A Model for Homeostatic Plasticity in Striatum

项目摘要 突触权重的变化编码新的学习和学习行为的执行。等 变化发生在不同的时间尺度上，所有这些变化都发生在动态系统中， 自我平衡地补偿以稳定网络活动并将活动维持在有用的动态范围内 范围强有力的数据支持这些稳定机制在进化上是保守的， 在神经系统中表现出来。在动物模型中，它们在感官方面的研究最好。 系统和神经肌肉接头。尽管这些机制被广泛接受， 大多数网络的稳态稳定作用没有被记录和/或很少被发现。 明白一个正常运作的突触可以在短时间内加强或减弱 （秒到分钟），包括Hebbian形式的突触可塑性，如LTP或LTD。 纹状体投射神经元（SPN）上形成皮质突触，异常Hebbian突触可塑性 是焦虑和成瘾样状态的标志，以及与之相关的神经疾病模型。 认知障碍或痴呆。总的来说，数据表明赫布可塑性是脆弱的 各种病理状况的反应。然而，数据也表明，在这种情况下， 皮质纹状体网络不再保持在一个有用的工作范围内， 用来稳定这个大部分闭环系统的系统也可能特别脆弱。重要的是， 皮质纹状体回路是否或如何适应活动的持续增加或减少， 明确，在理解健康和健康的整体皮质纹状体网络功能方面存在重大失误， 疾病状态。这种多PI R21的具体目标是定义稳态的性质。 在SPNs中进行调整。我们将评估对广泛的、有针对性的和细胞自主的反应， 增加或减少体外和完整系统内的神经活动。这些数据可以让我们 建立一个可测试的模型，用于未来评估自我平衡机制， 变得适应不良或被疾病相关的病理学破坏。

PROJECT SUMMARY Changes in synaptic weights encode new learning and the execution of learned behaviors. Such changes occur across different timescales, all within dynamic systems that recalibrate and compensate homeostatically to stabilize network activity and maintain activity within a useful dynamic range. Strong data support that these stabilizing mechanisms are conserved evolutionarily and represented across neural systems. In animal models, they have been best studied in sensory systems and at the neuromuscular junction. Despite broad acceptance of these mechanisms, the homeostatically stabilizing actions of most networks have not been documented and/or are poorly understood. A normal functioning synapse can be strengthened or weakened over fast timescales (seconds to minutes) and includes Hebbian forms of synapse plasticity such as LTP or LTD. In cortical synapses forming on striatal projection neurons (SPNs), abnormal Hebbian synaptic plasticity is a hallmark of anxiety- and addiction-like states and neurological disease models associated with cognitive impairment or dementia. Collectively, the data indicate that Hebbian plasticity is vulnerable to a variety of pathological conditions. However, the data also suggest that under such conditions, corticostriatal networks are no longer kept within a useful working range—that the adaptive actions designed to stabilize this largely closed-loop system may also be particularly vulnerable. Importantly, whether or how corticostriatal circuits adapt to sustained increases or decreases in activity is not clear, a significant lapse in understanding overall corticostriatal network function in health and disease states. The specific goals of this multi-PI R21 are to define the nature of homeostatic adaptation within SPNs. We will assess responses to widespread, targeted, and cell autonomous increases or decreases in neural activity in vitro and within an intact system. These data will permit us to build a testable model that can be used in the future for assessing how homeostatic mechanisms become maladaptive or subverted by disease-related pathologies.