Figure 3. Transforming multichannel spike waveforms to event-based δ-like functions removes all waveform-based information and allows extracting purely spatial features. *A*, The event-based δ-transformation procedure, illustrated for the FMC event. *a*, The mean waveforms, with δ-like functions marking the FMCs. The transformation replaces all voltage values with zeros, except for the event points, which are assigned the same value as the trough. In gray are channels for which the magnitude of the TTP is below a predetermined threshold (Materials and Methods). The main channels are boxed. *b*, Next, waveform-related information that might be recovered by combining multiple δ-transformed events is removed. The δ-like functions are scaled and centralized (arrowheads), placing the event of the main channel at the midpoint (129th sample). *B*, Left, The scaled waveform of the main channel of all units in the dataset before the transformation, sorted for PYR and PV cells separately by the time of the trough. Right, The same waveforms after event-based δ-transformation. The transformation removes nearly all of the variability between units. *C*, Cross-validated random forest models (*n* = 50; no chunking) were trained using waveform-based features extracted from the transformed spikes. The confusion matrix, based on a naive decision threshold of 0.5, yields a constant prediction of one class. n.s.*p *>* *0.05, Wilcoxon test. Numbers in every cell denote the median [IQR]. Performance was quantified by the threshold-independent AUC. The classification yields an AUC of exactly 0.5, corresponding to purely random prediction. *D*, A time-based feature, FMC-Time-lag-SD, derived from the differences between the times of the FMC event in different channels. The feature quantifies the temporal dispersion of the event, without considering the actual positions of the recording electrodes. *a*, FMC-Time-lag-SD is defined as the SD of the time differences between the FMC event of the main channel (vertical dotted lines) and the other channels. In gray are ignored channels, for which the magnitude of the TTP was below a predetermined threshold. *b*, Cumulative distribution of the FMC-Time-lag-SD feature for the entire population (411 PYR, 98 PV cells, no chunking). The smaller FMC-Time-lag-SD values of the PYR indicate higher spatiotemporal synchrony for PYR compared with PV cells. All conventions for the CDFs here and in subsequent panels are the same as in Figure 2*A*. *E*, A graph-based feature, FMC-Average-weight, derived from the differences between the FMC event time in different channels and the electrode locations. *a*, FMC-Average-weight is defined as the average edge weight in the event graph. The event graph is a directed graph with vertices representing the electrodes, and edges representing the transmission speed based on the timing of the events and the location of the electrodes. Only channels that passed the threshold for the magnitude of the TTP were considered. *b*, Cumulative distribution of the FMC-Average-weight feature (no chunking). The larger values for PYR indicate higher transmission rates for PYR compared with PV cells. *F*, A value-based feature, SPD-Count, derived from SPD of the maximal negativity on every channel. *a*, SPD-Count is defined as the number of channels that reached at least 50% of the maximal negativity of the main channel. *b*, Cumulative distribution of the SPD-Count feature (no chunking). No consistent difference between the PYR and PV cells is observed, suggesting similar spatial distributions of the scaled maximal negativity (*p *>* *0.05, *U* test). See also Extended Data Figure 3-1.