School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
PLoS Comput Biol. 2023 Jun 12;19(6):e1011170. doi: 10.1371/journal.pcbi.1011170. eCollection 2023 Jun.
Sensory inputs in nervous systems are often encoded at the millisecond scale in a precise spike timing code. There is now growing evidence in behaviors ranging from slow breathing to rapid flight for the prevalence of precise timing encoding in motor systems. Despite this, we largely do not know at what scale timing matters in these circuits due to the difficulty of recording a complete set of spike-resolved motor signals and assessing spike timing precision for encoding continuous motor signals. We also do not know if the precision scale varies depending on the functional role of different motor units. We introduce a method to estimate spike timing precision in motor circuits using continuous MI estimation at increasing levels of added uniform noise. This method can assess spike timing precision at fine scales for encoding rich motor output variation. We demonstrate the advantages of this approach compared to a previously established discrete information theoretic method of assessing spike timing precision. We use this method to analyze the precision in a nearly complete, spike resolved recording of the 10 primary wing muscles control flight in an agile hawk moth, Manduca sexta. Tethered moths visually tracked a robotic flower producing a range of turning (yaw) torques. We know that all 10 muscles in this motor program encode the majority of information about yaw torque in spike timings, but we do not know whether individual muscles encode motor information at different levels of precision. We demonstrate that the scale of temporal precision in all motor units in this insect flight circuit is at the sub-millisecond or millisecond-scale, with variation in precision scale present between muscle types. This method can be applied broadly to estimate spike timing precision in sensory and motor circuits in both invertebrates and vertebrates.
神经系统中的感觉输入通常以精确的尖峰定时码在毫秒级进行编码。现在有越来越多的证据表明，从缓慢呼吸到快速飞行等行为中，运动系统中存在精确的定时编码。尽管如此，由于记录完整的一组尖峰分辨运动信号以及评估编码连续运动信号的尖峰定时精度的困难，我们在很大程度上并不知道在这些电路中定时在什么尺度上起作用。我们也不知道精度尺度是否取决于不同运动单位的功能作用。我们引入了一种使用连续 MI 估计在增加的均匀噪声水平下估计运动电路中尖峰定时精度的方法。这种方法可以评估用于编码丰富的运动输出变化的精细尺度的尖峰定时精度。我们展示了与以前建立的评估尖峰定时精度的离散信息论方法相比，这种方法的优势。我们使用这种方法来分析近完整的、尖峰分辨的记录，这些记录记录了敏捷的 Hawk 飞蛾 Manduca sexta 中控制飞行的 10 个主要翅膀肌肉的运动。系绳飞蛾通过视觉跟踪产生一系列转向（偏航）扭矩的机器人花朵。我们知道，这个运动程序中的所有 10 块肌肉都通过尖峰时间编码了大部分关于偏航扭矩的信息，但我们不知道单个肌肉是否以不同的精度水平编码运动信息。我们证明，这种昆虫飞行电路中所有运动单位的时间精度尺度都在亚毫秒或毫秒级，不同肌肉类型之间存在精度尺度的变化。这种方法可以广泛应用于估计感觉和运动电路中的尖峰定时精度，包括无脊椎动物和脊椎动物。
