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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：
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
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/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

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/文摘