Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Reduction of conductance-based neuron models
Biological Cybernetics
Properties of a bursting model with two slow inhibitory variables
SIAM Journal on Applied Mathematics
Reduction of conductance-based models with slow synapses to neural nets
Neural Computation
Conductance-based integrate-and-fire models
Neural Computation
The NEURON simulation environment
Neural Computation
Simulating, Analyzing, and Animating Dynamical Systems: A Guide Toi Xppaut for Researchers and Students
Expanding NEURON’s Repertoire of Mechanisms with NMODL
Neural Computation
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We present a reduction of a Hodgkin-Huxley (HH)-style bursting model to a hybridized integrate-and-fire (IF) formalism based on a thorough bifurcation analysis of the neuron's dynamics. The model incorporates HH-style equations to evolve the subthreshold currents and includes IF mechanisms to characterize spike events and mediate interactions between the subthreshold and spiking currents. The hybrid IF model successfully reproduces the dynamic behavior and temporal characteristics of the full model over a wide range of activity, including bursting and tonic firing. Comparisons of timed computer simulations of the reduced model and the original model for both single neurons and moderately sized networks (n ≤ 500) show that this model offers improvement in computational speed over the HH-style bursting model.