Event-based sensors (e.g., DVS cameras) are capable of higher dynamic range, higher temporal resolution, lower time latency, and better power efficiency compared to conventional devices (e.g., RGB cameras). However, learning from these sensors remains challenging; event-based sensors output a stream of asynchronous events, which cannot be directly used by state-of-the-art convolutional neural networks (CNNs). In this paper, we present a novel event-based representation called MDOE that considers both the magnitude and density of events. Compared to existing representations, which discard one or more types of information about event polarity, temporal information, and/or density, MDOE contains richer information about events. It has two benefits: (i) it is a conceptually-simple generic representation that is task-independent; (ii) it achieves superior performance relative to existing representations on a variety of event-based datasets.