Abstract
We demonstrate that single-variable integrate-and-fire models can quantitatively capture the dynamics of a physiologically detailed model for fast-spiking cortical neurons. Through a systematic set of approximations, we reduce the conductance-based model to 2 variants of integrate-and-fire models. In the first variant (nonlinear integrate-and-fire model), parameters depend on the instantaneous membrane potential, whereas in the second variant, they depend on the time elapsed since the last spike [ Spike Response Model (SRM)]. The direct reduction links features of the simple models to biophysical features of the full conductance-based model. To quantitatively test the predictive power of the SRM and of the nonlinear integrate-and-fire model, we compare spike trains in the simple models to those in the full conductance-based model when the models are subjected to identical randomly fluctuating input. For random current input, the simple models reproduce 70-80 percent of the spikes in the full model ( with temporal precision of +/-2 ms) over a wide range of firing frequencies. For random conductance injection, up to 73 percent of spikes are coincident. We also present a technique for numerically optimizing parameters in the SRM and the nonlinear integrate-and-fire model based on spike trains in the full conductance-based model. This technique can be used to tune simple models to reproduce spike trains of real neurons.
Original language | English |
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Pages (from-to) | 959-976 |
Number of pages | 18 |
Journal | Journal of Neurophysiology |
Volume | 92 |
Issue number | 2 |
DOIs | |
Publication status | Published - Aug 2004 |
Externally published | Yes |
Keywords
- ASYNCHRONOUS STATES
- CORTICAL-NEURONS
- NETWORKS
- RELIABILITY
- COMPUTATION
- EXCITATION
- REDUCTION
- SYNCHRONY
- DYNAMICS
- HODGKIN