Temporal Receptive Field

A temporal receptive field (TRF) describes the temporal window within which a model considers past information to make predictions about the present. Current research focuses on optimizing TRF size and architecture across diverse applications, including video analysis, gait recognition, and language processing, employing models such as convolutional neural networks, transformers, and spiking neural networks to achieve this. Understanding and manipulating TRFs is crucial for improving the accuracy and efficiency of these models, leading to advancements in areas like action recognition, physiological signal extraction from video, and even understanding human brain function. The ability to dynamically adjust TRFs based on input data is a particularly active area of investigation.