Lee Joonyeol, Joshua Mati, Medina Javier F, Lisberger Stephen G
Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Edmond and Lily Safra Center for Brain Sciences, the Hebrew University, Jerusalem 91904, Israel.
Neuron. 2016 Apr 6;90(1):165-76. doi: 10.1016/j.neuron.2016.02.012. Epub 2016 Mar 10.
Analysis of the neural code for sensory-motor latency in smooth pursuit eye movements reveals general principles of neural variation and the specific origin of motor latency. The trial-by-trial variation in neural latency in MT comprises a shared component expressed as neuron-neuron latency correlations and an independent component that is local to each neuron. The independent component arises heavily from fluctuations in the underlying probability of spiking, with an unexpectedly small contribution from the stochastic nature of spiking itself. The shared component causes the latency of single-neuron responses in MT to be weakly predictive of the behavioral latency of pursuit. Neural latency deeper in the motor system is more strongly predictive of behavioral latency. A model reproduces both the variance of behavioral latency and the neuron-behavior latency correlations in MT if it includes realistic neural latency variation, neuron-neuron latency correlations in MT, and noisy gain control downstream of MT.
对平稳跟踪眼球运动中感觉运动潜伏期的神经编码分析揭示了神经变化的一般原则以及运动潜伏期的具体来源。MT中神经潜伏期的逐次试验变化包括一个以神经元与神经元潜伏期相关性表示的共享成分和每个神经元特有的独立成分。独立成分主要源于潜在放电概率的波动,而放电本身的随机性贡献出乎意料地小。共享成分导致MT中单个神经元反应的潜伏期对跟踪行为潜伏期的预测性较弱。运动系统中更深层次的神经潜伏期对行为潜伏期的预测性更强。如果一个模型包括现实的神经潜伏期变化、MT中的神经元与神经元潜伏期相关性以及MT下游的噪声增益控制,那么它就能再现行为潜伏期的方差以及MT中的神经元与行为潜伏期相关性。