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。正是从神经动力学中出现了作为认知基础的计算。此外，最终不是孤立的基因或细胞造成了认知异常，比如在神经纤维瘤病或自闭症中观察到的那些异常，而是它们如何在网络水平上改变功能。在过去的几十年里，关于控制突触强度的学习规则，以及在体内和成像方法中对皮层处理中经验依赖变化的描述，已经取得了重大进展。但是，在衔接这些层次的分析方面进展不大；在突触和细胞特性解释神经网络涌现特性的能力上存在一个解释缺口。事实上，数以百万计的突触和成千上万的神经元的特性被调整来产生的机制，不仅是受控的（与癫痫相反的）活动流，而且是神经动力学结果的计算，目前还不清楚。当前提案的目标是在体外使用皮质网络作为“简化准备”来研究局部皮质网络中神经处理和动态的基本原理。皮层神经元以经验依赖的方式对刺激产生选择性反应。这种选择性在感觉处理和模式识别中起着基本作用，并且似乎独立于刺激是听觉、体感还是视觉的性质而发展。在传统的体外制备中，负责选择性出现的学习规则可能并不连贯，因为这些规则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输入一样。我们将长期将皮质网络暴露在体外简单的输入模式中，以解决一些基本问题，包括：1)体外神经元是否能够以“经验依赖”的方式发展刺激选择性反应，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态的理解，本研究将有助于阐明正常和病理皮层功能。此外，皮质功能体外模型的开发将证明对影响皮质功能的疾病（如神经纤维瘤病和阿尔茨海默病）的实验模型的开发有价值。

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.