Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University New York, NY, USA.
Front Neural Circuits. 2010 Dec 30;4:123. doi: 10.3389/fncir.2010.00123. eCollection 2010.
In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly tunable wing steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuromechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob.
在动物世界中最令人惊叹的运动控制壮举之一，某些双翅目昆虫，如家蝇，可以在几十毫秒内准确地执行校正飞行动作。这些反射运动是通过平衡棒实现的，平衡棒是一种陀螺力传感器，与快速可调的翅膀转向肌肉协同作用。具体来说，位于平衡棒基部的机械感觉钟形感觉器将旋转引起的陀螺力转换为有关苍蝇身体角速度的信息。但是，苍蝇的神经结构究竟如何从左右平衡棒上的横向应变力产生角速度？为了探索潜在的算法，我们建立了旋转检测电路的神经力学模型。我们提出了一种神经生物学上合理的方法，通过这种方法，苍蝇可以准确地分离和测量施加的角速度的三维分量。我们的模型假设单个反转突触，并且在形式上类似于视网膜的某些方向选择性模型。使用多维误差分析，我们证明了我们的模型在各种输入条件下的鲁棒性。我们的分析揭示了给定苍蝇的物理结构和控制平衡棒末端旋钮处旋转引起的力的数学的情况下，苍蝇可以获得的最大信息量。
