Robust modulation of integrate-and-fire models

TL;DR

This paper introduces the MQIF model, a computationally efficient and physiologically interpretable neuron model suitable for large-scale neuromodulation studies, bridging the gap between conductance-based and integrate-and-fire models.

Contribution

The paper presents the MQIF model as a novel, scalable neuron model that captures neuromodulation effects with high physiological relevance and computational efficiency.

Findings

MQIF model effectively simulates neuromodulation effects.

It combines simplicity of integrate-and-fire with physiological detail.

Suitable for large-scale neural network simulations.

Abstract

By controlling the state of neuronal populations, neuromodulators ultimately affect behaviour. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied via conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This paper studies the modulation properties of the Multi-Quadratic Integrate-and-Fire (MQIF) model, a generalisation of the classical Quadratic Integrate-and-Fire (QIF) model. The model is shown to combine the computational economy of integrate-and-fire modelling and the physiological interpretability of conductance-based modelling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks.