Tiago Marques

The paper shows that visual experience shapes the retinotopic organization of cortical feedback inputs to primary visual cortex in mice.

The paper builds and evaluates hierarchical neural network models to approximate primate V1 neurons and shows their alignment with human object recognition behavior.

A fixed-weight, biologically parameterised V1 front-end gives standard image classifiers adversarial and corruption robustness at no extra training cost; its simulated neuronal noise drives most of the adversarial gain.

This paper reduces the number of supervised synaptic updates needed to model the primate ventral visual stream, achieving 80% of the adult match with significantly fewer updates.

The paper evaluates hyperparameter configurations for predicting human brain responses using convolutional neural networks and identifies optimal settings for fMRI and MEG data.