Group Classification Algorithm Our parameter variation resulted in a parameter space of 10,485,760 model instances. We classified these simulated HCO and isolated neuron instances by their activity characteristics, so that instances showing the same electrical activity are segregated to the same group. The classification algorithm parsed automatically the spike and voltage files corresponding to each simulated instance and gathered information about its electrical activity. Based on this information, the algorithm assigned a group label to each model instance. The HCO instances were grouped into the following 10 groups: spiking, silent, asymmetric activity, plateau, asymmetric bursting, irregular spikes, one burst, irregular period, unbalanced, and functional. The isolated neuron instances were split into the following 8 groups: spiking, silent, bistable, plateau, irregular spikes, one burst, irregular period, and regular bursting. The type of electrical activity characterizing each group can be visualized in Figures 2 and 4 of Doloc-Mihu & Calabrese (2011). Note: Our classification method was designed such that there were no positive misses (for example, we did not classify models with bursting activity within the group of models with spiking activity), but we could have false negatives (for example, we may classify models with spiking activity within the group of models with bursting activity). We used eight activity characteristics in the classification algorithm, as follows: Step 1 (activity type level): 1. Any spiking activity present or not (threshold of -20mV) to obtain the silent and non-silent HCOs 2. For non-silent HCOs, the number of spikes – presence of minimum 3 spikes to define a burst or not 3. Presence of a minimum inter-burst interval of 1 sec to differentiate between spiking and bursting activities Step 2 (spike type level): 4. The value of the voltage trace at the end point of the burst (greater than -35 mV) to obtain the plateaus 5. Coefficient of variation of the amplitudes of the spikes within the burst (less or equal than 0.07) to obtain the irregular spikes and the normal spikes HCO instances Step 3 (“number of bursts” level): 6. For the later group, the number of bursts per neuron in 100sec of simulation time to obtain the one burst instances and the repeated bursts instances Step 4 (period regularity level): 7. For the repeated instances, the coefficient of variation of the period (less than 0.05) to obtain the irregular period and the regular period instances Step 5 (phase level): 8. For the regular period instances, the mean phase (in the range of 0.45-0.55) to obtain the unbalanced and the functional instances. The pseudocode for the entire classification algorithm can be found in the Appendix 1 of Doloc-Mihu & Calabrese (2011). Table 3 from Appendix 4 provides the statistics for the HCO instances broken down by the synaptic components present. Last updated June 22, 2012. Please send comments to adolocm@emory.edu.