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Titlebook: BioInformation Processing; A Primer on Computat James K. Peterson Book 2016 The Editor(s) (if applicable) and The Author(s), under exclusiv

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發(fā)表于 2025-3-21 19:31:42 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱BioInformation Processing
期刊簡稱A Primer on Computat
影響因子2023James K. Peterson
視頻videohttp://file.papertrans.cn/187/186414/186414.mp4
發(fā)行地址Uses the principles of computation in neural systems to develop approximations for neural computation.Proposes network models of computation based on models of emotion arising from musical and paintin
學(xué)科分類Cognitive Science and Technology
圖書封面Titlebook: BioInformation Processing; A Primer on Computat James K. Peterson Book 2016 The Editor(s) (if applicable) and The Author(s), under exclusiv
影響因子This book shows how mathematics, computer science and science can be usefully and seamlessly intertwined. It begins with a general model of cognitive processes in a network of computational nodes, such as neurons, using a variety of tools from mathematics, computational science and neurobiology. It then moves on to solve the diffusion model from a low-level random walk point of view. It also demonstrates how this idea can be used in a new approach to solving the cable equation,?in order to better understand the neural computation approximations. It introduces specialized data for emotional content, which allows a brain model to be built using MatLab tools, and also highlights a simple model of cognitive dysfunction.
Pindex Book 2016
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Mammalian Neural Structureof brain modules such as cortex and thalamus among others. Since the brain is a three dimensional structure, understanding how these modules interact is difficult to visualize, so simple cartoon images of how various brain structures relate to others is presented through two dimensional slice inform
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Emotional Modelsual image was associated with a skin conductance value and a single functional MRI value. Different images then give rise to skin conductance and fMRI value pairs that were strongly clustered in that two dimensional space. Thus, sad, angry, happy and neutral visual data would give rise to separated
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Modeling Compositional Designessing ideas and instead are based on models built from nodes connected by links with simple scalar values. Each node processes information using a sigmoidal strategy and the values on the links between nodes are used to store information about the process being modeled. Elaborate connectionist desi
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