On the Capabilities and Computational Costs of Neuron Models
| Autor: | Lyle N. Long, Michael J. Skocik |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Neurons
Quantitative Biology::Neurons and Cognition Computer Networks and Communications Computer science Computation Models Neurological Action Potentials Bioinformatics Ion Channels Membrane Potentials Computer Science Applications Exponential function symbols.namesake medicine.anatomical_structure Artificial Intelligence medicine Euler's formula symbols Animals Applied mathematics Computer Simulation Neuron Ion Channel Gating Algorithms Software |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems. 25:1474-1483 |
| ISSN: | 2162-2388 2162-237X |
| Popis: | We review the Hodgkin-Huxley, Izhikevich, and leaky integrate-and-fire neuron models in regular spiking modes solved with the forward Euler, fourth-order Runge-Kutta, and exponential Euler methods and determine the necessary time steps and corresponding computational costs required to make the solutions accurate. We conclude that the leaky integrate-and-fire needs the least number of computations, and that the Hodgkin-Huxley and Izhikevich models are comparable in computational cost. |
| Databáze: | OpenAIRE |
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