Shape Learning: Computational Changes in Chronically Studied Neural Populations

形状学习：长期研究的神经群体的计算变化

基本信息

批准号：
9248364
负责人：
CHARLES E CONNOR
金额：
$ 42.46万
依托单位：
JOHNS HOPKINS UNIVERSITY
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：
2015-04-01 至 2019-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9248364
关键词：
Agnosia Autistic Disorder Behavior Behavioral Blindness Categories Chronic Code Complex Data Development Discrimination Disease Dyslexia Electrophysiology (science)Future Genetic Programming Geometry Goals Human Implant Individual Investigation Laboratories Lead Learning Lesion Mathematics Measures Methodology Monkeys Neurons Pathway interactions Patients Pattern Perception Performance Physiological Population Positioning Attribute Primates Prosthesis Recurrence Regression Analysis Rehabilitation therapy Sampling Shapes Signal Transduction Speed Stimulus Testing Time Training Visual Visual Cortex Visual impairment base cell cortex combinatorial feeding inferotemporal cortex information processing insight mathematical analysis neural circuit public health relevance relating to nervous system response shape analysis visual learning

项目摘要

DESCRIPTION (provided by applicant): Learning to discriminate new shapes is a fundamental visual ability for humans and other primates. It depends on long-term changes in shape computations in the ventral pathway of primate visual cortex, especially at final stages in IT (inferotemporal cortex). Our goal is to investigate these changes at the level of individual neurons and neural circuits, by (i) analyzing progressive shape computation changes in continuously identified neural populations across long timescales (weeks to months) and (ii) correlating these changes with improvements in shape discrimination accuracy and speed. We would achieve this goal by combining methodologies developed in our two laboratories. The Connor lab has developed mathematical analyses of neural shape computations, based on large-scale adaptive stimulus sampling guided by genetic algorithms and multi- dimensional parameterization of stimulus geometry. The Leopold lab has developed the use of microwire bundle implants for long-term electrophysiological recording from populations of IT neurons, continuously identified by their signature response patterns across 100s of stimuli. Adaptive sampling can leverage the order of magnitude increase in sampling time with microwire bundles, offering a new paradigm for high- throughput testing of mathematically tractable object stimuli in ventral pathway cortex. Based on our previous investigations of shape coding and shape processing dynamics, we hypothesize that learning to discriminate a new shape accurately and rapidly is based on a progression through distinct combinatorial computations operating on that shape's constituent fragments: (i) Initial low-accuracy behavior reflects linear combination of shape fragment signals, present in the untrained state, yielding only ambiguous information about complex shape configurations; (ii) Increasing accuracy during early learning reflects recurrent network nonlinear computations, yielding slow but unambiguous signals for shape fragment combinations; (iii) Increasing speed during late learning reflects feed-forward nonlinear computations, yielding accurate, fast performance. Chronic microwire recording will allow us to track this computational progression, for dozens of individual neurons, and correlate computational changes with behavioral improvements through time. This would be the first continuous observation of computational changes in individual IT cells during extended periods of visual learning (weeks to months). Whether or not the specific hypotheses are verified, this will provide the most direct insights to date into how specific changes in IT circuit-level information processing relate to shape learning, which is critical to our understanding of symbols and objects.

描述（由适用提供）：学习区分新形状是人类和其他主要的基本视觉能力。这取决于主要视觉皮层的腹侧途径中形状计算的长期变化，尤其是在IT的最后阶段（颞下皮皮层）。我们的目标是通过（i）分析在长时间尺度（几周到几个月）的连续鉴定的神经元种群中的渐进形状计算变化，研究这些变化，以及（ii）将这些变化与形状歧视精度和速度的改善相关联。我们将通过结合两个实验室中开发的方法来实现这一目标。康纳实验室（Connor Lab）基于遗传算法和刺激几何学的多维参数化引导的大规模自适应刺激采样，开发了神经元形状计算的数学分析。利奥波德实验室（Leopold Lab）开发了使用Microwire束机器来从IT神经元种群中进行的长期电生理记录，该记录通过100s刺激中的签名响应模式不断识别。自适应采样可以利用微型束束的抽样时间增加数量级，从而为腹侧途径皮层中数学上可牵引的物体刺激提供新的范式，以进行高通量测试。基于我们先前对形状编码和形状处理动力学的研究，我们假设学会准确，迅速地区分新的形状是基于通过在该形状的一致性碎片上运行的不同组合计算进行的进展：（i）最初的低智能行为行为是形状碎片的线性组合，反映了形状碎片的线性组合，仅在未经传播的状态中出现，仅构成了复杂的形状信息，就可以进行复杂的信息。（ii）在早期学习期间提高准确性反映了反复的网络非线性计算，产生了形状碎片组合的缓慢但明确的信号；（iii）在晚期学习期间提高速度，反映了前进的非线性计算，得出准确，快速的性能。慢性微导期的记录将使我们能够跟踪数十个单个神经元的计算进程，并将计算变化与随着时间的推移行为改善相关联。这将是在视觉学习的长时间（数周到几个月）中对单个IT细胞中单个IT细胞中计算变化的第一次连续观察。无论是否验证了特定的假设，这将为迄今为止的最直接见解，以了解IT电路级信息处理中的特定变化与形状学习有关，这对于我们对符号和对象的理解至关重要。