Model Calcium Sensors for Network Homeostasis: Sensor and Readout Parameter Analysis from a Database of Model Neuronal Networks

Abstract

In activity-dependent homeostatic regulation (ADHR) of neuronal and network properties, the intracellular Ca 2ϩ concentration is a good candidate for sensing activity levels because it is correlated with the electrical activity of the cell. Previous ADHR models, developed with abstract activity sensors for model pyloric neurons and networks of the crustacean stomatogastric ganglion, showed that functional activity can be maintained by a regulation mechanism that senses activity levels solely from Ca 2ϩ . At the same time, several intracellular pathways have been discovered for Ca 2ϩ -dependent regulation of ion channels. To generate testable predictions for dynamics of these signaling pathways, we undertook a parameter study of model Ca 2ϩ sensors across thousands of model pyloric networks. We found that an optimal regulation signal can be generated for 86% of model networks with a sensing mechanism that activates with a time constant of 1 ms and that inactivates within 1 s. The sensor performed robustly around this optimal point and did not need to be specific to the role of the cell. When multiple sensors with different time constants were used, coverage extended to 88% of the networks. Without changing the sensors, it extended to 95% of the networks by letting the sensors affect the readout nonlinearly. Specific to this pyloric network model, the sensor of the follower pyloric constrictor cell was more informative than the pacemaker anterior burster cell for producing a regulatory signal. Conversely, a global signal indicating network activity that was generated by summing the sensors in individual cells was less informative for regulation.

Cell Types, Network Homeostasis, and Pathological Compensation from a Biologically Plausible Ion Channel Expression Model

“…There are many ways to implement feedback control of membrane conductances (Günay and Prinz 2010; LeMasson et al, 1993; Liu et al, 1998; Olypher and Prinz 2010; Stemmler and Koch 1999). We wanted to focus on a rule that captures essential biological principles and has experimentally testable properties.…”

High Prevalence of Multistability of Rest States and Bursting in a Database of a Model Neuron

“…A brute force database is a powerful tool for assessing and cataloging regimes of neuronal activity [2], [19]–[25], [33]. It sweeps through multidimensional parameter space, obtaining and categorizing the activities of the model for each parameter set, i.e., case.…”

Compensation for Variable Intrinsic Neuronal Excitability by Circuit-Synaptic Interactions

“…There is an ever-growing body of literature that suggests that the activity levels of individual neurons and networks are homeostatically regulated to maintain a physiologically appropriate and stable network performance in the face of growth, channel turnover and perturbations by modification of either synaptic or intrinsic parameters or both (LeMasson et al, 1993; Turrigiano et al, 1994; Liu et al, 1998; Golowasch et al, 1999b; Turrigiano and Nelson, 2000; Paradis et al, 2001; Soto-Trevino et al, 2001; Luther et al, 2003; Turrigiano and Nelson, 2004; Davis, 2006; Haedo and Golowasch, 2006; Pratt and Aizenman, 2007; Rich and Wenner, 2007; Turrigiano, 2007; Zhang and Golowasch, 2007; Turrigiano, 2008; Wilhelm and Wenner, 2008; Dickman and Davis, 2009; Frank et al, 2009; Wilhelm et al, 2009; Zhang et al, 2009; Gunay and Prinz, 2010; O’Leary et al, 2010; Olypher and Prinz, 2010). …”