Robert M. Mok

This study shows that sparse coding at the input stage of a neural network enhances learning speed but reduces generalization, mirroring the hippocampus's dentate gyrus.

The paper argues that spatial cell types in the brain may not be essential for spatial cognition, as similar patterns emerge in non-spatial deep neural networks.

The paper proposes a controller-peripheral architecture to model brain coordination for learning, showing how higher-level control shapes lower-level representations and outperforms gradient descent models.

The paper decomposes a cognitive model into neuron-like units to explain how neural populations coordinate to form cognitive representations.

The paper investigates whether the brain forms abstract category representations independent of sensory or motor details using fMRI and cognitive modeling.

The paper investigates rER BOLD fMRI in NHP, finding that while individual elements work well, their combination is challenging due to noise and motion artifacts.

This paper shows that a clustering model of concept learning can explain both spatial navigation and conceptual knowledge by generating place and grid cell-like representations in spatial tasks and more abstract representations in conceptual tasks.

The paper investigates how selective attention in working memory is preserved in aging, finding that attention-related brain activity and memory performance remain robust.