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Andrew Luo

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10 papers

2 author rows

AAAI Conference 2026 Conference Paper

BrainLMM: A Label-Free Framework for Mapping Multi-Semantic Representation in the Human Visual Cortex

Tan Gao
Mufan Xue
Haofang Zheng
Shuo Lv
Jia Xu
Dabin Sheng
Ziming Mao
Xinyu Wu

Previous studies leveraging artificial neural networks have been used to investigate the semantic coding within human visual cortex. However, building an interpretable label-free framework that can effectively map brain responses to multiple coexisting semantic concepts remains largely unexplored. Here, we propose BrainLMM, a label-free framework for multi-semantic mapping of voxel responses by combining diverse vision encoders with the Describe-and-Dissect strategy, enabling a hypothesis-free analysis of the human high-level visual cortex. First, we construct voxel-wise encoding models leveraging diverse vision encoders to predict visual cortical responses to natural scene images. Then, we use BrainLMM to map individual brain voxels to multiple semantics without requiring any predefined labels. To evaluate the effectiveness of our method, we compute Pearson correlation coefficients to compare the multi-semantic mappings produced by BrainLMM and CLIP-MSM with ground-truth voxel responses within selective cortical areas. Our findings indicate that BrainLMM achieves more accurate predictions of visual responses compared to CLIP-MSM. Finally, to demonstrate the multi-semantic mapping capability of our method, we project multiple representative semantic concepts onto the cortical surface for visualization. Our method enables the discovery of voxels that exhibit strong activation in response to previously undefined semantic concepts across two independent datasets: the Natural Scenes Dataset (NSD) and the Natural Object Dataset (NOD).

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NeurIPS Conference 2025 Conference Paper

In Silico Mapping of Visual Categorical Selectivity Across the Whole Brain

Ethan Hwang
Hossein Adeli
Wenxuan Guo
Andrew Luo
Nikolaus Kriegeskorte

A fine-grained account of functional selectivity in the cortex is essential for understanding how visual information is processed and represented in the brain. Classical studies using designed experiments have identified multiple category-selective regions; however, these approaches rely on preconceived hypotheses about categories. Subsequent data-driven discovery methods have sought to address this limitation but are often limited by simple, typically linear encoding models. We propose an in silico approach for data-driven discovery of novel category-selectivity hypotheses based on an encoder–decoder transformer model. The architecture incorporates a brain-region to image-feature cross-attention mechanism, enabling nonlinear mappings between high-dimensional deep network features and semantic patterns encoded in the brain activity. We further introduce a method to characterize the selectivity of individual parcels by leveraging diffusion-based image generative models and large-scale datasets to synthesize and select images that maximally activate each parcel. Our approach reveals regions with complex, compositional selectivity involving diverse semantic concepts, which we validate in silico both within and across subjects. Using a brain encoder as a “digital twin” offers a powerful, data-driven framework for generating and testing hypotheses about visual selectivity in the human brain—hypotheses that can guide future fMRI experiments. Our code is available at: https: //kriegeskorte-lab. github. io/in-silico-mapping-web/.

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NeurIPS Conference 2025 Conference Paper

Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex

Muquan Yu
Mu Nan
Hossein Adeli
Jacob Prince
John A. Pyles
Leila Wehbe
Maggie Henderson
Michael Tarr

Understanding functional representations within higher visual cortex is a fundamental question in computational neuroscience. While artificial neural networks pretrained on large-scale datasets exhibit striking representational alignment with human neural responses, learning image-computable models of visual cortex relies on individual-level, large-scale fMRI datasets. The necessity for expensive, time-intensive, and often impractical data acquisition limits the generalizability of encoders to new subjects and stimuli. BraInCoRL uses in-context learning to predict voxelwise neural responses from few-shot examples without any additional finetuning for novel subjects and stimuli. We leverage a transformer architecture that can flexibly condition on a variable number of in-context image stimuli, learning an inductive bias over multiple subjects. During training, we explicitly optimize the model for in-context learning. By jointly conditioning on image features and voxel activations, our model learns to directly generate better performing voxelwise models of higher visual cortex. We demonstrate that BraInCoRL consistently outperforms existing voxelwise encoder designs in a low-data regime when evaluated on entirely novel images, while also exhibiting strong test-time scaling behavior. The model also generalizes to an entirely new visual fMRI dataset, which uses different subjects and fMRI data acquisition parameters. Further, BraInCoRL facilitates better interpretability of neural signals in higher visual cortex by attending to semantically relevant stimuli. Finally, we show that our framework enables interpretable mappings from natural language queries to voxel selectivity.

