Recent progress in deep learning extends the capability of artificial intelligence to various
practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis …

The brain is a highly modular structure. To exploit modularity, it is necessary that spiking
activity can propagate from one module to another while preserving the information it …

Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

Memristors and memristor crossbar arrays have been widely studied for neuromorphic and
other in-memory computing applications. To achieve optimal system performance, however …

Deep spiking neural networks (SNNs) hold the potential for improving the latency and
energy efficiency of deep neural networks through data-driven event-based computation …

Sparse representation of information provides a powerful means to perform feature
extraction on high-dimensional data and is of broad interest for applications in signal …

Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …

Deep learning techniques have been successfully applied in many areas of computer vision,
including low-level image restoration problems. For image super-resolution, several models …

The deep dorsal horn is a poorly characterized spinal cord region implicated in processing
low-threshold mechanoreceptor (LTMR) information. We report an array of mouse genetic …

Many real world tasks such as reasoning and physical interaction require identification and
manipulation of conceptual entities. A first step towards solving these tasks is the automated …