Neuron-glial Interactions

Although lagging behind classical computational neuroscience, theoretical and computational approaches are beginning to emerge to characterize different aspects of neuron-glial interactions. This chapter aims to provide essential knowledge on neuron-glial interactions in the mammalian brain, leveraging on computational studies that focus on structure (anatomy) and function (physiology) of such interactions in the healthy brain. Although our understanding of the need of neuron-glial interactions in the brain is still at its infancy, being mostly based on predictions that await for experimental validation, simple general modeling arguments borrowed from control theory are introduced to support the importance of including such interactions in traditional neuron-based modeling paradigms.Junior Leader Fellowship Program by “la Caixa” Banking Foundation (LCF/BQ/LI18/11630006

A Neuron-Glial Perspective for Computational Neuroscience

International audienceThere is growing excitement around glial cells, as compelling evidence point to new, previously unimaginable roles for these cells in information processing of the brain, with the potential to affect behavior and higher cognitive functions. Among their many possible functions, glial cells could be involved in practically every aspect of the brain physiology in health and disease. As a result, many investigators in the field welcome the notion of a Neuron-Glial paradigm of brain function, as opposed to Ramon y Cayal's more classical neuronal doctrine which identifies neurons as the prominent, if not the only, cells capable of a signaling role in the brain. The demonstration of a brain-wide Neuron-Glial paradigm however remains elusive and so does the notion of what neuron-glial interactions could be functionally relevant for the brain computational tasks. In this perspective, we present a selection of arguments inspired by available experimental and modeling studies with the aim to provide a biophysical and conceptual platform to computational neuroscience no longer as a mere prerogative of neuronal signaling but rather as the outcome of a complex interaction between neurons and glial cells

Neuron-Glial Interactions

Although lagging behind classical computational neuroscience, theoretical and computational approaches are beginning to emerge to characterize different aspects of neuron-glial interactions. This chapter aims to provide essential knowledge on neuron-glial interactions in the mammalian brain, leveraging on computational studies that focus on structure (anatomy) and function (physiology) of such interactions in the healthy brain. Although our understanding of the need of neuron-glial interactions in the brain is still at its infancy, being mostly based on predictions that await for experimental validation, simple general modeling arguments borrowed from control theory are introduced to support the importance of including such interactions in traditional neuron-based modeling paradigms.Comment: 43 pages, 2 figures, 1 table. Accepted for publication in the "Encyclopedia of Computational Neuroscience," D. Jaeger and R. Jung eds., Springer-Verlag New York, 2020 (2nd edition

Algorithm configuration using GPU-based metaheuristics

In this paper, a GPU-based implementation of Differential Evolution (DE) and Particle Swarm Optimization (PSO) in CUDA is used to automatically tune the parameters of PSO. The parameters were tuned over a set of 8 problems and then tested over 20 problems to assess the generalization ability of the tuners. We compare the results obtained using such parameters with the 'standard' ones proposed in the literature and the ones obtained by state-of-the-art tuning methods (irace). The results are comparable to the ones suggested for the standard version of PSO (SPSO), and the ones obtained by irace, while the GPU implementation makes tuning time acceptable. To the best of our knowledge, this is the first time that a general purpose library of GPU-based metaheuristics is used to solve this problem, as well as being one of the few cases where DE and PSO are both used as tuners

libCudaOptimize: an Open Source Library of GPU-based Metaheuristics

Evolutionary Computation techniques and other metaheuristics have been increasingly used in the last years for solving many real-world tasks that can be formulated as optimization problems. Among their numerous strengths, a major one is their natural predisposition to parallelization. In this paper, we introduce libCudaOptimize, an open source library which implements some metaheuristics for continuous optimization: presently Particle Swarm Optimization, Differential Evolution, Scatter Search, and Solis&Wets local search. This library allows users either to apply these metaheuristics directly to their own fitness function or to extend it by implementing their own parallel optimization techniques. The library is written in CUDA-C to make extensive use of parallelization, as allowed by Graphics Processing Units. After describing the library, we consider two practical case studies: the optimization of a fitness function for the automatic localization of anatomical brain structures in histological images, and the parallel implementation of Simulated Annealing as a new module, which extends the library while keeping code compatibility with it, so that the new method can be readily available for future use within the library as an alternative optimization technique

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, image-related tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Automatic hippocampus localization in histological images using PSO-based deformable models

The Allen Brain Atlas (ABA) is a cellular-resolution, genome-wide map of gene expression in the mouse brain which allows users to compare gene expression patterns in neuroanatomical structures. The correct localization of the structures is the first step to carry on this comparison in an automatic way. In this paper we present a completely automatic tool for the localization of the hippocampus that can be easily adapted also to other subcortical structures. This goal is achieved in two distinct phases. The first phase, called "best reference slice selection", is performed by comparing the image of the brain with a reference Atlas provided by ABA using a two-step affine registration. By doing so the system is able to automatically find to which brain section the image corresponds and wherein the image the hippocampus is roughly located. The second phase, the proper "hippocampus localization", is based on a method that combines Particle Swarm Optimization (PSO) and a novel technique inspired by Active Shape Models (ASMs). The hippocampus is found by adapting a deformable model derived statistically, in order to make it overlap with the hippocampus image. Experiments on a test set of 120 images yielded a perfect or good localization in 89.2% of cases

Automatic Hippocampus Localization in Histological Images using Differential Evolution-Based Deformable Models

In this paper, the localization of structures in biomedical images is considered as a multimodal global continuous optimization problem and solved by means of soft computing techniques. We have developed an automatic method aimed at localizing the hippocampus in histological images, after discoveries indicating the relevance of structural changes of this region as early biomarkers for Alzheimer’s disease and epilepsy. The localization is achieved by searching the parameters of an empirically-derived deformable model of the hippocampus which maximize its overlap with the corresponding anatomical structure in histological brain images. The comparison between six real-parameter optimization techniques (Levenberg-Marquardt, Differential Evolution, Simulated Annealing, Genetic Algorithms, Particle Swarm Optimization and Scatter Search) shows that Differential Evolution significantly outperforms the other techniques in this task, providing successful localizations in 90.9% and 93.0% of two test sets of real and synthetic images, respectively