Experience-dependent plasticity and dynamics in vitro

描述（由申请人提供）：皮层计算是神经动力学的结果：通过皮层网络的活动流创建的时空模式。认知背后的计算正是从神经动力学中产生的。此外，最终不是孤立的影响基因或细胞导致认知异常，例如在神经纤维瘤病或自闭症中观察到的那些，而是它们如何在网络水平上改变功能。在过去的几十年中，已经取得了显着的进展，关于学习规则的突触强度，以及在使用体内和成像方法的皮层处理的经验依赖性变化的描述。然而，在桥接这些层次的分析方面取得的进展较少;在突触和细胞特性解释神经网络的涌现特性的能力方面存在着一个解释空白。事实上，数百万个突触和数千个神经元的特性被调整，不仅产生受控的（与癫痫相反）活动流，而且产生作为神经动力学结果的计算，其机制还不清楚。在目前的建议的目标是使用皮层网络在体外作为一个“简化的准备”，研究的基本原则，神经处理和动力学内的局部皮层网络。皮质神经元以经验依赖的方式对刺激产生选择性反应。这种选择性在感觉处理和模式识别中起着重要作用，并且似乎独立于刺激是听觉、躯体感觉还是视觉的性质而发展。负责出现选择性的学习规则大概没有连贯地参与传统的体外准备，因为这些通常是在没有任何输入结构的情况下“发展”的，就像视觉或听觉系统被剥夺了模式化输入一样。我们将长期暴露于简单的输入模式的体外皮层网络，以解决一些基本问题，包括：1）体外神经元是否可以在“经验依赖”的方式开发刺激选择性反应，以及2）阐明自发网络动力学的计算作用。通过推进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性皮层功能。此外，皮质功能的体外模型的开发将证明对影响皮质功能的疾病（例如神经纤维瘤病和阿尔茨海默病）的实验模型的开发是有价值的。

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.