Amir Aschner

The paper examines feedforward and feedback interactions between visual cortical areas and finds they use distinct population activity patterns.

This paper introduces response models based on mixture models and exponential families to quantify neural coding properties in macaque primary visual cortex, capturing noise correlations and enabling Bayesian decoding.

The paper shows that variability in macaque V1 neurons reflects probabilistic inference tuned to natural image statistics, explaining variance-mean relationships and spatial context modulation.

The paper develops a method to detect differential correlations in neural data and applies it to V1 recordings, finding that V1 contains information consistent with behavioral performance.