Stork: Probabilistic computation in the cortex of the developing human brain

RICHARD ASLIN (2016-08-02 to 2017-07-31) Probabilistic computation in the cortex of the developing human brain. Amount: $361528

发展中的人类大脑的皮层中的概率计算

Abstract

The overall objective of the present proposal is to test a specific hypothesis about how the developing human brain is able to learn new information from the visual and auditory environment in such an efficient manner during early infancy. Extensive behavioral evidence from infants confirms that they can rapidly learn new combinations of features, but it remains unclear what neural mechanism supports this learning. The hypothesis under examination in the present proposal is based on neural recordings from the visual cortex of developing ferrets, which showed that patterns of activity shifted from being stimulus driven to being predicted by small deviations from background (i.e., non-stimulus driven) activity. That is, the developing ferret brain created a probabilistic model of the most likely features in the environment and used that model as a baseline from which stimulus driven activity was compared. This probabilistic coding model is an efficient way for the brain to represent new visual features because it focuses its activity on the most likely stimuli in the environment and creates patterns of spontaneous activity that are tuned to the environmental mean. The specific aims of the present proposal are to use a newly emerging neuroimaging method, called functional near-infrared spectroscopy (fNIRS), to non-invasively measure the blood oxygenation correlates of neural activity in the visual and auditory regions of the infant brain at four ages: 6 weeks, 3 months, 6 months, and 12 months. Infants will be tested in darkness or silence and in three stimulus conditions in each sensory modality that include both complex features typical of their natural environment and simple features that rarely occur in their natural environment. The probabilistic coding model predicts a gradual progression across post-natal age in the similarity of patterns of neural activity between darkness/silence and natural environmental input, with a corresponding failure to show similarity between darkness/silence and the non-natural stimulus conditions. Should the probabilistic coding model be supported, it would enable assessments of infants from at-risk or special populations, such as Autism Spectrum Disorder, both to establish an early biomarker of brain disorders and to serve as a possible explanation for what property of the neural system is aberrant in these disorders.

本提案的总体目标是测试一个特定的假设,即关于发育中的人类大脑如何能够在婴儿早期以如此有效的方式从视觉和听觉环境中学习新信息。来自婴儿的广泛的行为证据证实他们可以快速学习新的特征组合,但仍然不清楚什么神经机制支持这种学习。本提案中正在研究的假设基于来自发育中雪貂的视觉皮层的神经记录,其显示活动模式从刺激驱动转变为通过与背景(即,非刺激驱动)活动的小偏差预测。也就是说,正在发展的雪貂脑创建了环境中最可能的特征的概率模型,并将该模型用作比较刺激驱动活动的基线。这种概率编码模型是大脑表达新视觉特征的有效方式,因为它将其活动集中在环境中最可能的刺激上,并创建调整到环境均值的自发活动模式。本提案的具体目标是使用一种新兴的神经影像学方法,称为功能性近红外光谱(fNIRS),以四种方式非侵入性地测量婴儿大脑视觉和听觉区域的神经活动的血氧水平相关性。年龄:6周,3个月,6个月和12个月。婴儿将在黑暗或沉默中以及在每种感觉形态的三种刺激条件下进行测试,其包括其自然环境中典型的复杂特征和在其自然环境中很少发生的简单特征。概率编码模型预测在黑暗/沉默和自然环境输入之间的神经活动模式的相似性中跨越产后年龄的逐渐进展,相应的未能显示黑暗/沉默与非自然刺激条件之间的相似性。如果支持概率编码模型,它将能够评估来自高危或特殊人群的婴儿,例如自闭症谱系障碍,以建立脑疾病的早期生物标志物,并作为神经元属性的可能解释。系统在这些疾病中是异常的。

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