Temporal attention is a mechanism in neural networks that dynamically focuses on different parts of a sequence over time, enhancing the model's ability to capture temporal dependencies in sequential data. It is particularly useful in tasks such as video analysis, speech recognition, and time-series forecasting, where understanding the progression and context of information is crucial.