Continuous partitioning of neuronal variability.

ABSTRACT: 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

SUBMITTER: Rupasinghe A 

PROVIDER: S-EPMC12330597 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature