Plasticity of Neural Integrator Dynamics

项目摘要：眼神经整合器动力学的可塑性眼神经整合器将用于快速眼球运动的简短输入信号转换成维持眼肌张力以将眼球保持在固定角位置的持续神经活动模式。产生这种持续活动的主要候选机制是通过循环反馈回路重新激发神经元，这种机制需要精确调节正反馈的量以进行适当的操作。 正常情况下，视觉信号通过改变神经整合器中持续活动的性质来产生适当的调谐量。在实验上，可塑性将通过训练范例人工控制，该训练范例包括以由眼睛位置在真实的时间中动态控制的角速度旋转的视觉图案。 将建立最佳的训练方案，并定量测量神经积分器中神经元特性的变化。这些数据将用于将学习规则纳入神经积分器的理论递归神经网络模型。 瞬时输入到持续神经输出的转换是在神经系统中广泛观察到的短期记忆的一种形式。因此，更好地了解的作用调谐机制有助于神经整合功能应该有更广泛的意义，在神经生物学。

Project Summary: Plasticity of Neural Integrator Dynamics The oculomotor neural integrator converts brief input signals for rapid eye movements into sustained patterns of neural activity that maintain tension in eye muscles to hold the eye at a fixed angular position. The leading candidate mechanism for the production of this sustained activity is re-excitation of neurons through recurrent feedback loops, a mechanism that requires precise tuning of the amount of positive feedback for proper operation. Normally, visual signals produce the appropriate amount of tuning by changing the properties of the sustained activity in the neural integrator. Experimentally, the plasticity will be artificially controlled by a training paradigm consisting of a visual pattern rotating with an angular velocity dynamically controlled in real time by eye position. Optimal training protocols will be established and changes in the properties of neurons in the neural integrator will be quantitatively measured. These data will be used to incorporate learning rules into theoretical recurrent neural network models of the neural integrator. The conversion of a transient input into a sustained neural output is a form of short term memory that is widely observed in the nervous system. Thus better understanding of the role tuning mechanisms contribute to neural integrator function should have broader significance in neurobiology.