Abstract:
:A fast and accurate computational scheme for simulating nonlinear dynamic systems is presented. The scheme assumes that the system can be represented by a combination of components of only two different types: first-order low-pass filters and static nonlinearities. The parameters of these filters and nonlinearities may depend on system variables, and the topology of the system may be complex, including feedback. Several examples taken from neuroscience are given: phototransduction, photopigment bleaching, and spike generation according to the Hodgkin-Huxley equations. The scheme uses two slightly different forms of autoregressive filters, with an implicit delay of zero for feedforward control and an implicit delay of half a sample distance for feedback control. On a fairly complex model of the macaque retinal horizontal cell, it computes, for a given level of accuracy, one to two orders of magnitude faster than the fourth-order Runge-Kutta. The computational scheme has minimal memory requirements and is also suited for computation on a stream processor, such as a graphical processing unit.
journal_name
Neural Computjournal_title
Neural computationauthors
van Hateren JHdoi
10.1162/neco.2008.04-07-506subject
Has Abstractpub_date
2008-07-01 00:00:00pages
1821-46issue
7eissn
0899-7667issn
1530-888Xjournal_volume
20pub_type
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journal_title:Neural computation
pub_type: 杂志文章
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