Analog VLSI and neural systems
Analog VLSI and neural systems
The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
Numerical Recipes in FORTRAN: The Art of Scientific Computing
Numerical Recipes in FORTRAN: The Art of Scientific Computing
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Programmable Logic Construction Kits for Hyper-Real-Time Neuronal Modeling
Neural Computation
Encoding of high dynamic range video with a model of human cones
ACM Transactions on Graphics (TOG)
International Journal of Parallel, Emergent and Distributed Systems
Cardinal exponential splines: part II - think analog, act digital
IEEE Transactions on Signal Processing
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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.