Topic: principal cells

Efficiently transmitting information from dynamic environments necessitates sensory systems that rapidly and reversibly adapt to changes in input statistics. Single neurons adjust their input-output gains to adaptively normalize their stimulus-driven response variance (Fairhall et al. 2001). In neural populations, statistical whitening and related adaptations have been observed (Dan et al. 1996; Benucci et al. 2013; Wanner and Friedrich 2020), whereby joint statistics of neurons are decorrelated in addition to being re-scaled to have equal variance. Existing models of neural population adapt

A wealth of experimental studies show that the trial-to-trial variability of neuronal activity is quenched during stimulus evoked responses. This fact has helped ground a popular view that the variability of spiking activity can be decomposed into two components. The first is due to irregular spike timing conditioned on the firing rate of a neuron (i.e. a Poisson process), and the second is the trial-to-trial variability of the firing rate itself. Quenching of the variability of the overall response is assumed to be a reflection of a suppression of firing rate variability. Network models have