Timing and Learning in In Vitro Cortical Networks

描述（由申请人提供）：皮层计算依赖于通过皮层网络的活动流所产生的动作电位的时空模式。是从 这些模式是认知背后的计算模式。但最终不是基因或细胞孤立地构成正常或异常认知的基础，而是这些分子和细胞过程如何支配神经元网络的行为。在过去的几十年中，已经取得了重大进展，对理解关键的突触和神经可塑性的细胞机制，以及在使用行为，在体内，和成像方法在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;也就是说，在突触和细胞特性解释神经网络涌现特性的能力方面存在着解释性差距。事实上，数百万个突触和数千个神经元的特性被调整以通过神经动力学产生计算的机制还不清楚。然而，我们知道，大脑皮层功能的一个基本特征是，在整个出生后的发育过程中，神经回路都是由经验塑造的。此外，经验依赖性可塑性的异常会导致许多神经系统疾病，从学习障碍到智力迟钝。 经验依赖性可塑性的出现的学习规则，大概没有连贯地参与传统的研究，使用体外制剂，因为这些通常是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的输入。我们的目标是使用体外皮层网络作为“简化的准备”，以研究皮层回路的经验依赖性雕刻的基本原则。 为了实现这一目标，我们最近描述了我们认为是第一个体外学习的神经模拟物。具体地说，通过长期暴露在孵化器中的切片模式化的刺激（模仿感官体验），我们已经表明，神经动力学再现了所经历的刺激的时间特征。在这里，我们将使用新的电和光遗传学方法，以进一步证明，在体外皮层电路可以“学习”时间模式，并阐明了潜在的神经机制的时间和皮层计算。 我们认为，减少准备，提供了一个模拟的学习和定时在体外将最终被证明是需要研究计算，真正出现的神经网络的循环动力学。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，我们的实验将解决长期存在的问题，即大脑如何告诉时间。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，使用来自认知障碍动物模型的组织来研究病理电路水平的计算。 公共卫生相关性：人类的行为和认知依赖于由数万个神经元组成的局部皮层网络中发生的计算。许多神经系统疾病，包括某些形式的精神发育迟滞，精神分裂症和自闭症，似乎是这些回路中异常发育和处理的结果。如果不首先阐明大脑皮层回路如何执行计算的一些基本原理，我们就不太可能治愈某些神经系统疾病。目前的建议旨在实现这一目标。

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. PUBLIC HEALTH RELEVANCE: Human behavior and cognition rely on the computations that take place within local cortical networks composed of tens of thousands of neurons. Many neurological disorders, including some forms of mental retardation, schizophrenia, and autism, appear to be the consequence of abnormal development and processing within these circuits. It is unlikely we will be able to cure some neurological disorders without first elucidating some of the fundamental underpinnings of how cortical circuits perform computations. The current proposal is aimed at achieving this goal.