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Artificial Brains: Simulation and Emulation of Neural Networks

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Form Versus Function: Theory and Models for Neuronal Substrates

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Abstract

When describing increasingly complex systems, the required array of equations equivalently grows in size and complexity. In many (usually simple) cases, statistical methods can be applied to distill macroscopic equations from those governing the microscopic components of a system, with thermodynamics offering a paradigmatic example.

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Notes

  1. 1.

    Here, complexity can be understood both as number of constituent components, as well as concerning the nature of the equations describing their dynamics and interactions.

  2. 2.

    Strictly speaking, \({E_\mathrm {l}} \) is of course not a reversal potential in the same sense as \({E^\mathrm {rev}_\mathrm {e}} \) and \({E^\mathrm {rev}_\mathrm {i}} \) (see Sect. 2.1.1). However, from a purely mathematical perspective, these three parameters have equivalent contributions to the LIF Eq. 2.54.

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  1. Department of Electronic Vision(s), Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany

    Mihai Alexandru Petrovici

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Petrovici, M.A. (2016). Artificial Brains: Simulation and Emulation of Neural Networks. In: Form Versus Function: Theory and Models for Neuronal Substrates . Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-39552-4_3

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