CRCNS: Formation of stimulus selective neural assemblies in piriform cortex

CRCNS：梨状皮层刺激选择性神经组件的形成

基本信息

批准号：
9049840
负责人：
Brent D. Doiron
金额：
$ 28.59万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
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：
2015-08-01 至 2018-07-31
项目状态：
已结题

项目摘要

Sensory cortex decomposes complex inputs into feature-based components, and distributes their representation over populations of neurons. In special cases this distribution is very clear--for instance the visual and auditory system respectively map retinal image and acoustic frequency along a spatial dimension within cortex. In general, however, rich sensory scenes are a mixture of features, and it remains unclear how the cortex mingles and segregates aspects of a complex sensory representation across neural populations. Olfactory stimuli are extraordinarily complex, with distinct odors comprised from a mixture of compounds. The circuitry in olfactory cortex is equally intricate, with populations of neurons coupling to one another with seemingly random rules, and receiving similarly random projections from lower centers. The combination of these two facts obfuscates the organization of odor representation. Our proposal leverages advances in experimental circuit identification and manipulation, as well as theoretical frameworks for large-scale cortical networks, to establish principles for the distribution of odor identity and concentration coding across olfactory cortex. Specifically, we aim to establish the following links between olfactory circuitry and odor coding: 1 )There exists a graded distribution of specific inhibitory sub-circuits along the rostral-caudal axis of the olfactory cortex. 2) The rostral-caudal distribution of inhibition interacts with Hebbian plasticity mechanisms so as to shape the odor selectivity of cells along the rostral-caudal axis. These advances will provide much needed insights into how cortical structures represent and process distinct aspects of an odor scene.

Sensory Cortex将复杂的输入分解为基于特征的组件，并在神经元种群上分发它们的表示。在特殊情况下，此分布非常清晰 - 例如，视觉和听觉系统分别沿皮质内的空间尺寸绘制视网膜图像和声学频率。然而，通常，丰富的感觉场景是特征的混合，尚不清楚皮质混合和分离神经种群中复杂的感觉表示的各个方面。嗅觉刺激非常复杂，具有不同化合物混合物的不同气味。嗅皮层中的电路同样复杂，神经元的种群与看似随机的规则相互耦合，并从下中心接收类似的随机项目。这两个事实的结合掩盖了气味代表的组织。我们的建议利用实验电路识别和操纵以及大规模皮质网络的理论框架的进步，以建立跨嗅觉皮层的气味识别和浓度编码的分布的原理。具体而言，我们旨在在嗅觉电路和气味编码之间建立以下联系： 1）存在沿嗅觉皮层的尾auda轴的特定抑制亚电路的分级分布。 2）抑制作用的延髓审计分布与HEBBIAN可塑性机制相互作用，从而塑造沿延髓轴轴的细胞的气味选择性。这些进步将为皮质结构如何表示和处理气味场景的不同方面提供急需的见解。