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

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

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

Technical Note: A comparison of point set registration methods for electromagnetic tracking

Purpose High dose rate brachytherapy applies intense and destructive radiation. A treatment plan defines radiation source dwell positions to avoid irradiating healthy tissue. The study discusses methods to quantify any positional changes of source locations along the various treatment sessions. Methods Electromagnetic tracking (EMT) localizes the radiation source during the treatment sessions. But in each session the relative position of the patient relative to the filed generator is changed. Hence, the measured dwell point sets need to be registered onto each other to render them comparable. Two point set registration techniques are compared: a probabilistic method called coherent point drift (CPD) and a multidimensional scaling (MDS) technique. Results Both enable using EMT without external registration and achieve very similar results with respect to dwell position determination of the radiation source. Still MDS achieves smaller grand average deviations (CPD-rPSR: MD = 2.55 mm, MDS-PSR: MD = 2.15 mm) between subsequent dwell position determinations, which also show less variance (CPD-rPSR: IQR = 4 mm, MDS-PSR: IQR = 3 mm). Furthermore, MDS is not based on approximations and does not need an iterative procedure to track sensor positions inside the implanted catheters. Conclusion Although both methods achieve similar results, MDS is to be preferred over rigid CPD while nonrigid CPD is unsuitable as it does not preserve topology