Numerical methods for neuronal modeling

TL;DR: This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.

TL;DR: This work presents the basic ideas that would help informed users make the most efficient use of NEURON, the powerful and flexible environment for implementing models of individual neurons and small networks of neurons.

TL;DR: This work presents the basic ideas that would help informed users make the most efficient use of NEURON, the powerful and flexible environment for implementing models of individual neurons and small networks of neurons.

TL;DR: It is shown that a saddle-node bifurcation is not required to see stochastic resonance ~SR! without periodic input when there exists a stable deterministic subthreshold oscillation.

TL;DR: This well written book is enlarged by the following topics: B-splines and their computation, elimination methods for large sparse systems of linear equations, Lanczos algorithm for eigenvalue problems, implicit shift techniques for theLR and QR algorithm, implicit differential equations, differential algebraic systems, new methods for stiff differential equations and preconditioning techniques.

TL;DR: This article concludes a series of papers concerned with the flow of electric current through the surface membrane of a giant nerve fibre by putting them into mathematical form and showing that they will account for conduction and excitation in quantitative terms.

TL;DR: In this paper, the authors present methods of evaluating numerical solutions of the non-linear partial differential equation to the boundary conditions A, k, q are known constants, where q is the rate of heat generation.