Literature DB >> 30795896

Deep Neural Networks as Scientific Models.

Radoslaw M Cichy1, Daniel Kaiser2.   

Abstract

Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to solve cognitive tasks at which humans excel. In the absence of explanations for such cognitive phenomena, in turn cognitive scientists have started using DNNs as models to investigate biological cognition and its neural basis, creating heated debate. Here, we reflect on the case from the perspective of philosophy of science. After putting DNNs as scientific models into context, we discuss how DNNs can fruitfully contribute to cognitive science. We claim that beyond their power to provide predictions and explanations of cognitive phenomena, DNNs have the potential to contribute to an often overlooked but ubiquitous and fundamental use of scientific models: exploration.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  deep learning; explanation; exploration; neural network; prediction; scientific model

Year:  2019        PMID: 30795896     DOI: 10.1016/j.tics.2019.01.009

Source DB:  PubMed          Journal:  Trends Cogn Sci        ISSN: 1364-6613            Impact factor:   20.229


  50 in total

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