Plasticity of Neural Integrator Dynamics

动眼神经积分器将快速眼球运动的短暂输入信号转化为持续的神经活动模式，维持眼肌张力，使眼睛保持在固定的角度位置。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，这一机制需要精确调整正反馈的数量才能正常运行。通常，视觉信号通过改变神经积分器中持续活动的特性来产生适当数量的调谐。在实验中，可塑性将通过一种由视觉模式组成的训练范式来人为地控制，该模式以由眼睛位置实时动态控制的角速度旋转。将建立最优的训练方案，并定量测量神经积分器中神经元属性的变化。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。将短暂的输入转化为持续的神经输出是在神经系统中广泛观察到的一种短期记忆形式。因此，更好地理解调节机制对神经积分器功能的作用在神经生物学中具有更广泛的意义。

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.