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NeurIPS Conference 2025 Conference Paper

SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning

Weijian Mai
Jiamin Wu
Yu Zhu
Zhouheng Yao
Dongzhan Zhou
Andrew Luo
Qihao Zheng
Wanli Ouyang

Deciphering how visual stimuli are transformed into cortical responses is a fundamental challenge in computational neuroscience. This visual-to-neural mapping is inherently a one-to-many relationship, as identical visual inputs reliably evoke variable hemodynamic responses across trials, contexts, and subjects. However, existing deterministic methods struggle to simultaneously model this biological variability while capturing the underlying functional consistency that encodes stimulus information. To address these limitations, we propose SynBrain, a generative framework that simulates the transformation from visual semantics to neural responses in a probabilistic and biologically interpretable manner. SynBrain introduces two key components: (i) BrainVAE models neural representations as continuous probability distributions via probabilistic learning while maintaining functional consistency through visual semantic constraints; (ii) A Semantic-to-Neural Mapper acts as a semantic transmission pathway, projecting visual semantics into the neural response manifold to facilitate high-fidelity fMRI synthesis. Experimental results demonstrate that SynBrain surpasses state-of-the-art methods in subject-specific visual-to-fMRI encoding performance. Furthermore, SynBrain adapts efficiently to new subjects with few-shot data and synthesizes high-quality fMRI signals that are effective in improving data-limited fMRI-to-image decoding performance. Beyond that, SynBrain reveals functional consistency across trials and subjects, with synthesized signals capturing interpretable patterns shaped by biological neural variability. Our code is available at https: //github. com/MichaelMaiii/SynBrain.

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NeurIPS Conference 2025 Conference Paper

Vision Transformers with Self-Distilled Registers

Zipeng Yan
Yinjie Chen
Chong Zhou
Bo Dai
Andrew Luo

Vision Transformers (ViTs) have emerged as the dominant architecture for visual processing tasks, demonstrating excellent scalability with increased training data and model size. However, recent work has identified the emergence of artifact tokens in ViTs that are incongruous with local semantics. These anomalous tokens degrade ViT performance in tasks that require fine-grained localization or structural coherence. An effective mitigation of this issue is the addition of register tokens to ViTs, which implicitly ''absorb'' the artifact term during training. Given the availability of existing large-scale pre-trained ViTs, in this paper we seek to add register tokens to existing models without retraining the models from scratch, which is infeasible considering their size. Specifically, we propose Post Hoc Registers ( PH-Reg ), an efficient self-distillation method that integrates registers into an existing ViT without requiring additional labeled data and full retraining. PH-Reg initializes both teacher and student networks from the same pre-trained ViT. The teacher remains frozen and unmodified, while the student is augmented with randomly initialized register tokens. By applying test-time augmentation to the teacher’s inputs, we generate denoised dense embeddings free of artifacts, which are then used to optimize only a small subset of unlocked student weights. We show that our approach can effectively reduce the number of artifact tokens, improving the segmentation and depth prediction of the student ViT under zero-shot and linear probing.

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NeurIPS Conference 2024 Conference Paper

Diffusion PID: Interpreting Diffusion via Partial Information Decomposition

Shaurya Dewan
Rushikesh Zawar
Prakanshul Saxena
Yingshan Chang
Andrew Luo
Yonatan Bisk

Text-to-image diffusion models have made significant progress in generating naturalistic images from textual inputs, and demonstrate the capacity to learn and represent complex visual-semantic relationships. While these diffusion models have achieved remarkable success, the underlying mechanisms driving their performance are not yet fully accounted for, with many unanswered questions surrounding what they learn, how they represent visual-semantic relationships, and why they sometimes fail to generalize. Our work presents Diffusion Partial Information Decomposition (DiffusionPID), a novel technique that applies information-theoretic principles to decompose the input text prompt into its elementary components, enabling a detailed examination of how individual tokens and their interactions shape the generated image. We introduce a formal approach to analyze the uniqueness, redundancy, and synergy terms by applying PID to the denoising model at both the image and pixel level. This approach enables us to characterize how individual tokens and their interactions affect the model output. We first present a fine-grained analysis of characteristics utilized by the model to uniquely localize specific concepts, we then apply our approach in bias analysis and show it can recover gender and ethnicity biases. Finally, we use our method to visually characterize word ambiguity and similarity from the model’s perspective and illustrate the efficacy of our method for prompt intervention. Our results show that PID is a potent tool for evaluating and diagnosing text-to-image diffusion models. Link to project page: https: //rbz-99. github. io/Diffusion-PID/.

