Predictive coding is arguably the currently dominant theoretical framework for the study of
perception. It has been employed to explain important auditory perceptual phenomena, and …

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 …

In the last years, the Reservoir Computing (RC) framework has emerged as a state of-the-art
approach for efficient learning in temporal domains. Recently, within the RC context, deep …

Synthetic aperture radar (SAR) automatic target recognition (ATR) technology is one of the
research hotspots in the field of image cognitive learning. Inspired by the human cognitive …

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 …

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The
majority of results in AI thus far have been achieved using deep neural networks trained with …

Deep neural networks excel at image classification, but their performance is far less robust
to input perturbations than human perception. In this work we explore whether this …

The active visual system comprises the visual cortices, cerebral attention networks, and
oculomotor system. While fascinating in its own right, it is also an important model for …

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 …

Highlights•Subunit pooling and normalization are building blocks of hierarchical cortical
models.•Image statistics models predict when normalization is recruited in primary …