Computational model of cellular plasticity

本研究项目的主要目标是建立Hermissenda光感受器中光诱发发生器电位和尖峰产生的数学模型。Hermissenda的Pavivian条件反射导致支持条件刺激（CS）的通路的识别细胞中的几个K+电导的修改。该制剂 特别适合于生物物理分析，因为已鉴定的光感受器的电压和光依赖性电导已经用电压钳技术在一定程度上详细表征。此外，常规的细胞神经生理学技术已经鉴定了在所鉴定的光感受器中的调节的几种神经相关物， CS路径。这些最近的结果提供了一个机会，直接相关的生物物理学的修改，在确定的细胞神经相关的条件反射，表达的兴奋性和突触强度的变化。光感受器的计算机模拟的应用将被用来帮助阐明各种生物物理变化的作用， 在条件动物的识别细胞中兴奋性和突触强度的改变。该方案包括以下四个具体目标：1）利用电压钳研究中获得的光感受器的电压和光依赖电导， 光诱发发生器电位的定量模型; 2）使用定量模型来检查由条件反射产生的各种膜电导的改变与由电导变化预测的发生器电位和尖峰频率的改变之间的对应性; 3）使用定量模型来识别 可能有助于发生器电位和由条件反射产生的尖峰频率的修改的电导变化;以及4）开发CS通路活性的网络模型，其并入所识别的细胞中的兴奋性的已知变化并改变所识别的突触连接处的突触强度。这些职责将为我们提供重要的见解 对学习和记忆的生理学的理解。

The primary goal of this research project is to develop a mathematical model of the light-elicited generator potential and spike generation in the photoreceptors of Hermissenda. Paviovian conditioning of Hermissenda results in the modification of several K+ conductances in identified cells of the pathway supporting the conditioned stimulus (CS). The preparation is especially well suited for a biophysical analysis since the voltage- and light-dependent conductances of identified photoreceptors have been characterized in some detail with voltage-clamp techniques. In addition, conventional cellular neurophysiological techniques have identified several neural correlates of conditioning in the identified photoreceptors of the CS pathway. These recent results provide an opportunity to directly relate biophysical modifications in identified cells to neural correlates of conditioning that are expressed by changes in excitability and synaptic strength. The application of computer simulations of the photoreceptors would be used to help elucidate the role of diverse biophysical changes to the modification of excitability and synaptic strength in identified cells of conditioned animals. The proposal consists of the following four specific aims: 1) Use the voltage and light-dependent conductances of the photoreceptors derived from voltage-clamp studies to develop a quantitative model of the light-elicited generator potentials; 2) Use the quantitative model to examine the correspondence between modifications in various membrane conductances produced by conditioning and the modification of generator potentials and spike frequency predicted by the conductance changes; 3) Use the quantitative model to identify conductance changes that may contribute to the modification of generator potentials and spike frequency produced by conditioning; and 4) Develop a network model of CS pathway activity that incorporates known changes in excitability in identified cells and changes synaptic strength at identified synaptic connections. These duties will provide important insights into our understanding of the physiology of learning and memory.