標(biāo)題: Titlebook: Computational Modelling of the Brain; Modelling Approaches Michele Giugliano,Mario Negrello,Daniele Linaro Book 2022 Springer Nature Switze [打印本頁] 作者: bradycardia 時間: 2025-3-21 16:24
書目名稱Computational Modelling of the Brain影響因子(影響力)
書目名稱Computational Modelling of the Brain影響因子(影響力)學(xué)科排名
書目名稱Computational Modelling of the Brain網(wǎng)絡(luò)公開度
書目名稱Computational Modelling of the Brain網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Modelling of the Brain被引頻次
書目名稱Computational Modelling of the Brain被引頻次學(xué)科排名
書目名稱Computational Modelling of the Brain年度引用
書目名稱Computational Modelling of the Brain年度引用學(xué)科排名
書目名稱Computational Modelling of the Brain讀者反饋
書目名稱Computational Modelling of the Brain讀者反饋學(xué)科排名
作者: 不溶解 時間: 2025-3-21 22:43
Advances in Experimental Medicine and Biologyhttp://image.papertrans.cn/c/image/232836.jpg作者: 正式演說 時間: 2025-3-22 00:38
Computational Modelling of the Brain978-3-030-89439-9Series ISSN 0065-2598 Series E-ISSN 2214-8019 作者: MILL 時間: 2025-3-22 07:51
https://doi.org/10.1007/b138114This chapter gives a short overview of computational models dealing with two fundamental building blocks in spatial cognition: grid and place cells, and of the open issues such models may help address.作者: Expiration 時間: 2025-3-22 12:48
Challenges for Place and Grid Cell ModelsThis chapter gives a short overview of computational models dealing with two fundamental building blocks in spatial cognition: grid and place cells, and of the open issues such models may help address.作者: 雀斑 時間: 2025-3-22 16:33 作者: 雀斑 時間: 2025-3-22 17:24 作者: 鐵塔等 時間: 2025-3-22 22:16 作者: 售穴 時間: 2025-3-23 01:43 作者: acetylcholine 時間: 2025-3-23 06:24 作者: 案發(fā)地點 時間: 2025-3-23 10:23 作者: 阻塞 時間: 2025-3-23 16:24 作者: 憤慨一下 時間: 2025-3-23 18:53
https://doi.org/10.1007/978-1-4615-0759-8efficiency and a reduction of the number of parameters involved against biological realism. Simulations of point-model neurons show very realistic features of neural dynamics but are very hard to configure and to analyse. Instead of using hundreds or thousands of point-model neurons, a population ca作者: Magisterial 時間: 2025-3-23 23:35 作者: 強行引入 時間: 2025-3-24 05:23
https://doi.org/10.1007/978-1-4615-0759-8y a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the 作者: 燦爛 時間: 2025-3-24 07:01
R. M. Potvliege,Philip H. G. Smith and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also 作者: 亞麻制品 時間: 2025-3-24 12:46 作者: nerve-sparing 時間: 2025-3-24 17:12 作者: 加劇 時間: 2025-3-24 20:41 作者: FLAX 時間: 2025-3-25 00:44 作者: IRS 時間: 2025-3-25 03:25 作者: 反饋 時間: 2025-3-25 11:31 作者: Tdd526 時間: 2025-3-25 11:57 作者: Afflict 時間: 2025-3-25 19:46
https://doi.org/10.1007/b138114d clinical directions. In this chapter we offer a potted Past, Present, and Future of whole-brain modelling, noting what we take to be some of its greatest successes, hardest challenges, and most exciting opportunities.作者: 現(xiàn)實 時間: 2025-3-25 23:49
A User’s Guide to Generalized Integrate-and-Fire Modelsvious exposure to modelling a clear understanding of the strengths and limitations of GIF models, along with the mathematical intuitions required to digest more detailed and technical treatments of this topic.作者: Custodian 時間: 2025-3-26 01:28
Neuron–Glia Interactions and Brain Circuitsns they have with the neurons. In this chapter, we introduce the biology of neuron–glia interactions, summarize the existing computational models and tools, and emphasize the glial properties that may be important in modeling brain functions in the future.作者: N防腐劑 時間: 2025-3-26 04:34
Computing Extracellular Electric Potentials from Neuronal Simulationstion dynamics in the extracellular medium, and we show what assumptions must be introduced to get the VC theory on the simplified form that is commonly used by neuroscientists. Furthermore, we provide examples of how the theory is applied to compute spikes, LFP signals, and EEG signals generated by neurons and neuronal populations.作者: 傀儡 時間: 2025-3-26 09:55
Whole-Brain Modelling: Past, Present, and Futured clinical directions. In this chapter we offer a potted Past, Present, and Future of whole-brain modelling, noting what we take to be some of its greatest successes, hardest challenges, and most exciting opportunities.作者: Coronation 時間: 2025-3-26 15:08 作者: 荒唐 時間: 2025-3-26 19:12
Modeling Dendrites and Spatially-Distributed Neuronal Membrane Propertiesdendrites. Dendrites were first discovered at the beginning of the twentieth century and were characterized by great anatomical variability, both within and across species. Over the past years, a rich repertoire of active and passive dendritic mechanisms has been unveiled, which greatly influences t作者: asthma 時間: 2025-3-26 21:13 作者: 代替 時間: 2025-3-27 03:28
