Exploration and Implementation of Neural Ordinary Differential Equations

Neural ordinary differential equations (ODEs) have recently emerged as a novel ap- proach to deep learning, leveraging the knowledge of two previously separate domains, neural networks and differential equations. In this paper, we first examine the back- ground and lay the foundation for traditional artificial neural networks. We then present neural ODEs from a rigorous mathematical perspective, and explore their advantages and trade-offs compared to traditional neural nets

Lifelong Learning with Spiking Neural Networks

A long-standing challenge in machine learning has been the ability of lifelong learning. This supports machines that retain continually learned knowledge and apply it to new and different tasks. In an effort to address this problem, some recent work has moved toward more biologically-inspired computational methods. Methods such as spiking neural networks have proven to be a promising solution, especially when equipped with unsupervised synaptic-timing-dependent plasticity learning rules. Here, we compare existing learning rules through an experiment implemented using the BindsNET Python library, and highlight a new direction towards a more generalized artificial intelligence