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Artificial neural networks (ANNs), originally inspired by biological neuralnetworks (BNNs), have achieved remarkable successes in many tasks such asvisual representation learning. However, whether there exists semanticcorrelations/connections between the visual representations in ANNs and thosein BNNs remains largely unexplored due to both the lack of an effective tool tolink and couple two different domains, and the lack of a general and effectiveframework of representing the visual semantics in BNNs such as human functionalbrain networks (FBNs). To answer this question, we propose a novelcomputational framework, Synchronized Activations (Sync-ACT), to couple thevisual representation spaces and semantics between ANNs and BNNs in human brainbased on naturalistic functional magnetic resonance imaging (nfMRI) data. Withthis approach, we are able to semantically annotate the neurons in ANNs withbiologically meaningful description derived from human brain imaging for thefirst time. We evaluated the Sync-ACT framework on two publicly availablemovie-watching nfMRI datasets. The experiments demonstrate a) the significantcorrelation and similarity of the semantics between the visual representationsin FBNs and those in a variety of convolutional neural networks (CNNs) models;b) the close relationship between CNN's visual representation similarity toBNNs and its performance in image classification tasks. Overall, our studyintroduces a general and effective paradigm to couple the ANNs and BNNs andprovides novel insights for future studies such as brain-inspired artificialintelligence.

Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations