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

• 批准号: 8858962
• 负责人: CHARLES E CONNOR
• 金额: $ 31.99万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 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/8858962
• 关键词: Agnosia; Autistic Disorder; Behavior; Behavioral; Blindness; Categories; Cells; Chronic; Code; Complex; Data; Development; Discrimination; Disease; Dyslexia; Future; Genetic Programming; Geometry; Goals; Human; Implant; Individual; Investigation; Laboratories; Lead; Learning; Lesion; Measures; Methodology; Monkeys; Neurons; Pathway interactions; Patients; Pattern; Perception; Performance; Physiological; Population; Positioning Attribute; Primates; Process; Prosthesis; Recurrence; Regression Analysis; Rehabilitation therapy; Relative (related person); Sampling; Shapes; Signal Transduction; Speed; Staging; Stimulus; Testing; Time; Training; Visual; Visual Cortex; base; combinatorial; feeding; inferotemporal cortex; information processing; insight; neural circuit; public health relevance; relating to nervous system; response; shape analysis; visual learning

 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。它取决于灵长类视觉皮层腹侧通路形状计算的长期变化，尤其是在 IT（颞下皮层）的最后阶段。我们的目标是在单个神经元和神经回路水平上研究这些变化，方法是：（i）分析在长时间尺度（数周到数月）内连续识别的神经群体中渐进的形状计算变化，以及（ii）将这些变化与形状辨别准确性和速度的改进相关联。我们将通过结合我们两个实验室开发的方法来实现这一目标。康纳实验室基于遗传算法和刺激几何的多维参数化引导的大规模自适应刺激采样，开发了神经形状计算的数学分析。 Leopold 实验室开发了使用微丝束植入物对 IT 神经元群进行长期电生理记录，通过数百个刺激的特征反应模式不断识别这些神经元。自适应采样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。基于我们之前对形状编码和形状处理动力学的研究，我们假设学习准确快速地区分新形状是基于通过对该形状的组成片段进行不同组合计算的进展：（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，仅产生有关复杂形状配置的模糊信息； (ii) 早期学习期间准确性的提高反映了循环网络非线性计算，为形状片段组合产生缓慢但明确的信号； (iii) 后期学习期间速度的提高反映了前馈非线性计算，从而产生准确、快速的性能。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进展，并将计算变化与随时间的行为改善联系起来。这将是首次在长时间的视觉学习（几周到几个月）期间连续观察单个 IT 细胞的计算变化。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，这对于我们理解符号和物体至关重要。