Role of interneuron subtypes in controlling trial-by-trial output variability in theneocortex

Abstract

Trial-by-trial variability is a ubiquitous property of neuronal activity in vivo and affects the stimulus response. Computational models have revealed how local network structure and feedforward inputs control the trial-by-trial variability. However, the role of input statistics and different interneuron subtypes in shaping the trial-by-trial variability was less understood. Here we investigated the dynamics of stimulus response in a model of cortical microcircuit with one excitatory and three inhibitory interneuron populations (PV, SST, VIP). We show that the variance ratio of inputs to different neuron populations and input covariances are the main determinants of output trial-by-trial variability. The effect of input covariances is contingent on the input variance ratios. In general, the network shows smaller output trial-by-trial variability in a PV-dominated regime than in an SST-dominated regime. Our work reveals mechanisms by which output trial-by-trial variability can be controlled in a context, state, and task-dependent manner.