Exact Distinction of Excitatory and Inhibitory Neurons in Neural Networks: a Study with GFP-GAD67 Neurons Optically and Electrophysiologically Recognized on Multielectrode Arrays

Distinguishing excitatory from inhibitory neurons with multielectrode array (MEA) recordings is a serious experimental challenge. The current methods, developed in vitro, mostly rely on spike waveform analysis. These however often display poor resolution and may produce errors caused by the variability of spike amplitudes and neuron shapes. Recent recordings in human brain suggest that the spike waveform features correlate with time-domain statistics such as spiking rate, autocorrelation and coefficient of variation. However, no precise criteria are available to exactly assign identified units to specific neuronal types, either in vivo or in vitro. To solve this problem, we combined MEA recording with fluorescence imaging of neocortical cultures from mice expressing green fluorescent protein (GFP) in GABAergic cells. In this way, we could sort out ‘authentic excitatory neurons’ (AENs) and ‘authentic inhibitory neurons’ (AINs). We thus characterized 1275 units (from 405 electrodes, n=10 experiments), based on autocorrelation, burst length, spike number, spiking rate, squared coefficient of variation and Fano factor (the ratio between spike-count variance and mean). These metrics differed by about one order of magnitude between AINs and AENs. In particular, the Fano factor turned out to provide a firing code which exactly (no overlap) recognizes excitatory and inhibitory units. The difference in Fano factor between all of the identified AEN and AIN groups was highly significant (p < 10-8, ANOVA post-hoc Tukey test). Our results indicate a statistical metric-based approach to distinguish excitatory from inhibitory neurons independently from the spike width.