Jingyang Zhou

This paper introduces GSN, a method to disentangle signal and noise in neural responses, showing it improves dimensionality estimates in fMRI data.

The paper introduces a method to compare neural models by measuring their perceptual discriminability using metric tensors, demonstrating its effectiveness with color and autoencoder models.

The paper quantifies and predicts chromatic thresholds by analyzing cone proportions and noise assumptions, showing improved predictions when Poisson noise is removed.

The paper develops a delayed normalization model to capture nonlinear neural dynamics in macaque and human V1 across different measurement techniques, finding that responses are near-additive in time.

The paper examines how perceptual discriminability relates to neuronal receptive fields under different models of neuronal computations, finding that it is determined by receptive fields and response covariance in stochastic cases.

The paper proposes a unified framework that links perceptual sensitivity and intensity judgments by modeling them as properties of an internal representation, showing compatibility with Webers law and Stevens power law.

The paper presents a computational model of temporal dynamics in visual cortex that explains diverse temporal phenomena across multiple measurement types and cortical areas.

This paper investigates temporal processing in the human visual cortex using fMRI, finding sub-additive summation and adaptation, and proposes a model that parallels spatial compressive summation.