The asymmetric particle population density method for simulation of coupled noisy oscillators
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A wide variety of biological phenomena can be modeled by the collective activity of a population of individual units. A common strategy for simulating such a system, the population density approach, is to take the macroscopic limit and update its population density function. However, in many cases, the coupling between the units and noise gives rise to complex behaviors challenging to existing population density approach methods. To address these challenges, we develop the asymmetric particle population density (APPD) method that efficiently and accurately simulates such populations consist of coupled elements. The APPD is well-suited for a parallel implementation. We compare the performance of the method against direct Monte-Carlo simulation and verify its accuracy by applying it to the well-studied Hodgkin-Huxley model, with a range of challenging scenarios. We find that our method can accurately reproduce complex macroscopic behaviors such as inhibitory coupling-induced clustering and noise-induced firing while being faster than the direct simulation.
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