Convolutional neural network (CNN) driven by image recognition has been shown to be
able to explain cortical responses to static pictures at ventral-stream areas. Here, we further …

The brain represents visual objects with topographic cortical patterns. To address how
distributed visual representations enable object categorization, we established predictive …

Based on the predictive coding theory in neuro-science, we designed a bi-directional and
recur-rent neural net, namely deep predictive coding networks (PCN), that has feedforward, …

Neurophysiological field-potential signals consist of both arrhythmic and rhythmic patterns
indicative of the fractal and oscillatory dynamics arising from likely distinct mechanisms. Here, …

The human visual cortex extracts both spatial and temporal visual features to support
perception and guide behavior. Deep convolutional neural networks (CNNs) provide a …

Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown
to be able to predict and decode cortical responses to natural images or videos. Here, we …

Spontaneous activity observed with resting-state fMRI is used widely to uncover the brain's
intrinsic functional networks in health and disease. Although many networks appear modular …

Despite the wide applications of functional magnetic resonance imaging (fMRI) to mapping
brain activation and connectivity in cortical gray matter, it has rarely been utilized to study …

Inspired by" predictive coding"-a theory in neuroscience, we develop a bi-directional and
dynamic neural network with local recurrent processing, namely predictive coding network (…

Groundwater is the most important alternative drinking water source in the coastal urban
area of Shenzhen City, China. Understanding the main geochemical factors affecting …