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Reassessing hierarchical correspondences between brain and deep networks through direct interface.

ABSTRACT: Functional correspondences between deep convolutional neural networks (DCNNs) and the mammalian visual system support a hierarchical account in which successive stages of processing contain ever higher-level information. However, these correspondences between brain and model activity involve shared, not task-relevant, variance. We propose a stricter account of correspondence: If a DCNN layer corresponds to a brain region, then replacing model activity with brain activity should successfully drive the DCNN's object recognition decision. Using this approach on three datasets, we found that all regions along the ventral visual stream best corresponded with later model layers, indicating that all stages of processing contained higher-level information about object category. Time course analyses suggest that long-range recurrent connections transmit object class information from late to early visual areas.

SUBMITTER: Sexton NJ

PROVIDER: S-EPMC9278854 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Reassessing hierarchical correspondences between brain and deep networks through direct interface.

Sexton Nicholas J NJ Love Bradley C BC

Science advances 20220713 28

Functional correspondences between deep convolutional neural networks (DCNNs) and the mammalian visual system support a hierarchical account in which successive stages of processing contain ever higher-level information. However, these correspondences between brain and model activity involve shared, not task-relevant, variance. We propose a stricter account of correspondence: If a DCNN layer corresponds to a brain region, then replacing model activity with brain activity should successfully driv ...[more]

PMID: 35857493

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Project description:BackgroundCircular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the regulation of various diseases and cancers. Determining the functions of circRNAs in mammalian cells is of great significance for revealing their mechanism of action in physiological and pathological processes, diagnosis and treatment of diseases. However, determining the functions of circRNAs on a large scale is a challenging task because of the high experimental costs.ResultsIn this paper, we present a hierarchical deep learning model, DeepciRGO, which can effectively predict gene ontology functions of circRNAs. We build a heterogeneous network containing circRNA co-expressions, protein-protein interactions and protein-circRNA interactions. The topology features of proteins and circRNAs are calculated using a novel representation learning approach HIN2Vec across the heterogeneous network. Then, a deep multi-label hierarchical classification model is trained with the topology features to predict the biological process function in the gene ontology for each circRNA. In particular, we manually curated a benchmark dataset containing 185 GO annotations for 62 circRNAs, namely, circRNA2GO-62. The DeepciRGO achieves promising performance on the circRNA2GO-62 dataset with a maximum F-measure of 0.412, a recall score of 0.400, and an accuracy of 0.425, which are significantly better than other state-of-the-art RNA function prediction methods. In addition, we demonstrate the considerable potential of integrating multiple interactions and association networks.ConclusionsDeepciRGO will be a useful tool for accurately annotating circRNAs. The experimental results show that integrating multi-source data can help to improve the predictive performance of DeepciRGO. Moreover, The model also can combine RNA structure and sequence information to further optimize predictive performance.

Dataset Information

Reassessing hierarchical correspondences between brain and deep networks through direct interface.

Publications

Reassessing hierarchical correspondences between brain and deep networks through direct interface.

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