Receptor-aware master-equation mean-field models can bridge molecular perturbations to whole-brain dynamics, but only within a restricted validity domain and with first-order truncations that discard covariance dynamics.
Some asymptotics for the Bessel functions with an explicit error term
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abstract
We show how one can obtain an asymptotic expression for some special functions satisfying a second order differential equation with a very explicit error term starting from appropriate upper bounds. We will work out the details for the Bessel function $J_\nu (x)$ and the Airy function $Ai(x)$ and find a sharp approximation for their zeros. We also answer the question raised by Olenko by showing that $$c_1 | \nu^2-1/4\,| < \sup_{x \ge 0} x^{3/2}|J_\nu(x)-\sqrt{\frac{2}{\pi x}} \, \cos (x-\frac{\pi \nu}{2}-\frac{\pi}{4}\,)| <c_2 |\nu^2-1/4\,|, $$ $ \nu \ge -1/2 \, ,$ for some explicit numerical constants $c_1$ and $c_2.$
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Mechanistic bridges from receptors to whole-brain dynamics: mean-field reductions, validity domains, and computational trade-offs
Receptor-aware master-equation mean-field models can bridge molecular perturbations to whole-brain dynamics, but only within a restricted validity domain and with first-order truncations that discard covariance dynamics.