Experience-dependent plasticity and dynamics in vitro

描述(申请人提供)：皮质计算是神经动力学的结果：通过皮质网络的活动流所产生的时空模式。作为认知基础的计算正是从神经动力学中产生的。此外，最终导致认知异常的不是孤立的基因或细胞的影响，而是它们如何改变网络层面的功能。在过去的几十年里，在控制突触强度的学习规则方面，以及在使用活体和成像方法描述皮层处理的经验依赖性变化方面，已经取得了重大进展。然而，在连接这些分析水平方面取得的进展较少；在突触和细胞属性解释神经网络的新兴属性的能力方面存在解释差距。事实上，数百万个突触和数千个神经元的特性被调节以产生受控(而不是癫痫)活动流的机制，以及作为神经动力学结果的计算还不清楚。目前的建议的目标是使用体外皮层网络作为一种“简化的准备”来研究局部皮质网络中潜在的神经处理和动力学的基本原理。大脑皮层神经元以经验依赖的方式对刺激产生选择性反应。这种选择性在感觉加工和模式识别中起着基础性的作用，并且似乎独立于刺激本质上是听觉的、躯体感觉的还是视觉的。在传统的体外准备中，负责出现选择性的学习规则可能并不连贯，因为这些规则通常在没有任何输入结构的情况下“发展”，很像视觉或听觉系统被剥夺了图案化的输入。我们将长期地将体外皮层网络暴露于简单的输入模式，以解决一些基本问题，包括：1)体外神经元是否能够以一种‘经验依赖’的方式产生刺激选择性反应，以及2)阐明自发网络动力学的计算作用。通过提高我们对感觉经验如何塑造网络功能和动力学的理解，这项研究将有助于阐明正常和病理的皮质功能。此外，皮质功能的体外模型的发展将被证明对影响皮质功能的疾病的实验模型的发展具有价值，例如神经纤维瘤病和阿尔茨海默病。

DESCRIPTION (provided by applicant): Cortical computations are the result of neural dynamics: the spatial-temporal patterns created by the flow of activity through cortical networks. It is from neural dynamics that the computations that underlie cognition emerge. Additionally, it is ultimately not the effect genes or cells in isolation that underlie cognitive abnormalities, such as those observed in neurofibromatosis or autism, but how they alter function at the network level. Over the past decades significant progress has been made regarding the learning rules governing synaptic strength, as well as in the description of experience-dependent changes in cortical processing using in vivo and imaging approaches. However, less progress has been made in bridging these levels of analyses; there is an explanatory gap in the ability of synaptic and cellular properties to account for the emergent properties of neural networks. Indeed, the mechanisms by which the properties of millions of synapses and thousands of neurons are adjusted to produce, not only a controlled (as opposed to epileptic) flow of activity, but a computation as a result of neural dynamics are not understood. The goal in the current proposal is to use cortical networks in vitro as a 'reduced preparation' to study the fundamental principals underlying neural processing and dynamics within local cortical networks. Cortical neurons develop selective responses to stimuli in an experience-dependent manner. This selectivity plays a fundamental role in sensory processing and pattern recognition, and appears to develop independently of whether the stimuli are auditory, somatosensory or visual in nature. The learning rules responsible for the emergence of selectivity are presumably not coherently engaged in traditional in vitro preparations, since these normally 'develop' in the absence of any input structure, much like the visual or auditory system being deprived of patterned input. We will chronically expose cortical networks in vitro to simple input patterns to address a number of fundamental issues, including: 1) whether neurons in vitro can develop stimulus-selective responses in an 'experience-dependent' fashion, and 2) to elucidate the computational role of spontaneous network dynamics. By advancing our understanding of how sensory-experience shapes network function and dynamics, this research will contribute to the elucidation of both normal and pathological cortical function. Furthermore the development of an in vitro model of cortical function will prove valuable to the development of experimental models for diseases affecting cortical function, such as neurofibromatosis and Alzheimer's disease.