安慰
发表于 2025-3-28 16:36:47
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
许可
发表于 2025-3-28 20:46:34
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
cliche
发表于 2025-3-29 00:25:26
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发展
发表于 2025-3-29 03:32:59
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Omnipotent
发表于 2025-3-29 09:49:29
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
agglomerate
发表于 2025-3-29 15:09:07
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OCTO
发表于 2025-3-29 17:52:33
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indubitable
发表于 2025-3-29 19:59:08
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
Projection
发表于 2025-3-30 01:18:02
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
Immobilize
发表于 2025-3-30 07:32:18
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