Data to 14 Oct 2026

Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway

arXiv 1411.6422 ↗First posted 24 Nov 2014q-bio.NCcited by 94

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Abstract (arXiv)

Converging evidence suggests that the mammalian ventral visual pathway encodes increasingly complex stimulus features in downstream areas. Using deep convolutional neural networks, we can now quantitatively demonstrate that there is indeed an explicit gradient for feature complexity in the ventral pathway of the human brain. Our approach also allows stimulus features of increasing complexity to be mapped across the human brain, providing an automated approach to probing how representations are mapped across the cortical sheet. Finally, it is shown that deep convolutional neural networks allow decoding of representations in the human brain at a previously unattainable degree of accuracy, providing a more sensitive window into the human brain.

Key facts. Cited by 94 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Nov 2014.

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Citing papers per year: 2021: 8; 2022: 15; 2023: 16; 2024: 13; 2025: 19; 2026: 23.

94 papers.

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2023Getting aligned on representational alignmentcited by 143
2022Human alignment of neural network representationscited by 74
2023In search of dispersed memories: Generative diffusion models are associative memory networkscited by 44
2023Brain encoding models based on multimodal transformers can transfer across language and visioncited by 32
2023BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstructioncited by 30
2022Symmetry-Based Representations for Artificial and Biological General Intelligencecited by 26
2023Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputscited by 23
2024Dimensions underlying the representational alignment of deep neural networks with humanscited by 22
2021Capturing the objects of vision with neural networkscited by 21
2022Mind the gap: Challenges of deep learning approaches to Theory of Mindcited by 21
2022Visio-Linguistic Brain Encodingcited by 13
2024Cognitive Overload Attack:Prompt Injection for Long Contextcited by 13

38 more

2022Personalized visual encoding model construction with small datacited by 12
2022Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signalscited by 12
2022Visual representations in the human brain are aligned with large language modelscited by 12
2023A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brainscited by 12
2024Evaluating Representational Similarity Measures from the Lens of Functional Correspondencecited by 12
2021Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoderscited by 10
2021Long-range and hierarchical language predictions in brains and algorithmscited by 10
2022Multimodal foundation models are better simulators of the human braincited by 10
2023Brain2Music: Reconstructing Music from Human Brain Activitycited by 10
2025Transformer brain encoders explain human high-level visual responsescited by 10
2023Joint fMRI Decoding and Encoding with Latent Embedding Alignmentcited by 8
2023End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutionscited by 8
2021Deep Recurrent Encoder: A scalable end-to-end network to model brain signalscited by 6
2024BRACTIVE: A Brain Activation Approach to Human Visual Brain Learningcited by 6
2021More Than Meets the Eye: Self-Supervised Depth Reconstruction From Brain Activitycited by 5
2024Reanimating Images using Neural Representations of Dynamic Stimulicited by 5
2025Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformercited by 5
2023Applicability of scaling laws to vision encoding modelscited by 4
2023The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Modelscited by 4
2024How does the primate brain combine generative and discriminative computations in vision?cited by 4
2024Achieving More Human Brain-Like Vision via Human EEG Representational Alignmentcited by 4
2024Modeling the Human Visual System: Comparative Insights from Response-Optimized and Task-Optimized Vision Models, Language Models, and different Readout Mechanismscited by 4
2025LaVCa: LLM-assisted Visual Cortex Captioningcited by 4
2025Triple Phase Transitions: Understanding the Learning Dynamics of Large Language Models from a Neuroscience Perspectivecited by 4
2025Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortexcited by 4
2026Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrievalcited by 4
2021Relating CNNs with brain: Challenges and findingscited by 3
2021Multimodal neural networks better explain multivoxel patterns in the hippocampuscited by 3
2022Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networkscited by 3
2022Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movementscited by 3
2023Deep Learning Models to Study Sentence Comprehension in the Human Braincited by 3
2023Probing neural representations of scene perception in a hippocampally dependent task using artificial neural networkscited by 3
2023Generate your neural signals from mine: individual-to-individual EEG converterscited by 3
2023Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAIcited by 3
2025Signatures of human-like processing in Transformer forward passescited by 3
2025LITcoder: A General-Purpose Library for Building and Comparing Encoding Modelscited by 3
2025Representational Alignment Across Model Layers and Brain Regions with Multi-Level Optimal Transportcited by 3
2022Functional Network: A Novel Framework for Interpretability of Deep Neural Networkscited by 2

50 most cited of 94 All 94 (JSON)