Fueled by innovation in the computer vision and artificial intelligence communities, recent
developments in computational neuroscience have used goal-driven hierarchical …

Understanding brain circuits begins with an appreciation of their component parts—the cells.
Although GABAergic interneurons are a minority population within the brain, they are crucial …

Batch Normalization (BN) is a milestone technique in the development of deep learning,
enabling various networks to train. However, normalizing along the batch dimension …

The aim of this paper is to introduce a new learning procedure for neural networks and to
demonstrate that it works well enough on a few small problems to be worth further …

We describe an image compression method, consisting of a nonlinear analysis
transformation, a uniform quantizer, and a nonlinear synthesis transformation. The …

We compare the robustness of humans and current convolutional deep neural networks
(DNNs) on object recognition under twelve different types of image degradations. First, using …

Spiking neural networks (SNNs) that enables energy efficient implementation on emerging
neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown …

Mounting evidence suggests that 'core object recognition,'the ability to rapidly recognize
objects despite substantial appearance variation, is solved in the brain via a cascade of …

Philosopher Ned Block argues in this book that there is a" joint in nature" between
perception and cognition and that by exploring the nature of that joint, one can solve …

Hypergraphs extend traditional graphs by allowing edges to connect multiple nodes, while
superhypergraphs further generalize this concept to represent even more complex …