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Learning and updating internal visual models

学习和更新内部视觉模型

基本信息

批准号：
9334881
负责人：
ADAM KOHN
金额：
$ 44.77万
依托单位：
ALBERT EINSTEIN COLLEGE OF MEDICINE, INC
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-30 至 2018-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9334881
关键词：
Affect Animals Area Autistic Disorder Behavior Biological Brain Code Computer Simulation Data Detection Development Disease Electroencephalography Elements Environment Event Fatigue Goals Health Human Individual Investigation Knowledge Learning Machine Learning Mission Modeling Monkeys Neurons Pattern Perception Process Property Prosthesis Public Health Recording of previous events Research Research Personnel Scheme Schizophrenia Sensory Signal Transduction Stimulus Strategic Planning Structure Testing Time Uncertainty Update Vision Visual Visual Cortex Visual Perception Work area V4 area striata awake base brain machine interface cognitive neuroscience computational neuroscience design discount expectation extrastriate visual cortex human subject improved neuromechanism neurophysiology novel phenomenological models prevent public health relevance relating to nervous system response statistics treatment strategy visual adaptation visual processing

项目摘要

DESCRIPTION (provided by applicant): In line with the strategic plan of the NEI, this project is focused on filling a profound gap in our understanding of neural mechanisms of visual perception. Specifically, we aim to understand how the adaptation of visual cortical circuits contributes to perception. Adaptation is a ubiquitous process by which neural processing and perception are dramatically influenced by recent visual inputs. However, the functional purpose of adaptation is poorly understood. Based on preliminary data, this project tests the hypothesis that visual adaptation instantiates a form of predictive coding, which is used to make unexpected events salient. We posit that cortical circuits learn the statistical structure of visua input in a manner that extends beyond previous fatigue- based descriptions of adaptation effects. This learning is used to discount expected features and signal novel ones. Our project will test this hypothesis through the collaborative effort of three investigators with expertise in human EEG, animal neurophysiology, and computational modeling. Aim 1 will assess the ability of cortical circuits to adapt to temporal sequences of input and to signal deviations from expected sequences. Aim 2 will evaluate the effect of stimulus uncertainty on adaptation and responses to novel events. Aim 3 will determine how adaptation dynamics and responses to novel stimuli are influenced by the temporal constancy of stimulus statistics. Each of these aims involves an experimental manipulation that yields distinct behavior from fatigue- based and predictive coding mechanisms. Thus, together our aims will provide a robust test of our core hypothesis, and provide a much richer understanding of the adaptive properties of cortical circuits. Results from our project will contribute to answering one of the continuing puzzles in visual research, which is to understand the functional purpose of adaptive mechanisms in visual perception.