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（由申请人提供）：学习辨别新形状是人类和其他灵长类动物的基本视觉能力，它取决于灵长类动物视觉皮层腹侧通路形状计算的长期变化，尤其是在 IT（颞下）的最后阶段。我们的目标是通过（i）分析长时间尺度（几周到几个月）连续识别的神经群体的渐进形状计算变化来研究单个神经元和神经回路的这些变化。将这些变化与形状辨别准确性和速度的提高联系起来，康纳实验室开发了基于遗传算法引导的大规模自适应刺激采样的神经形状计算的数学分析，从而实现了这一目标。 Leopold 实验室开发了使用微线束植入物来记录 IT 神经元群体的长期电生理学记录，并通过其在 100 个刺激中的特征反应模式来持续识别。可以利用微丝束的采样时间的数量级增加，为腹侧通路皮层中数学上可处理的物体刺激的高通量测试提供新的范例。根据我们之前对形状编码和形状处理动力学的研究，我们捕获了这种学习。准确快速地区分新形状的基础是通过对该形状的组成片段进行不同的组合计算来进行操作：（i）初始低精度行为反映了形状片段信号的线性组合，存在于未经训练的状态，产生仅关于复杂形状配置的模糊信息；（ii）在早期学习循环网络非线性计算期间提高准确性，为形状片段组合产生缓慢但明确的信号；（iii）在后期学习前馈非线性计算期间提高速度，产生准确、快速的信号。慢性微丝记录将使我们能够跟踪数十个单个神经元的计算行为进展，并将计算变化与随时间的改进相关联，这将是对个体计算变化的首次连续观察。无论具体假设是否得到验证，这都将提供迄今为止最直接的见解，了解 IT 电路级信息处理的具体变化如何与形状学习相关。对于我们理解符号和物体至关重要。