Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contributions of within-trial spike irregularity and trial-to-trial rate variability to FF fluctuations have remained elusive. Here, we introduce a principled approach for accurate estimation of spiking irregularity and rate variability in time for doubly stochastic point processes. Consistent with previous evidence, analysis showed stimulus-induced reduction in rate variability across multiple cortical and subcortical areas. However, unlike what was previously thought, spiking irregularity, was not constant in time but could be enhanced due to factors such as bursting abating the quench in the post-stimulus FF. Simulations confirmed plausibility of a time varying spiking irregularity arising from within and between pool correlations of excitatory and inhibitory neural inputs. By accurate parsing of neural variability, our approach reveals previously unnoticed changes in neural response variability and constrains candidate mechanisms that give rise to observed rate variability and spiking irregularity within brain regions.

Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contribution of within trial spike irregularity (nψ) and trial to trial rate variability (nRV) to FF reduction has remained elusive. Here, we introduce a principled approach for accurate estimation of variability components for a doubly stochastic point process which unlike previous methods allows for a time varying nψ (aka φ). Notably, analysis across multiple subcortical and cortical areas showed across the board reduction in rate variability. However, unlike what was previously thought, spiking irregularity was not constant in time and was even enhanced in some regions abating the quench in the post-stimulus FF. Simulations confirmed plausibility of a time varying nψ arising from within and between pool correlations of excitatory and inhibitory neural inputs. By accurate parsing of neural variability, our approach constrains candidate mechanisms that give rise to observed rate variability and spiking irregularity within brain regions.