs. We show that it is possible to learn a weight-dependent plasticity function that prevents catastrophic forgetting over multiple tasks. We highlight the effectiveness of our method by evaluating it on a set of MNIST classification tasks. We further find that the use of our method promotes synaptic multi-modality, similar to that seen in biology.

High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions

Controlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings.

Explaining distortions in metacognition with an attractor network model of decision uncertainty

Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible.

Comment on “Emplazamiento del magmatismo Paleoceno-Eoceno bajo un régimen transtensional y su evolución a un equilibrio dinámico en el borde occidental de Colombia” by Grajales et al., Rev. Mex. Cienc. Geol. (2020), 37(3), 250-268

Grajales et al. (2020) reviewed geochronological and geochemical data from Paleogene volcanic and plutonic rocks outcropping in the Panama-Choco Block (north western Cordillera) and southern Western Cordillera, as well as the Central Cordillera of Colombia. These data were used to support a model of continuous Paleogene arc magmatism along the Colombian continental margin, and to propose a paleogeographic model for the arc. The authors did not discuss previously published paleomagnetic, geochemical, geochronological, thermochronological and provenance constraints from Cretaceous to Miocene rocks of western and northern Colombia, Panama, and Ecuador that support a more plausible model of a double subduction system controlled by the convergence of the Caribbean and Farallon plates beneath the north Andean block during Paleogene. In this comment, we discuss shortcomings in the data and model proposed by Grajales et al. (2020) and present an alternative interpretation for contemporaneous arc-like magmatism during the Paleogene in the Northern Andes. We conclude that the double subduction system is the more plausible explanation for the contemporaneous arc-like magmatism during the Paleogene, currently exposed in the northern and southern portions of the Northern Andes.