Dana H. Ballard

The paper shows that humans select whole-body movements with minimum energetic cost by comparing movement cost in a dynamic model of the human skeleton.

The paper shows that phase delays in cortical spikes during gamma oscillations match sparse coding models, linking experimental data to receptive field structures.

The paper proposes a modular reinforcement learning model to estimate rewards and discount factors from human behavior data, enabling accurate prediction of navigation decisions in virtual reality.

The paper introduces a model called gamma spike multiplexing to explain how cortical networks use gamma frequency latencies to modulate fast communication, offering advantages in parallel processing and speed.