A Sparse Reformulation of the Green's Function Formalism Allows Efficient Simulations of Morphological Neuron Models.
We prove that when a class of partial differential equations, generalized from the cable equation, is defined on tree graphs and the inputs are restricted to a spatially discrete, well chosen set of points, the Green's function (GF) formalism can be rewritten to scale as O(n) with the number n of inputs locations, contrary to the previously reported O(n(2)) scaling. We show that the linear scaling can be combined with an expansion of the remaining kernels as sums of exponentials to allow efficient simulations of equations from the aforementioned class. We furthermore validate this simulation paradigm on models of nerve cells and explore its relation with more traditional finite difference approaches. Situations in which a gain in computational performance is expected are discussed.
Blue Brain Project, Brain Mind Institute, EPFL, Geneva 1202, Switzerland willem.wybo@epfl.ch.
Willem A M
WA
Wybo
Chair of Geometry, Mathematics Institute for Geometry and Applications, EPFL, Lausanne 1015, Switzerland daniele.boccalini@epfl.ch.
Daniele
D
Boccalini
Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa 1919-1, Japan btorbennielsen@gmail.com.
Benjamin
B
Torben-Nielsen
Blue Brain Project, Brain Mind Institute, EPFL, Geneva 1202, Switzerland marc-oliver.gewaltig@epfl.ch.
Marc-Oliver
MO
Gewaltig
D000465:Algorithms; D001369:Axons / Q000502:physiology; D003198:Computer Simulation; D003712:Dendrites / Q000502:physiology*; D016014:Linear Models; D008959:Models, Neurological; D009413:Nerve Fibers, Myelinated / Q000502:physiology; D017711:Nonlinear Dynamics