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Fast and Flexible Conjunction Coding in Biological and Artificial Vision

生物和人工视觉中快速灵活的联合编码

基本信息

批准号：
10389898
负责人：
JohnMark Edward Taylor
金额：
$ 6.68万
依托单位：
COLUMBIA UNIV NEW YORK MORNINGSIDE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-16 至 2025-04-15
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10389898
关键词：
Anatomy Architecture Artificial Intelligence Attention Automobile Driving Behavior Biological Brain Code Cognitive Cognitive Science Color Complex Computer Models Computer Vision Systems Crowding Data Set Development Doctor of Philosophy Engineering Environment Design Exhibits Feedback Functional Magnetic Resonance Imaging Future Glass Homologous Gene Human Image Industrialization Influentials Lateral Link Literature Mentorship Methodology Methods Modeling Modernization Names Natural experiment Neurologic Deficit Neurons Neurosciences Patients Population Prevalence Psychophysics Recurrence Research Safety Shapes Stimulus Stream Sum Syndrome Techniques Technology Testing Toy Training Vision Visual Visual Agnosias Visual system structure Work artificial neural network base career cognitive neuroscience computational neuroscience deep learning deep neural network experience experimental study flexibility human model improved neuroimaging neurophysiology novel object recognition open source operation presynaptic relating to nervous system response synergism theories vision science visual search

项目摘要

Project Summary How does the human brain encode visual feature conjunctions? Two inﬂuential, yet disparate, research traditions have proposed two different mechanisms. Research in neurophysiology and computer vision has explored static conjunction coding, where feature conjunctions are automatically extracted via a feedforward hierarchy. While efﬁcient, this mechanism may be limited to encoding conjunctions enabled by the hierarchy's learned connectivity. By contrast, research in cognitive psychology has explored dynamic conjunction coding, which sequentially encodes task-relevant conjunctions via attentional selection. While slow, this mechanism is ﬂexible and capable of encoding any feature conjunction. Despite evidence for both mechanisms, their interplay remains unclear. In this project, we leverage advances in deep learning and open neuroimaging datasets to understand how these two mechanisms interact in the human brain, yielding the best of both worlds: fast, but ﬂexible conjunction coding. Through the three complementary Speciﬁc Aims, we advance our understanding of these fundamental issues in vision science, and on a practical level, develop approaches that can be used to improve computer vision, aiding the development of useful technologies like autonomous vehicles. In the course of this project, I will master modern approaches in deep learning and computational neuroscience through the mentorship of my sponsor Dr. Kriegeskorte and my co-sponsor Dr. Fusi, equipping me for a career leading a lab that bridges cognitive science, neuroscience, and artiﬁcial intelligence. Hypotheses: The human brain implements static conjunction coding via neural populations with “and-like” tuning to feature combinations that emerges via feedforward convergence of neurons tuned to single features, while dy- namic conjunction coding requires recurrent connections. Static conjunction coding can rapidly encode familiar, but not unfamiliar conjunctions, while dynamic conjunction coding can encode any conjunction, but more slowly. Aim 1: We use a massive open-source fMRI dataset to chart the prevalence of static conjunction coding through- out the human visual system, using a method we developed in preliminary analyses. Aim 2: We apply “synthetic neurophysiology” to feedforward artiﬁcial neural networks to understand how static conjunction coding emerges in a feedforward hierarchy, beginning by characterizing conjunction-tuned units iden- tiﬁed in preliminary analyses, followed by testing inﬂuential models for how this occurs in biological vision. Aim 3: We test whether feedforward artiﬁcial neural networks exhibit similar limitations on a visual search task compared to known results for human feedforward vision, followed by testing whether introducing recurrent con- nections to networks can overcome these limitations.

项目总结