Timing and Learning in In Vitro Cortical Networks

描述(申请人提供)：大脑皮层计算依赖于通过大脑皮层网络的活动流所产生的动作电位的时空模式。它来自于 这些模式是作为认知基础的计算出现的。但最终，正常或异常认知的基础不是基因或孤立的细胞，而是这些分子和细胞过程如何管理神经元网络的行为。在过去的几十年里，在理解神经可塑性的关键突触和细胞机制方面，以及在使用行为、体内和成像方法描述皮层处理中的经验依赖性变化方面，已经取得了重大进展。然而，在连接这些水平的分析方面取得的进展较少；也就是说，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，数以百万计的突触和数以千计的神经元的属性被调节以通过神经动力学产生计算的机制尚不清楚。然而，众所周知，皮质功能的一个基本特征是，在整个出生后的发育过程中，神经回路是由经验塑造的。此外，依赖经验的可塑性异常会导致许多神经疾病，从学习障碍到智力低下。在使用体外制剂的传统研究中，导致经验依赖性可塑性出现的学习规则大概不是连贯的，因为这些规则通常在没有任何输入结构的情况下“发展”--就像视觉或听觉系统被剥夺了模式化输入一样。我们的目标是利用体外的大脑皮层网络作为一种“简化的准备”来研究大脑皮层回路依赖经验的塑形背后的基本原理。为了实现这一目标，我们最近描述了我们认为是第一个神经模拟的体外学习。具体地说，通过将孵化器中的切片长期暴露于模式化刺激(模仿感觉体验)，我们已经表明，神经动力学再现了经验刺激的时间特征。在这里，我们将使用新的电学和光遗传学方法进一步证明，体外大脑皮质回路可以“学习”时间模式，并阐明计时和皮质计算的潜在神经机制。我们认为，在体外提供学习和计时模拟的简化准备最终将被证明是研究真正从神经网络的循环动力学中出现的计算所必需的。此外，通过证明大脑皮层网络天生具有“学习”时间模式的能力，我们的实验将解决大脑如何告诉时间这个长期存在的问题。此外，在体外研究网络行为和“学习”的能力应该提供一种方法，利用认知障碍动物模型的组织来研究病理回路水平的计算。

DESCRIPTION (provided by applicant): Cortical computations rely on the spatiotemporal patterns of action potentials created by the flow of activity through cortical networks. It is from these patterns that the computations that underlie cognition emerge. But it is ultimately not genes or cells in isolation that underlie normal or abnormal cognition, but how these molecular and cellular processes govern the behavior of networks of neurons. Over the past decades significant progress has been made towards understanding critical synaptic and cellular mechanisms of neural plasticity, as well as in the description of experience-dependent changes in cortical processing using behavioral, in vivo, and imaging approaches. However, less progress has been made in bridging these levels of analyses; that is, 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 computations through neural dynamics are not understood. It is known, however, that a cardinal feature of cortical function is that throughout postnatal development neural circuits are sculpted by experience. Furthermore, abnormalities in experience-dependent plasticity contribute to a number of neurological disorders, ranging from learning disabilities to mental retardation. The learning rules responsible for the emergence of experience-dependent plasticity are presumably not coherently engaged in traditional studies using 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. Our goal is to use cortical networks in vitro as a 'reduced preparation' to study the fundamental principles underlying the experience-dependent sculpting of cortical circuits. Towards this goal we have recently described what we consider to be the first neural analog of learning in vitro. Specificall, by chronically exposing slices in the incubator to patterned stimuli (mimicking sensory experience) we have shown that the neural dynamics reproduces the temporal features of the experienced stimuli. Here we will use novel electrical and optogenetic methods to further demonstrate that cortical circuits in vitro can "learn" temporal patterns, and elucidate the underlying neural mechanisms of timing and cortical computations. We suggest that a reduced preparation that provides an analog of learning and timing in vitro will ultimately prove to be required to study computations that truly emerge from the recurrent dynamics of neural networks. Additionally, by demonstrating that cortical networks are inherently capable of 'learning' temporal patterns, our experiments will address the long-standing question of the how the brain tells time. Furthermore, the ability to study network behavior and 'learning' in vitro should provide a means to study pathological circuit level computations using tissue from animal models of cognitive disorders.