Balanced Excitatory and Inhibitory Inputs to Cortical Neurons Decouple Firing Irregularity from Rate Modulations

In vivo cortical neurons are known to exhibit highly irregular spike patterns. Because the intervals between successive spikes fluctuate greatly, irregular neuronal firing makes it difficult to estimate instantaneous firing rates accurately. If, however, the irregularity of spike timing is decoupled from rate modulations, the estimate of firing rate can be improved. Here, we introduce a novel coding scheme to make the firing irregularity orthogonal to the firing rate in information representation. The scheme is valid if an interspike interval distribution can be well fitted by the gamma distribution and the firing irregularity is constant over time. We investigated in a computational model whether fluctuating external inputs may generate gamma process-like spike outputs, and whether the two quantities are actually decoupled. Whole-cell patch-clamp recordings of cortical neurons were performed to confirm the predictions of the model. The output spikes were well fitted by the gamma distribution. The firing irregularity remained approximately constant regardless of the firing rate when we injected a balanced input, in which excitatory and inhibitory synapses are activated concurrently while keeping their conductance ratio fixed. The degree of irregular firing depended on the effective reversal potential set by the balance between excitation and inhibition. In contrast, when we modulated conductances out of balance, the irregularity varied with the firing rate. These results indicate that the balanced input may improve the efficiency of neural coding by clamping the firing irregularity of cortical neurons. We demonstrate how this novel coding scheme facilitates stimulus decoding.