Nicole C. Rust

TL;DR: It is proposed that understanding the algorithm that produces core object recognition will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical subnetworks with a common functional goal.

TL;DR: Research is progressing with the goals of defining a single “standard model” for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes, which would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.

TL;DR: This work shows that the responses of MT cells can be captured by a linear-nonlinear model that operates not on the visual stimulus, but on the afferent responses of a population of nonlinear V1 cells, and robustly predicts the separately measured responses to gratings and plaids.

TL;DR: This analysis reveals an unsuspected richness of neuronal computation within V1, where simple and complex cell responses are best described using more linear filters than the one or two found in standard models.

TL;DR: Spike-triggered average and covariance analyses can be used to estimate the filters and nonlinear combination rule from extracellular experimental data and demonstrated with simulated model neuron examples that emphasize practical issues that arise in experimental situations.