Is the integrate-and-fire model good enough?--a review

Area of Science:

• Computational neuroscience
• Neuronal modeling

Background:

• Neuronal models like integrate-and-fire (IF), FitzHugh-Nagumo (FHN), and Hodgkin-Huxley (HH) are crucial for understanding neural computation.
• Investigating the impact of correlated synaptic inputs and inhibitory effects is essential for realistic neuronal simulations.

Purpose of the Study:

• To analyze the behavior of IF, FHN, IF-FHN, and HH models under correlated inputs.
• To explore the phenomenon of inhibition-boosted firing (IBF) in neuronal models.
• To evaluate the IF-FHN model as a computationally efficient yet biophysically relevant alternative.

Main Methods:

• Simulations and theoretical analysis of IF, FHN, IF-FHN, and HH neuronal models.
• Investigating responses to correlated synaptic inputs modeled as diffusion approximations.
• Examining the effects of inhibitory inputs on neuronal firing rates.

Main Results:

• The IF and HH models exhibit opposing responses to correlated inputs.
• The IF-FHN model demonstrates behavior similar to the HH model.
• Increasing inhibitory input can lead to increased firing rates (IBF) in FHN and HH models.
• Theoretical conditions for IBF were derived using IF and IF-FHN models.

Conclusions:

• The IF-FHN model offers a balance between computational simplicity and biophysical realism.
• Understanding IBF provides insights into complex neuronal dynamics.
• Correlated inputs significantly influence neuronal model output, with model-specific variations.