Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision.

ABSTRACT: Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. The primate visual system, by contrast, contains abundant recurrent connections. Recurrent signal flow enables recycling of limited computational resources over time, and so might boost the performance of a physically finite brain or model. Here we show: (1) Recurrent convolutional neural network models outperform feedforward convolutional models matched in their number of parameters in large-scale visual recognition tasks on natural images. (2) Setting a confidence threshold, at which recurrent computations terminate and a decision is made, enables flexible trading of speed for accuracy. At a given confidence threshold, the model expends more time and energy on images that are har

SUBMITTER: Spoerer CJ 

PROVIDER: S-EPMC7556458 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature