Visual Neuroscience

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Visual neuroscience asks how the retina and brain turn light into perception and action, from detecting edges and motion to recognizing objects and navigating scenes. Researchers combine psychophysics and neural recordings with fMRI and EEG, and use computational models to test ideas about receptive fields, cortical pathways, and neural population codes. Encoding and decoding models, representational comparisons, and optimized or synthetic stimuli help relate brain activity to visual features and behavior. A central challenge is that models can predict neural responses without reproducing the mechanisms behind them, and alignment can vary across brain areas, tasks, and species.

Latest papers

53 papers10 papers in the last four weeks, up 233% on the four weeks before. 0.1% of all new papers.

Alignment of visual brain encoding models with individual perceptual similarity judgments
Oct 15, 2026Daniel Chong, Haomiao Chen, Keith W Jamison +4Neural Encoding Visual Neuroscience
Cornell University · Cornell Tech · Weill Cornell Medicine +2
Neuromorphic Decoding and Dynamical Image Reconstruction from Populations of Spiking Retinal Ganglion Cells
Oct 12, 2026Ning Zhang, Arto NurmikkoNeural Decoding Image Reconstruction
School of Engineering, Brown University, Providence, RI 02912 USA
Neurons++: A Unified Visual-Stream Framework for Image and Video Reconstruction from fMRI with Neural-to-Visual Interpretability
Oct 9, 2026Haonan Wang, Yuchen Wang, Jingyu Lu +4fMRI-Based Visual Decoding Neural Decoding
The Hong Kong University of Science and Technology · Shenzhen Loop Area Institute, Shenzhen, China
Replaying the Movie in Your Mind Decoding Dynamic Visual Perception and Recall from EEG
Oct 9, 2026Zikai Fang, Tian-Yi Zhou, Xuan-Hao Liu +5EEG-Based Visual Decoding Neural Decoding
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China
FlyHack: Stress-Testing Mechanistic Claims in a Connectome-Constrained Visual Model
Oct 9, 2026Aashish DhawanMechanistic Interpretability Visual Neuroscience
Department of Computer & Information Science & Engineering University of Florida
Structure alone supports efficient visual computation in the Drosophila visual system
Oct 7, 2026Eudald Correig-Fraga, Roger Guimerà, Marta Sales-PardoVisual Neuroscience
Innovamat Education, Sant Cugat del Vallès, Catalonia · Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Catalonia · Center for Computational Science and Applied Mathematics (ComSCIAM), Universitat Rovira i Virgili, Tarragona, Catalonia +1
ReGraph: A Computational Account of Emergent Generalization in the "what" and "where" Dual Visual Streams
Oct 6, 2026Hyewon Kang, Jungmin Lee, Ilgyu Lee +1Spatial Generalization Visual Neuroscience
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea · Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Republic of Korea · Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
Future Video Generation Better Aligns with the Human Visual Cortex than Observed Video
Sep 30, 2026Chang-Bae Bang, Hyungjin Chung, Byung-Hoon KimVideo Diffusion Models Predictive Coding
Department of Psychiatry, Yonsei University College of Medicine · Institute of Behavioral Sciences in Medicine, Yonsei University College of Medicine · Department of Biomedical Systems Informatics, Yonsei University College of Medicine +2
Receptive-field-constrained stimulus optimization for human early and intermediate visual cortex
Sep 28, 2026Junru Zhao, Hanfei Guo, Andrew Luo +1Visual Neuroscience Neural Encoding
Carnegie Mellon University · University of Hong Kong
Cross-attention encoding models reveal dynamic spatiotemporal routing across human higher visual cortex
Sep 28, 2026Iishaan Inabathini, Margaret M. HendersonCross-Attention Visual Neuroscience
Carnegie Mellon University
Structured Bayesian Modeling of Dynamic Receptive1 Fields in Salamander Retinal Ganglion Cells
Sep 25, 2026Alokesh MannaVisual Neuroscience
Department of Statistics, Texas A&M University, College Station, TX, USA
Do Vision Model See Like the Brain? A Comparison Across EEG Encoding Model
Sep 22, 2026Shashank Baghel, Kshitij Dwivedi, Dinesh Singh +1Vision Transformer Visual Neuroscience
Centre for Human-Computer Interaction, Indian Institute of Technology, Mandi, Mandi, 175075, Himachal Pradesh, India · Freie Universität, Berlin, Germany · Goethe University, Frankfurt, Germany
A Bio-Plausible Visual Neural Network for Locust-Inspired Collision Perception
Sep 9, 2026Qinbing Fu, Jiani Li, Jiajun Huang +1Visual Neuroscience Spiking Neural Networks
School of Mathematics and Information Science, Guangzhou University, China
Primate vision reveals a missing principle for robust dynamic AI
Aug 24, 2026Matteo Dunnhofer, Christian Micheloni, Kohitij KarVideo Representation Learning Visual Neuroscience
York University, Department of Biology and Centre for Vision Research, Toronto, Canada · Department of Mathematics, Computer Science, and Physics, University of Udine, Udine, Italy
Evaluation Resolution Confounds Learning-Rule Comparisons in Model-Brain RSA of Early Visual Cortex
Aug 11, 2026Nils LeuteneggerRepresentational Similarity Analysis Visual Neuroscience
Independent Researcher, Switzerland
IRIS: A Visual Cortex-Inspired Framework for Analyzing Orientation Selectivity in Vision Transformers
Aug 5, 2026Vaishnavi B Mohan, Vijayakrishna Naganoor, Yashas Annadani +1Transformer Interpretability Visual Representation Learning
University of Washington, USA · Microsoft, USA · TU Munich, Germany +1
A Bayes-Markov Neuromorphic Model of Cortical Orientation Selectivity: A Computational Re-implementation and Quantitative Simulation Study
Aug 1, 2026Abolfazl Moslemi, Milad Sarabadani, Fatemeh Sefidian +1Markov Random Fields Bayesian Inference
Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran · School of Mathematics, Statistics, and Computer Science, Faculty of Science, University of Tehran, Tehran, Iran · Department of Psychology, Islamic Azad University, Tehran, Iran +1
Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learning
Jul 21, 2026Stella Ho, Joel Villalobos, Joseph West +7Neural Decoding Visual Neuroscience
Department of Biomedical Engineering and Graeme Clark Institute The University of Melbourne Victoria, Australia · School of Computing and Information Systems The University of Melbourne Victoria, Australia · Bionics Institute Victoria, Australia +2
Toward a mechanistic understanding of inference in visual cortex and diffusion models
Jul 17, 2026Zeyu Yun, Alexander Belsten, Dasheng Bi +3Mechanistic Interpretability Visual Neuroscience
Dept. of Electrical Engineering and Computer Science, UC Berkeley · Redwood Center for Theoretical Neuroscience, UC Berkeley · Herbert Wertheim School of Optometry and Vision Science, UC Berkeley +3
STSBench: A Large-Scale Dataset for Modeling Neuronal Activity in the Dorsal Stream of Primate Visual Cortex
Jul 17, 2026Ethan B. Trepka, Ruobing Xia, Shude Zhu +6Neural Decoding Visual Neuroscience
Neuroscience Interdepartmental Program, Stanford University, Stanford, CA · Department of Neurobiology, Stanford University, Stanford, CA · Howard Hughes Medical Institute, Stanford University, Stanford, CA +1
NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity
Jul 2, 2026Yingtian Tang, Sogand Salehi, Ming Zhou +3Visual Neuroscience
EPFL · Johns Hopkins University
Heterogeneous synaptic motifs bridge microscale structure and macroscale nonlinear dynamics
Jun 26, 2026Meiyi Zhang, Jinjian Yu, Louis Tao +1Dynamical Systems Recurrent Neural Networks
Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, China · Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China · School of Physics, Peking University, China +7
Dual-Stream EEG Decoding for 3D Visual Perception
Jun 20, 2026Ninon Lizé Masclef, Taisija Demcenko, Antonella Catanzaro +1EEG-Based Visual Decoding Visual Neuroscience
Massachusetts Institute of Technology
Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction
Jun 3, 2026Hoang-Son Vo, Van-Hung Bui, Minh-Huy Mai-Duc +2Hierarchical Representation Learning Visual SSMs
Chonnam National University, Gwangju, Republic of Korea · Vietnam National University - Ho Chi Minh City, University of Science, Vietnam · Institute for Cybersecurity and Digital Technologies, Russia
Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning Rules
May 28, 2026Nils LeuteneggerNeural Representation Geometry Predictive Coding
Independent Researcher, Switzerland
Brain-IT-VQA: From Brain Signals to Answers
May 28, 2026Roman Beliy, Matias Cosarinsky, Oliver Heinimann +2Visual Question Answering Neural Decoding
Weizmann Institute of Science
Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images
May 27, 2026Joséphine Raugel, Maximilian Seitzer, Marc Szafraniec +6Backpropagation Visual Representation Learning
Meta AI · Ecole Normale Supérieure, PSL University
From Activation to Causality: Discovery of Causal Visual Representations in the Human Brain
May 22, 2026Yuval Golbari, Navve Wasserman, Matias Cosarinsky +5Causal Representation Learning Causal Discovery
Weizmann Institute of Science · Massachusetts Institute of Technology
Cross-Species RSA Reveals Conserved Early Visual Alignment but Divergent Higher-Area Rankings Across Human fMRI and Macaque Electrophysiology
May 21, 2026Nils LeuteneggerRepresentational Similarity Analysis Visual Representation Learning
Independent Researcher, Switzerland
Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry
May 19, 2026Pablo Marcos-Manchón, Rishi Jha, Lluís FuentemillaNeural Representation Geometry Unsupervised Representation Learning
Department of Cognition, Development and Education Psychology, University of Barcelona · Institute of Neurosciences, University of Barcelona · Department of Computer Science, Cornell University +1

