Model Neuron Encoding the Realistic Learning Rule

描述（申请人提供）：活动依赖性突触修饰、长期增强（LTP）和抑制（LTD）在神经网络的信息处理和存储中是必不可少的。因此，了解突触修饰的机制对于理解学习和记忆功能至关重要。我们研究的长期目标是了解突触修饰如何参与海马神经网络的信息处理。然而，中枢神经网络的复杂性阻碍了仅通过生物学方法解决这些问题。因此，我们建议结合电生理学和计算建模方法，在大鼠海马切片中建立一个编码突触前和突触后相关活动诱导的突触修饰的模型神经元系统。这些研究将使我们能够预测突触修饰如何调节神经网络中多个时空不同的输入，这超出了当前的电生理技术。

DESCRIPTION (provided by applicant): Activity-dependent synaptic modifications, long-term potentiation (LTP) and depression (LTD) are essential in information processing and storage in neural networks. Thus, understanding mechanisms of synaptic modifications are crucial in understanding learning and memory functions. The long-term goal of our research is to understand how synaptic modifications are involved in information processing in hippocampal neural network. However, the complexity of central neural networks hinders addressing these issues through biological approaches alone. Therefore, we propose to combine electrophysiologic and computational modeling approaches to develop a model neuron system encoding synaptic modifications induced by correlated pre- and postsynaptic activity in rat hippocampal slices. These studies will enable us to predict how synaptic modifications modulate the multiple spatiotemporal-distinct inputs in neuronal networks that is beyond current electrophysiological techniques. Previously we have demonstrated by electrophysiological studies in hippocampal "CAI neurons that 1) correlated activity at 5 Hz can induce either LTP or LTD, depending on the precise timing of pre-and postsynaptic activation, 2) a narrow time window (15 ms) that exists for LTP of the activated site is flanked by two time windows for LTD, which can spread from stimulated (homosynaptic) to non-stimulated (heterosynaptic) sites, 3) the transition between LTP and LTD occurs within 25 ms, a characteristic time for 40 Hz oscillations. Furthermore, the postsynaptic Ca2+, derived from Ca2+ influx via N-methyl-D-aspartate receptors and a differential release of Ca2+ from internal stores via ryanodine and IP3 receptors, regulates both polarity and input specificity of activity-induced synaptic modificalion. Our findings suggest a link between activity-dependent synaptic modifications and oscillation patters of place cells in the hippocampus, which are believed to be responsible for spatial memory. Using an integrative approach that combines electrophysiology, Ca2+ and optical imaging, and computational modeling in hippocampal slices, we specifically aims to address following questions: 1) What is the spatiotemporal pattern of spread of synaptic plasticity from activated synapses to non-activated synapses? We will characterize the spread of LTP and LTD postsynaptically along the dendritic arbor or presynaptically via retrograde signaling. 2) How are inhibitory neurons involved in determination of polarity and extent of synaptic modifications in neural networks? 3) How are spike-timing based synaptic modifications at different pathways integrated in a postsynaptic neuron? Taking advantage of the spike-timing based induction protocol, we will investigate how signals originated from inputs with various spatiotemporal differences can be integrated in a single postsynaptic neuron. These studies will contribute significantly to our understanding the cellular and molecular basis of learning and memory.