Name
Title | ||
|---|---|---|
Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. |
Abstract | ||
|---|---|---|
Neurons in many brain areas exhibit high trial-to-trial variability, with spike counts that are overdispersed relative to a Poisson distribution. Recent work (Goris, Movshon, & Simoncelli, 2014) has proposed to explain this variability in terms of a multiplicative interaction between a stochastic gain variable and a stimulus-dependent Poisson firing rate, which produces quadratic relationships bet... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1162/neco_a_01062 | Neural Computation |
Keywords | Field | DocType |
neural response variability,doubly stochastic models,over-dispersion,neural encoding models,closed-loop experiments | Statistical physics,Population,Mathematical optimization,Nonlinear system,Fano factor,Biology,Multiplicative function,Quadratic equation,Poisson distribution,Genetics,Gaussian noise,Bayesian probability | Journal |
Volume | Issue | ISSN |
30 | 4 | 0899-7667 |
Citations | PageRank | References |
1 | 0.37 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam S. Charles | 1 | 113 | 10.21 |
| Park, Mijung | 2 | 41 | 5.11 |
| J. Patrick Weller | 3 | 1 | 0.37 |
| Gregory D. Horwitz | 4 | 1 | 0.37 |
| Pillow, Jonathan W. | 5 | 346 | 39.95 |