Adaptive population codes for flexible visually-guided behaviors

适应性群体代码，用于灵活的视觉引导行为

• 批准号: 10531248
• 负责人: John T Serences
• 金额: $ 39.5万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10531248
• 关键词: Afferent Neurons; Area; Attention; Automobile Driving; Behavior; Behavioral; Cells; Clinical; Code; Diagnosis; Dimensions; Dissociation; Environment; Frequencies; Functional Magnetic Resonance Imaging; Goals; Human; Intervention; Joints; Learning; Maps; Measures; Mediating; Memory; Modeling; Motion; Motor; Neurons; Neurosciences; Occipital lobe; Outcome; Parietal Lobe; Pattern; Population; Prefrontal Cortex; Psychophysics; Response to stimulus physiology; Role; Rotation; Scheme; Sensory; Short-Term Memory; Signal Transduction; Stimulus; Testing; United States National Institutes of Health; Visual; Visual Cortex; Work; active vision; experimental study; extrastriate visual cortex; flexibility; frontal lobe; high dimensionality; human subject; information processing; member; neural; nonhuman primate; novel; operation; prospective; recruit; response; selective attention; sensory input; sensory integration; teacher; theories; visual information; visual processing

Summary/Abstract Active vision requires encoding and remembering relevant information based on current task goals. Classic accounts posit that sensory encoding, attentional selection and working memory are mediated by persistent changes in the firing rates, or the gain, of visually responsive neurons that have a fixed tuning profile (termed “pure” or “fixed-selectivity” neurons). The focus on gain modulations in fixed-selectivity neurons has revealed a great deal about the basic mechanisms of attention and memory. However, it is becoming increasingly clear that dynamic changes in task demands may require more flexible coding schemes. For example, holding information in working memory in the same format as the stimulus-evoked response may lead to interference with new sensory inputs. Similarly, flexibly encoding sensory representations to complete one task – say a simple choice between two motor responses – might require a reconfiguration of the representation if another stimulus- response mapping suddenly becomes relevant. Finally, sensory codes must be flexible in the sense that early in processing they should form high-dimensional representations to represent as much information as possible about the current state of the world. Later in processing, when a decision or motor response needs to be made, the code should collapse to only represent the smaller subset of relevant choices. All of these computations are more naturally accomplished via the operation of neurons that have flexible tuning for both sensory features and for task demands (termed “mixed-selectivity” neurons). Based on these considerations, we hypothesize that flexible behaviors are supported by mixed-selectivity neurons that “rotate” high-dimensional neural codes to become robust to interference or to sub-serve other changes in task demands. We will use modelling, psychophysics, and functional magnetic resonance imaging (fMRI) to test predictions about how mixed-selectivity should modulate large-scale activation patterns that are measured non-invasively in human subjects. Collectively, this work will challenge traditional theories of sensory encoding, attention, and working memory that are based on the notion of fixed-selectivity, and will provide important constraints on models of visual information processing to support more targeted diagnoses and interventions in clinical settings.

摘要/摘要 主动愿景需要基于当前的任务目标来编码和记住相关信息。 经典的帐户海报，感官编码，注意选择和工作记忆是由 具有固定调整曲线的视觉响应神经元的触发率或增益的持续变化 （称为“纯”或“固定选择性”神经元）。固定选择性神经元中的增益调制的重点 关于注意力和记忆的基本机制揭示了很多。但是，它变得越来越 清楚的是，任务需求的动态变化可能需要更灵活的编码方案。例如，举行 与刺激诱发的响应相同格式的工作记忆中的信息可能导致干扰 具有新的感官输入。同样，灵活地编码感觉表示以完成一项任务 - 说一个简单的 两个电动机响应之间的选择 - 如果另一种刺激 - 可能需要对表示形式进行重新配置。 响应映射突然变得相关。最后，感觉代码在早期的意义上必须灵活 在处理时，它们应形成高维表示以表示尽可能多的信息 关于世界的现状。后来处理，当需要做出决定或运动响应时， 该代码应崩溃以仅表示相关选择的较小子集。所有这些计算是 通过具有灵活调整的感官功能和 对于任务需求（称为“混合选择性”神经元）。 基于这些考虑因素，我们假设柔性行为是由混合选择性支持的 “旋转”高维神经元代码的神经元变得牢固地干扰或sub-serve其他 任务需求的变化。我们将使用建模，心理物理学和功能磁共振成像 （fMRI）测试有关如何调节大规模激活模式的预测 在人类受试者中无创测量。总的来说，这项工作将挑战传统的感官理论 基于固定选择性概念的编码，注意和工作记忆，并将提供 对视觉信息处理模型的重要限制，以支持更多针对性的诊断和 临床环境中的干预措施。