On the simulation of nonlinear bidimensional spiking neuron models
Jonathan Touboul

TL;DR
This paper analyzes the limitations of fixed time-step methods for simulating nonlinear bidimensional spiking neuron models and introduces a variable step algorithm to improve accuracy and efficiency.
Contribution
The paper proposes a novel variable step simulation algorithm for bidimensional spiking neuron models, addressing the inaccuracies of fixed step methods.
Findings
Fixed time-step methods produce unbounded errors at spike times.
The new variable step algorithm improves accuracy and computational efficiency.
Simulation results outperform traditional fixed step schemes.
Abstract
Bidimensional spiking models currently gather a lot of attention for their simplicity and their ability to reproduce various spiking patterns of cortical neurons, and are particularly used for large network simulations. These models describe the dynamics of the membrane potential by a nonlinear differential equation that blows up in finite time, coupled to a second equation for adaptation. Spikes are emitted when the membrane potential blows up or reaches a cutoff value. The precise simulation of the spike times and of the adaptation variable is critical for it governs the spike pattern produced, and is hard to compute accurately because of the exploding nature of the system at the spike times. We thoroughly study the precision of fixed time-step integration schemes for this type of models and demonstrate that these methods produce systematic errors that are unbounded, as the cutoff…
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