Characterizing spiking in noisy type II neurons

Abstract

Understanding the dynamics of noisy neurons remains an important challenge in neuroscience. Here, we describe a simple probabilistic model that accurately describes the firing behavior in a large class (type II) of neurons. To demonstrate the usefulness of this model, we show how it accurately predicts the interspike interval (ISI) distributions, bursting patterns and mean firing rates found by: (1) simulations of the classic Hodgkin-Huxley model with channel noise, (2) experimental data from squid giant axon with a noisy input current and (3) experimental data on noisy firing from a neuron within the suprachiasmatic nucleus (SCN). This simple model has 6 parameters, however, in some cases, two of these parameters are coupled and only 5 parameters account for much of the known behavior. From these parameters, many properties of spiking can be found through simple calculation. Thus, we show how the complex effects of noise can be understood through a simple and general probabilistic model.

Angiotensin II induces coordinated calcium bursts in aldosterone-producing adrenal rosettes

“…To evaluate the robustness of this unanticipated result, an alternative thresholding approach was used 30 ( Supplementary Fig. 1).…”

“…A separate method for fitting the inter-event interval distribution with a series of Gaussian distributions based on Bodova et al 30 was also implemented (see equation for Supplementary Fig. 1b).…”

Unraveling the stochastic transition mechanism between oscillation states by the landscape and the minimum action path theory

“…Mathematical modeling has been applied to study the dynamical properties of biological systems. The systems with sustained oscillations have been studied before, 5,31–33,48–53 but the properties of the transition between oscillations have not been fully understood. In this work, we seek to explore the transition mechanisms of multiple oscillation systems based on landscape and minimum action theory.…”

Minimum Action Path Theory Reveals the Details of Stochastic Transitions Out of Oscillatory States

“…Relevant examples including sustained oscillations are circadian rhythms [13][14][15], cAMP oscillations in Dictyostelium [16], cell-cycle regulation [17][18][19], or patterns of bursting in neuronal activity [20][21][22][23][24].…”