Computational physics of the mind

In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures

Surrogate-assisted Bayesian inversion for landscape and basin evolution models

The complex and computationally expensive features of the forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods. Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters. In our previous work, parallel tempering Bayeslands was developed as a framework for parameter estimation and uncertainty quantification for the landscape and basin evolution modelling software Badlands. Parallel tempering Bayeslands features high-performance computing with dozens of processing cores running in parallel to enhance computational efficiency. Although parallel computing is used, the procedure remains computationally challenging since thousands of samples need to be drawn and evaluated. In large-scale landscape and basin evolution problems, a single model evaluation can take from several minutes to hours, and in certain cases, even days. Surrogate-assisted optimization has been with successfully applied to a number of engineering problems. This motivates its use in optimisation and inference methods suited for complex models in geology and geophysics. Surrogates can speed up parallel tempering Bayeslands by developing computationally inexpensive surrogates to mimic expensive models. In this paper, we present an application of surrogate-assisted parallel tempering where that surrogate mimics a landscape evolution model including erosion, sediment transport and deposition, by estimating the likelihood function that is given by the model. We employ a machine learning model as a surrogate that learns from the samples generated by the parallel tempering algorithm. The results show that the methodology is effective in lowering the overall computational cost significantly while retaining the quality of solutions.Comment: Under review. arXiv admin note: text overlap with arXiv:1811.0868

Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing

Longitudinal neuroimaging analysis methods have remarkably advanced our understanding of early postnatal brain development. However, learning predictive models to trace forth the evolution trajectories of both normal and abnormal cortical shapes remains broadly absent. To fill this critical gap, we pioneered the first prediction model for longitudinal developing cortical surfaces in infants using a spatiotemporal current-based learning framework solely from the baseline cortical surface. In this paper, we detail this prediction model and even further improve its performance by introducing two key variants. First, we use the varifold metric to overcome the limitations of the current metric for surface registration that was used in our preliminary study. We also extend the conventional varifold-based surface registration model for pairwise registration to a spatiotemporal surface regression model. Second, we propose a morphing process of the baseline surface using its topographic attributes such as normal direction and principal curvature sign. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we use the proposed varifold-based shape regression model to estimate geodesic cortical shape evolution trajectories for each training subject. We then build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we select the best learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints, based on similarity metrics between this baseline surface and the learnt baseline population average surface atlas. We used a leave-one-out cross validation method to predict the inner cortical surface shape at 3, 6, 9 and 12 months of age from the baseline cortical surface shape at birth. Our method attained a higher prediction accuracy and better captured the spatiotemporal dynamic change of the highly folded cortical surface than the previous proposed prediction method

Integrated surface-subsurface model to investigate the role of groundwater in headwater catchment runoff generation : a minimalist approach to parameterisation

This work was funded by NERC/JPI SIWA project (NE/M019896/1) and the European Research Council ERC (project GA 335910 VeWa). Numerical simulations were performed using the Maxwell High Performance Computing Cluster of the University of Aberdeen IT Service, provided by Dell Inc. and supported by Alces Software. Aquanty Inc. is acknowledged for support in providing HGS simulation software compatible with the Maxwell High Performance Computing Cluster. We would also like to thank the anonymous reviewers for their constructive comments that improved the manuscript.Peer reviewedPublisher PD

A formal evaluation of the performance of different corporate styles in stable and turbulent environments

The notion of "parenting styles", introduced by Goold, Campbell and Alexander, has been widely acknowledged by the Corporate Strategy literature as a good broad description of the different ways in which corporate managers choose to manage and organize multibusiness firms. The purpose of this paper is to present a formal test of the relationship between parenting style and performance. For this test, we developed a set of agent-based simulations using the Performance Landscapes framework, which captures and describes the evolution of firms led by different parenting styles in business environments with different levels of complexity and dynamism. We found that the relative performance of each style is contingent upon the characteristics of the environment in which the firm operates. In less complex business environments, the Strategic Planning style outperforms the Strategic Control and Financial Control styles. In highly complex and highly dynamic environments, by contrast, the Strategic Control style performs best. Our results also demonstrate the importance of planning and flexibility at the corporate level and so contribute to the wider debate on Strategic Planning vs. Emergent Strategies.Corporate strategy; Parenting styles; Agent-based models;

Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks

Recent developments in Deep Learning are noteworthy when it comes to learning the probability distribution of points through neural networks, and one of the crucial parts for such progress is because of Generative Adversarial Networks (GANs). In GANs, two neural networks, Generator and Discriminator, compete amongst each other to learn the probability distribution of points in visual pictures. A lot of research has been conducted to overcome the challenges of GANs which include training instability, mode collapse and vanishing gradient. However, there was no significant proof found on whether modern techniques consistently outperform vanilla GANs, and it turns out that different advanced techniques distinctively perform on different datasets. In this thesis, we propose two neuroevolutionary training techniques for deep convolutional GANs. We evolve the deep GANs architecture in low data regime. Using Fréchet Inception Distance (FID) score as the fitness function, we select the best deep convolutional topography generated by the evolutionary algorithm. The parameters of the best-selected individuals are maintained throughout the generations, and we continue to train the population until individuals demonstrate convergence. We compare our approach with the Vanilla GANs, Deep Convolutional GANs and COEGAN. Our experiments show that an evolutionary algorithm-based training technique gives a lower FID score than those of benchmark models. A lower FID score results in better image quality and diversity in the generated images