Yuguo Yu

This paper establishes a theoretical framework linking neural response sparsity and variability to energy-efficient coding, showing they are intrinsically correlated and optimized for information encoding.

This study introduces a self-evolving spiking neural network that forms pinwheel structures, showing improved visual coding efficiency and reliability through sparse coding and synaptic plasticity.

This study investigates the functional role of pinwheel topology in V1 of high-order animals, showing how it affects visual acuity and coding efficiency through spiking patterns and deep learning models.

The paper shows that a small number of dysregulated adult-born neurons in the hippocampus disrupt brain-wide functional connectivity and spatial memory.

This paper develops a spatiotemporal computational model of human visual search that accounts for memory constraints and saccadic inaccuracies, showing human strategies are suboptimal but balance performance and task costs.

The paper derives equivalent cable energy functions for action potential conduction in cortical axons with different boundary conditions, showing higher accuracy than the Na+-counting method.