A Generic Approach to Solving Jump Diffusion Equations with Applications to Neural Populations

Abstract: Diffusion processes have been applied with great success to model the dynamics of large populations throughout science, in particular biology. One advantage is that they bridge two different scales: the microscopic and the macroscopic one. Diffusion is a mathematical idealisation, however: it assumes vanishingly small state changes at the microscopic level. In real biological systems this is often not the case. The differential Chapman-Kolmogorov equation is more appropriate to model population dynamics that is not well described by drift and diffusion alone. Here, the method of characteristics is used to transform deterministic dynamics away and find a coordinate frame where this equation reduces to a Master equation. There is no longer a drift term, and solution methods there are insensitive to density gradients, making the method suitable for jump processes with arbitrary jump sizes. Moreover, its solution is universal: it no longer depends explicitly on the deterministic system. We demonstrate the technique on simple models of neuronal populations. Surprisingly, it is suitable for fast neural dynamics, even though in the new coordinate frame state space may expand rapidly towards infinity. We demonstrate universality: the method is applicable to any one dimensional neural model and show this on populations of leaky- and quadratic-integrate-and-fire neurons. In the diffusive limit, excellent approximations of Fokker-Planck equations are achieved. Nothing in the approach is particular to neuroscience, and the neural models are simple enough to serve as an example of dynamical systems that demonstrates the method.