Neuron–Glia Interactions and Brain Circuitsample, astrocytes, one type of glial cells, have been shown to promote coordinated activation of neuronal networks, modulate sensory-evoked neuronal network activity, and influence brain state transitions during development. This reinforces the idea that astrocytes not only provide the “housekeeping作者: amygdala 時間: 2025-3-27 08:58 作者: 骨 時間: 2025-3-27 11:39 作者: flimsy 時間: 2025-3-27 15:31
Computing Extracellular Electric Potentials from Neuronal Simulationsty is an important tool for understanding such measurements. Volume conductor (VC) theory is used to compute extracellular electric potentials stemming from neural activity, such as extracellular spikes, multi-unit activity (MUA), local field potentials (LFP), electrocorticography (ECoG), and electr作者: Ccu106 時間: 2025-3-27 17:48 作者: HACK 時間: 2025-3-27 23:09 作者: 紳士 時間: 2025-3-28 02:40
Reconstruction of the Hippocampusion. The amount of data on this region increases every day and delineates a complex and fragmented picture, but an integrated understanding of hippocampal function remains elusive. Computational methods can help to move the research forward, and reconstructing a full-scale model of the hippocampus i作者: outrage 時間: 2025-3-28 09:24
Whole-Brain Modelling: Past, Present, and Futurek to as early as the 1940s. It was not until the late 2000s, however, that a nascent paradigm emerged in roughly its current form—concurrently, and in many ways joined at the hip, with its sister field of macro-connectomics. This period saw a handful of seminal papers authored by a certain motley cr作者: stressors 時間: 2025-3-28 10:32 作者: 安慰 時間: 2025-3-28 16:36
References and Additional Readings,s may be familiar with 1D models yet unfamiliar with the more detailed 3D description of neurons. As such, this chapter introduces some of the techniques used in detailed 3D, molecular modeling, and shows the steps required for building such models from a foundation of the more familiar 1D descripti作者: 許可 時間: 2025-3-28 20:46
https://doi.org/10.1007/b138009ght on how dendrites contribute to neuronal and circuit computations. This chapter aims to help the interested reader build biophysical models incorporating dendrites by detailing how their electrophysiological properties can be described using simple mathematical frameworks. We start by discussing 作者: cliche 時間: 2025-3-29 00:25 作者: 發(fā)展 時間: 2025-3-29 03:32 作者: Omnipotent 時間: 2025-3-29 09:49
https://doi.org/10.1007/978-1-4615-0759-8n quantities. Exploiting organizational principles that link the plethora of data in a unifying framework can be useful for informing computational models. Besides overarching principles, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal 作者: agglomerate 時間: 2025-3-29 15:09 作者: OCTO 時間: 2025-3-29 17:52 作者: indubitable 時間: 2025-3-29 19:59
Modeling Neurons in 3D at the Nanoscales may be familiar with 1D models yet unfamiliar with the more detailed 3D description of neurons. As such, this chapter introduces some of the techniques used in detailed 3D, molecular modeling, and shows the steps required for building such models from a foundation of the more familiar 1D descripti作者: Projection 時間: 2025-3-30 01:18
Modeling Dendrites and Spatially-Distributed Neuronal Membrane Propertiesght on how dendrites contribute to neuronal and circuit computations. This chapter aims to help the interested reader build biophysical models incorporating dendrites by detailing how their electrophysiological properties can be described using simple mathematical frameworks. We start by discussing 作者: Immobilize 時間: 2025-3-30 07:32
The Mean Field Approach for Populations of Spiking Neuronsequations for populations of integrate-and-fire neurons. An effort is made to derive the main equations of the theory using only elementary methods from calculus and probability theory. The chapter ends with a discussion of the assumptions of the theory and some of the consequences of violating thos作者: HIKE 時間: 2025-3-30 11:30 作者: breadth 時間: 2025-3-30 13:34
Bringing Anatomical Information into Neuronal Network Modelsn quantities. Exploiting organizational principles that link the plethora of data in a unifying framework can be useful for informing computational models. Besides overarching principles, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal 作者: BROOK 時間: 2025-3-30 20:13 作者: Mawkish 時間: 2025-3-30 23:21
Reconstruction of the Hippocampuse-scale hippocampus models. A large-scale model of the hippocampus is a compound model of several building blocks: ion channels, morphologies, single cell models, connections, synapses. We discuss each of those building blocks separately and discuss how to merge them back and simulate the resulting 作者: 一起平行 時間: 2025-3-31 01:16
