Calcium sensor parameters and readout configurations for activity-dependent homeostatic regulation of pyloric network rhythms in the lobster stomatogastric ganglion

Abstract

In central pattern generating (CPG) neural networks,activity-dependent homeostatic regulation (ADHR) hasbeen proposed to explain the experimentally observedrobust activity that persists in spite of constant molecularturnover and environmental changes. In the pyloric CPGnetwork of the lobster stomatogastric ganglion (STG),ADHR is dependent on and correlated with levels of intra-cellular calcium, which acts as a second messenger thataffects ion channel and synaptic properties of the cell. Pre-vious studies showed that calcium sensors can be used tomaintain stable activity levels in individual model neu-rons [1] and pyloric rhythms in one model network [2].For regulation, these studies used deviations of the cal-cium current from a target value. However, they did notaddress the choice of sensor activation and inactivationvariables, and the robustness of selected parameters andsensor configurations in the network. To address theseissues, we developed a testbed that judges the quality of asensor by using its readings to make a prediction aboutwhether a network activity pattern is functional.To make predictions, we used a classifier trained with sen-sor readings from a model pyloric network database [3].Based on their selected activity characteristics being simi-lar to biological data, 2% of these networks were labeledas functional. In each testbed with different sensor place-ments and parameters, the percentage of functional net-works correctly predicted by the classifier is indicated witha success rate.Directly using the average calcium concentration from thethree model cells of the network resulted in a 52% predic-tion success if shuffled, establishing a control case, com-pared to 77% without shuffling. Using average calciumcurrent instead of the concentration, we obtained a simi-lar success (77%), supporting the choice by earlier cal-cium sensor models [1,2]. We confirmed that the successrate increased by the addition of activation (78%) andinactivation (86%) variables in the averaged sensors,showing that the inactivation component is indispensable(see Figure 1). By testing all combinations of selected acti-vation and inactivation parameters, we found their opti-mal values. It is biologically reasonable for the sensorminimal and maximal values to be involved in regulationand using them in addition to the sensor averagesincreased the success to 87%. Finally, using the fast, slowand DC sensors proposed earlier [1] together in the samecell marginally increased the success further to 88%.Taken together, our results suggest that activity sensing forADHR of the pyloric network can potentially be achievedwith relatively few, simple calcium sensors and that theproperties of these sensors need not necessarily beadjusted to the particular role of each neuron in the net-work.