Heiko Neumann B

This paper introduces a two-point neuron model to simulate how contextual information is integrated in visual processing, showing how apical-basal integration supports coherent signal amplification and learning.

The paper investigates why deep networks rely on color and texture over shape for classification, showing that training methods bias networks toward simpler features.

This paper proposes a neural dynamical model of incremental binding in object-based attention and demonstrates its efficiency and alignment with neuroanatomical evidence.

This study demonstrates that binaural integration improves vertical sound source localization by leveraging sound type-specific prior information.

This paper develops a neural model to simulate binaural signal integration with an adaptation mechanism, showing it extends the perceptual range of auditory space and improves localization precision.

The paper develops a model of stabilized neoHebbian learning to explain how visual input guides the formation and maintenance of spatially aligned auditory maps in the colliculus.