Evaluation of the Hierarchical Correspondence between the Human Brain and Artificial Neural Networks: A Review

Author:

Pham Trung Quang1,Matsui Teppei2,Chikazoe Junichi1ORCID

Affiliation:

1. Araya Inc., Tokyo 101-0025, Japan

2. Graduate School of Brain Science, Doshisha University, Kyoto 610-0321, Japan

Abstract

Artificial neural networks (ANNs) that are heavily inspired by the human brain now achieve human-level performance across multiple task domains. ANNs have thus drawn attention in neuroscience, raising the possibility of providing a framework for understanding the information encoded in the human brain. However, the correspondence between ANNs and the brain cannot be measured directly. They differ in outputs and substrates, neurons vastly outnumber their ANN analogs (i.e., nodes), and the key algorithm responsible for most of modern ANN training (i.e., backpropagation) is likely absent from the brain. Neuroscientists have thus taken a variety of approaches to examine the similarity between the brain and ANNs at multiple levels of their information hierarchy. This review provides an overview of the currently available approaches and their limitations for evaluating brain–ANN correspondence.

Funder

JSPS KAKENHI

Core Research for Evolutionary Science and Technology

Japan Agency for Medical Research and Development

Publisher

MDPI AG

Subject

General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology

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