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IROS Conference 2024 Conference Paper

Disentangled Acoustic Fields For Multimodal Physical Scene Understanding

Jie Yin
Andrew Luo
Yilun Du
Anoop Cherian
Tim K. Marks
Jonathan Le Roux
Chuang Gan 0001

We study the problem of multimodal physical scene understanding, where an embodied agent needs to find fallen objects by inferring object properties, direction, and distance of an impact sound source. Previous works adopt feed-forward neural networks to directly regress the variables from sound, leading to poor generalization and domain adaptation issues. In this paper, we illustrate that learning a disentangled model of acoustic formation, referred to as disentangled acoustic field (DAF), to capture the sound generation and propagation process, enables the embodied agent to construct a spatial uncertainty map over where the objects may have fallen. We demonstrate that our analysis-by-synthesis framework can jointly infer sound properties by explicitly decomposing and factorizing the latent space of the disentangled model. We further show that the spatial uncertainty map can significantly improve the success rate for the localization of fallen objects by proposing multiple plausible exploration locations.

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NeurIPS Conference 2023 Conference Paper

Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models

Andrew Luo
Maggie Henderson
Leila Wehbe
Michael Tarr

A long standing goal in neuroscience has been to elucidate the functional organization of the brain. Within higher visual cortex, functional accounts have remained relatively coarse, focusing on regions of interest (ROIs) and taking the form of selectivity for broad categories such as faces, places, bodies, food, or words. Because the identification of such ROIs has typically relied on manually assembled stimulus sets consisting of isolated objects in non-ecological contexts, exploring functional organization without robust a priori hypotheses has been challenging. To overcome these limitations, we introduce a data-driven approach in which we synthesize images predicted to activate a given brain region using paired natural images and fMRI recordings, bypassing the need for category-specific stimuli. Our approach -- Brain Diffusion for Visual Exploration ("BrainDiVE") -- builds on recent generative methods by combining large-scale diffusion models with brain-guided image synthesis. Validating our method, we demonstrate the ability to synthesize preferred images with appropriate semantic specificity for well-characterized category-selective ROIs. We then show that BrainDiVE can characterize differences between ROIs selective for the same high-level category. Finally we identify novel functional subdivisions within these ROIs, validated with behavioral data. These results advance our understanding of the fine-grained functional organization of human visual cortex, and provide well-specified constraints for further examination of cortical organization using hypothesis-driven methods.

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NeurIPS Conference 2022 Conference Paper

Learning Neural Acoustic Fields

Andrew Luo
Yilun Du
Michael Tarr
Josh Tenenbaum
Antonio Torralba
Chuang Gan

Our environment is filled with rich and dynamic acoustic information. When we walk into a cathedral, the reverberations as much as appearance inform us of the sanctuary's wide open space. Similarly, as an object moves around us, we expect the sound emitted to also exhibit this movement. While recent advances in learned implicit functions have led to increasingly higher quality representations of the visual world, there have not been commensurate advances in learning spatial auditory representations. To address this gap, we introduce Neural Acoustic Fields (NAFs), an implicit representation that captures how sounds propagate in a physical scene. By modeling acoustic propagation in a scene as a linear time-invariant system, NAFs learn to continuously map all emitter and listener location pairs to a neural impulse response function that can then be applied to arbitrary sounds. We demonstrate NAFs on both synthetic and real data, and show that the continuous nature of NAFs enables us to render spatial acoustics for a listener at arbitrary locations. We further show that the representation learned by NAFs can help improve visual learning with sparse views. Finally we show that a representation informative of scene structure emerges during the learning of NAFs.

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ICLR Conference 2022 Conference Paper

Prototype memory and attention mechanisms for few shot image generation

Tianqin Li
Zijie Li
Andrew Luo
Harold Rockwell
Amir Barati Farimani
Tai Sing Lee

Recent discoveries indicate that the neural codes in the primary visual cortex (V1) of macaque monkeys are complex, diverse and sparse. This leads us to ponder the computational advantages and functional role of these “grandmother cells." Here, we propose that such cells can serve as prototype memory priors that bias and shape the distributed feature processing within the image generation process in the brain. These memory prototypes are learned by momentum online clustering and are utilized via a memory-based attention operation, which we define as Memory Concept Attention (MoCA). To test our proposal, we show in a few-shot image generation task, that having a prototype memory during attention can improve image synthesis quality, learn interpretable visual concept clusters, as well as improve the robustness of the model. Interestingly, we also find that our attentional memory mechanism can implicitly modify the horizontal connections by updating the transformation into the prototype embedding space for self-attention. Insofar as GANs can be seen as plausible models for reasoning about the top-down synthesis in the analysis-by-synthesis loop of the hierarchical visual cortex, our findings demonstrate a plausible computational role for these “prototype concept" neurons in visual processing in the brain.

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