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Alignment of visual brain encoding models with individual perceptual similarity judgments

Daniel Chong, Haomiao Chen, Keith W Jamison, Raja Marjieh+3

Cornell University · Cornell Tech · Weill Cornell Medicine+2

Neural Encoding Visual Neuroscience fMRI Representation Learning

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Encoding models that predict brain activation responses to images have traditionally been trained to maximize accuracy when predicting observed brain data. However, their internal representations may not reflect individual human perception. In this work, we investigate whether we can fine-tune brain encoding models to better align their representations with individuals' similarity judgments. Using similarity judgments of 100 images from 8 participants in the Natural Scenes Dataset (NSD), we adapt a generic human similarity judgment model (DreamSim) separately for each participant to obtain personalized perceptual similarity models. These models are then used to obtain subject-specific similarity structure across images in their encoding model training sets. We fine-tune each participant's baseline encoder in two conditions, aligning its representations with either generic or personalized similarity judgments, with the same initialization and objective. We find that baseline brain encoders reflect within-subject perceptual similarity more strongly than measured fMRI responses and preserve intersubject correlations present in measured fMRI, but fail to reflect those of human perceptual similarity judgments. Fine-tuning with generic, population-level perceptual similarity information results in better alignment with human judgments than baseline encoders, but these models still fail to capture intersubject correlations of human judgments. However, fine-tuning with personalized perceptual similarity information results in models that are the most aligned with human perceptual similarity judgments and also reflect intersubject correlations of human judgments. Our framework provides an approach for bootstrapping human similarity judgment data into more individualized, human-aligned brain encoding models of vision.