Self-supervised learning has been shown to be very effective in learning useful
representations, and yet much of the success is achieved in data types such as images …

Understanding how biological neural networks carry out learning using spike-based local
plasticity mechanisms can lead to the development of real-time, energy-efficient, and …

The existing few-shot medical segmentation networks share the same practice that the more
prototypes, the better performance. This phenomenon can be theoretically interpreted in …

Continuous monitoring with an ever-increasing number of sensors has become ubiquitous
across many application domains. However, acquired time series are typically high …

Cortical plasticity is one of the main features that enable our ability to learn and adapt in our
environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic …

Promoting sustainability and resilience is critical for long-term development of cities.
However, there are still no consistent approaches to identify and measure sustainability and …

This paper introduces Elegans-AI models, a class of neural networks that leverage the
connectome topology of the Caenorhabditis elegans to design deep and reservoir …

The field of artificial intelligence has significantly advanced over the past decades, inspired
by discoveries from the fields of biology and neuroscience. The idea of this work is inspired …

An intelligent system capable of continual learning is one that can process and extract
knowledge from potentially infinitely long streams of pattern vectors. The major challenge …

Spectral clustering (SC) is one of the most popular clustering methods and often outperforms
traditional clustering methods. SC uses the eigenvectors of a Laplacian matrix calculated …