Continuous partitioning of neuronal variability

Neural variability poses a major challenge to understanding the information content of neural codes. Recent work by Goris et al. [1] has provided new insights into variability in the visual pathway by partitioning it into two components: a stimulus component reflecting sensory tuning, and a modulatory component arising from stimulus-independent fluctuations in excitability. However, a key limitation of this framework is that it lacks a continuous-time interpretation; it applies only to spike counts measured in time bins of a given size, and does not generalize to different bin sizes. Here we overcome this limitation using a new model for continuous-time partitioning of neural variability, the “continuous modulated Poisson” (CMP). This model extends the partitioning model of Goris et al. [1] by modeling a neuron’s instantaneous firing rate as the product of a time-varying stimulus drive and a continuous-time stochastic gain process. We apply this model to spike responses from four different visual areas (LGN, V1, V2, and MT) and show that it accurately captures spike train variability across timescales and throughout the visual hierarchy. Moreover, we demonstrate that the modulatory gain process decays according to an exponentiated power law, with higher variance and slower decay at later stages of the visual hierarchy. This model provides new insights into the organization of both stimulus-driven and stimulus-independent modulatory processes and provides a powerful framework for characterizing neural variability across brain regions.