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

基本信息

批准号：
10320050
负责人：
John T Serences
金额：
$ 38.32万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
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/10320050
关键词：
Afferent Neurons Area Attention Automobile Driving Behavior Behavioral Cells Clinical Code Diagnosis Dimensions Environment Foundations Frequencies Functional Magnetic Resonance Imaging Goals Human Intervention Joints Lead Learning Measures Mediating Memory Modeling Motion Motor Neurons Neurosciences Occipital lobe Outcome Parietal Lobe Pattern Population Prefrontal Cortex Psychophysics Response to stimulus physiology Role Scheme Sensory Short-Term Memory Signal Transduction Stimulus Testing United States National Institutes of Health Visual Visual Cortex Work active vision base experimental study extrastriate visual cortex flexibility frontal lobe high dimensionality human subject information processing nonhuman primate novel operation prospective relating to nervous system response selective attention sensory input sensory integration teacher theories visual information visual processing

项目摘要

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/摘要主动视觉需要根据当前任务目标编码和记住相关信息。经典观点认为，感觉编码、注意力选择和工作记忆是由具有固定调谐曲线的视觉反应神经元的放电率或增益持续变化（称为“纯”或“固定选择性”神经元）对固定选择性神经元的增益调制的关注已成为焦点。揭示了大量关于注意力和记忆的基本机制，但它正在变得越来越多。显然，任务需求的动态变化可能需要更灵活的编码方案，例如，保持。工作记忆中的信息与刺激诱发反应的格式相同可能会导致干扰类似地，灵活地编码感官表征来完成一项任务——比如说一个简单的任务。两种运动反应之间的选择——如果有另一个刺激，可能需要重新配置表示—— 最后，感觉编码必须在早期就变得灵活。在处理过程中，它们应该形成高维表示来表示尽可能多的信息稍后在处理过程中，当需要做出决定或运动反应时，代码应该折叠为仅表示相关选择的较小子集。通过神经元的操作更自然地完成，这些神经元对感觉特征和用于任务需求（称为“混合选择性”神经元）。基于这些考虑，我们勇敢地认为灵活的行为是由混合选择性支持的 “旋转”高维神经代码以变得对干扰具有鲁棒性或为其他神经元提供服务的神经元我们将使用建模、心理物理学和功能磁共振成像。（fMRI）来测试关于混合选择性如何调节大规模激活模式的预测总的来说，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、注意力和工作记忆，并将提供对视觉信息处理模型的重要约束，以支持更有针对性的诊断和临床环境中的干预措施。