Adam Kohn

The paper compares representational geometry in macaque V1 and V2 for naturalistic textures, finding that V2 has lower-dimensional and more discriminable representations.

The paper uses recurrent network models to show how input alignment with connectivity shapes directional interactions in cortical circuits, revealing excitatory inputs' dominant role in controlling directionality.

The paper investigates how adaptation to naturalistic textures affects neuronal responsivity and selectivity in macaque V1 and V2, finding that V2 shows specific adaptation dependent on higher-order texture statistics.

The paper tests for neuronal correlates of salience in macaque V1 and V2 using naturalistic textures and finds little evidence for pop-out responses but suggests adaptation can induce them in V2.

The paper extends an affine model to explain stimulus-dependent shared variability in visual cortex areas V1 and V2, linking circuit mechanisms to statistical models.

This paper introduces DLAG, a framework to disentangle bidirectional signal flow between neuronal populations, demonstrating its effectiveness on synthetic data and primate visual areas.

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

The paper investigates how visual crowding affects information about target orientation in V1 and V4 neuronal populations, finding greater information loss in V4.

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.