Biophysical mechanisms regulating synchrony transfer in somatosensory cortex

调节体感皮层同步传递的生物物理机制

• 批准号: 8338430
• 负责人: Steven A. Prescott
• 金额: $ 15.66万
• 依托单位: HOSPITAL FOR SICK CHLDRN (TORONTO)
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-30 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8338430
• 关键词: Acetylcholine; Address; Affect; Cells; Chronic; Code; Complex; Computer Simulation; Coupled; Data; Detection; Electrophysiology (science); Esthesia; Generations; Hypersensitivity; Lead; Link; Measures; Modeling; Molecular; Nervous system structure; Network-based; Neurobiology; Neurons; Norepinephrine; Output; Pain; Pattern; Perception; Play; Preparation; Process; Property; Protocols documentation; Rattus; Recording of previous events; Relative (related person); Role; Sensory; Sensory Process; Signal Transduction; Simulate; Slice; Solutions; Somatosensory Cortex; Specific qualifier value; Stimulus; Synapses; Synaptic plasticity; Techniques; Testing; Time; Training; Translations; Work; base; computerized data processing; detector; effective therapy; falls; information processing; innovation; insight; mathematical model; neuroregulation; painful neuropathy; postsynaptic; presynaptic; relating to nervous system; sensory system; time interval; virtual

DESCRIPTION (provided by applicant): Sensory input can evoke very different percepts depending on how information is processed by the nervous system. Fundamental aspects of that neural processing remain poorly understood. Evidence points to correlation of spiking across neurons as a possible neural coding mechanism, especially in sensory systems. For example, sensory information is most effectively transmitted to the cortex when spiking is synchronized across thalamocortical neurons, and available evidence suggests that synchronous activity continues to be propagated to downstream cortical layers. This transfer of synchrony between pre- and postsynaptic neurons (i.e. synchrony transfer) is crucial, lest the information carried by synchronous spiking be lost. An important yet unresolved issue is how well synchrony is transferred between layers of cortex and, in general, how synchrony transfer is regulated. One thing is clear: sets of neurons transfer synchronous input to their postsynaptic targets only if they themselves respond to synchronous inputs with synchronous spiking. What, then, are the biophysical mechanisms that control spike synchrony across a set of neurons receiving synchronous input? Deciphering the cellular and synaptic bases for synchrony transfer has proven extremely challenging because synchrony is a multi-neuron, network-level phenomenon that is difficult to measure or control using standard experimental techniques. Consequently, the task has fallen to computer modeling. But although modeling has provided valuable insights, the need for experimentation persists. Our solution to this challenge is to embed real neurons in virtual networks by integrating electrophysiology with mathematical modeling. This will enable us to experimentally investigate the biophysical mechanisms regulating synchrony transfer in a slice preparation of rat somatosensory cortex. In brief, we will simulate synaptic connectivity patterns by combining dynamic clamp and mathematical modeling such that individually recorded neurons operate (and will be analyzed) as if they are part of a network propagating synchronous activity. Synchrony transfer will be quantified by comparing output synchrony, calculated by cross-correlation of recorded output spike trains, with input synchrony, specified when constructing our simulated synaptic input. We will use this innovative approach to test our central hypothesis that biophysical mechanisms at the level of single neurons, microcircuits, and synaptic plasticity can enable good synchrony transfer between cortical layers. We have identified spike generation, feedforward inhibition, and spike time dependent plasticity as candidate mechanisms based on theoretical insights derived from our previous work. Relating network-level phenomena like synchrony with their underlying biophysical mechanisms is essential for understanding the neurobiological basis of sensory processing. By combining mathematical modeling with electrophysiology to study real neurons embedded in virtual networks, our proposed study will establish direct links between network-level synchrony and the cellular and synaptic mechanisms regulating synchrony transfer.

描述(申请人提供)：根据神经系统如何处理信息，感官输入可以唤起非常不同的感知。这种神经过程的基本方面仍然知之甚少。有证据表明，神经元之间的尖峰相关是一种可能的神经编码机制，特别是在感觉系统中。例如，当跨丘脑皮质神经元的棘波同步时，感觉信息最有效地传递到皮质，现有证据表明，同步活动继续传播到下游的皮质层。这种突触前和突触后神经元之间的同步性转移(即同步转移)是至关重要的，以免同步棘波携带的信息丢失。一个重要但尚未解决的问题是，同步性在大脑皮层各层之间的传递有多好，以及通常情况下，同步传递是如何调节的。有一件事是明确的：只有当神经元本身对同步输入做出同步尖峰反应时，它们才会将同步输入转移到它们的突触后目标。那么，控制接受同步输入的一组神经元之间的棘波同步的生物物理机制是什么？破译同步传递的细胞和突触基础被证明是极具挑战性的，因为同步是一种多神经元、网络水平的现象，很难使用标准的实验技术来测量或控制。因此，这项任务就落到了计算机建模上。但是，尽管建模提供了有价值的见解，但对实验的需求依然存在。我们对这一挑战的解决方案是将电生理学与数学建模相结合，将真实神经元嵌入虚拟网络中。这将使我们能够在实验上研究大鼠体感皮层切片制备中调节同步传递的生物物理机制。简而言之，我们将通过结合动态钳制和数学建模来模拟突触连接模式，以便单独记录的神经元运行(并将被分析)，就像它们是传播同步活动的网络的一部分。同步传递将通过比较输出同步性和输入同步性来量化，输出同步性是通过记录的输出棘波序列的互相关计算出来的，输入同步性是在构建模拟突触输入时指定的。我们将使用这种创新的方法来验证我们的中心假设，即单个神经元、微电路和突触可塑性水平的生物物理机制可以实现皮质层之间的良好同步转移。基于我们以前工作中的理论见解，我们已经将尖峰产生、前馈抑制和尖峰时间依赖可塑性确定为候选机制。将网络水平的同步现象与其潜在的生物物理机制联系起来，对于理解感觉处理的神经生物学基础至关重要。通过将数学建模和电生理学相结合来研究虚拟网络中嵌入的真实神经元，我们提出的研究将在网络水平的同步与调节同步传递的细胞和突触机制之间建立直接联系。