Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future

By Grace W. Lindsay. Journal of Cognitive Neuroscience, 2020

529 citations recorded by the source corpus.

Abstract Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neuroscience and the various ways models can provide insight. Specifically, it covers the origins of CNNs and the methods by which we validate them as models of biological vision. It then goes on to elaborate on what we can learn about biological vision by understanding and experimenting on CNNs and discusses emerging opportunities for the use of CNNs in vision research beyond basic object recognition.

Subfields: Biophysics, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/1cd9ab95572772db415f35f223fbd914c14cb7c7

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Citations in the corpus

If deep learning is the answer, what is the question? (2020, background): “One strong candidate is supervised learning with gradient descent, in which representations are sculpted by feedback about the label, name or category associated with a sensory input 29 .”
Do Vision Transformers See Like Convolutional Neural Networks? (2021, unknown): “These techniques have been very successful in providing insights on properties of different vision architectures [29, 22, 18], representation structure in language models [48, 25, 47, 21], dynamics of training methods [33, 24] and domain specific model behavior [27, 35, 38].”