ArticleHuman brain mapping2018
Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.
Article in Human brain mapping, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed, 44 citations in OpenAlex.
- Noisy models of the ventral stream reveal the impact of recurrence and learned representations on information processing timescales.PLoS computational biology · 2026Article
- Pixel-level understanding of a world in motion within a neural encoding framework.Scientific reports · 2026Article
- Compression-enabled interpretability of voxelwise encoding models.PLoS computational biology · 2025Article
- Architecture of the brain's visual system enhances network stability and performance through layers, delays, and feedback.PLoS computational biology · 2023Article
- Convolutional neural networks for vision neuroscience: significance, developments, and outstanding issues.Frontiers in computational neuroscience · 2023Review
- High-Level Visual Encoding Model Framework with Hierarchical Ventral Stream-Optimized Neural Networks.Brain sciences · 2022Article
- Article
- Naturalistic Stimuli: A Paradigm for Multi-Scale Functional Characterization of the Human Brain.Current opinion in biomedical engineering · 2021Article
- A Visual Encoding Model Based on Contrastive Self-Supervised Learning for Human Brain Activity along the Ventral Visual Stream.Brain sciences · 2021Article
- GaborNet Visual Encoding: A Lightweight Region-Based Visual Encoding Model With Good Expressiveness and Biological Interpretability.Frontiers in neuroscience · 2021Article
- Movies and narratives as naturalistic stimuli in neuroimaging.NeuroImage · 2021Review
- Article
- Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex.NeuroImage · 2019Article
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.JMIR mHealth and uHealth · 2019Article
- Category Decoding of Visual Stimuli From Human Brain Activity Using a Bidirectional Recurrent Neural Network to Simulate Bidirectional Information Flows in Human Visual Cortices.Frontiers in neuroscience · 2019Article
- Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions.Human brain mapping · 2018Article
- Transferring and generalizing deep-learning-based neural encoding models across subjects.NeuroImage · 2018Article
- Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.Human brain mapping · 2018Article
- Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex.Communications biology · 2018Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.