Over time, the two branches interact recurrently to build a scene representation from moving fixations.

Here, we develop a novel learning rule designed to minimize interferencebetween sequentially learned tasksinrecurrent networks.

Thus, there is a need for a reference point, on which each model canbetested andfrom where potential improvements canbe derived. In this study, we select publicly available state-of-the-art visual search models and datasets in natural scenes, and provide a common framework for their evaluation. To this end, we apply a unified format and criteria, bridging the gaps between them, and we estimate the models' efficiency and similarity with humans using a specific set of metrics.

The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs).

For an example unit j, if it shows activity for input images having white pixels at 6 dva to 8 dva, we say the RF for the unit j is 2 dva and its eccentricity is 7 dva.