Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS).

ABSTRACT: Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on a kernel smoothing technique that considers the bandwidth as a random variab

SUBMITTER: Ahmadi N 

PROVIDER: S-EPMC6248928 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature