Titlebook: Computational Modelling of the Brain; Modelling Approaches Michele Giugliano,Mario Negrello,Daniele Linaro Book 2022 Springer Nature Switze

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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.

現(xiàn)實

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.

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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.

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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.

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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.

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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