Format

Send to

Choose Destination
Annu Rev Vis Sci. 2015 Nov 24;1:417-446. doi: 10.1146/annurev-vision-082114-035447.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

Author information

1
Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom; email: nikolaus.kriegeskorte@mrc-cbu.cam.ac.uk.

Abstract

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

KEYWORDS:

artificial intelligence; biological vision; computational neuroscience; computer vision; deep learning; neural network; object recognition

Supplemental Content

Full text links

Icon for Atypon
Loading ...
Support Center