Department of Biology, Lund University, Lund, Sweden.
Computational Science Research Center, San Diego State University, San Diego, CA, United States.
Front Neural Circuits. 2021 Aug 16;15:684872. doi: 10.3389/fncir.2021.684872. eCollection 2021.
Dragonflies are highly skilled and successful aerial predators that are even capable of selectively attending to one target within a swarm. Detection and tracking of prey is likely to be driven by small target motion detector (STMD) neurons identified from several insect groups. Prior work has shown that dragonfly STMD responses are facilitated by targets moving on a continuous path, enhancing the response gain at the present and predicted future location of targets. In this study, we combined detailed morphological data with computational modeling to test whether a combination of dendritic morphology and nonlinear properties of NMDA receptors could explain these observations. We developed a hybrid computational model of neurons within the dragonfly optic lobe, which integrates numerical and morphological components. The model was able to generate potent facilitation for targets moving on continuous trajectories, including a localized spotlight of maximal sensitivity close to the last seen target location, as also measured during recordings. The model did not, however, include a mechanism capable of producing a traveling or spreading wave of facilitation. Our data support a strong role for the high dendritic density seen in the dragonfly neuron in enhancing non-linear facilitation. An alternative model based on the morphology of an unrelated type of motion processing neuron from a dipteran fly required more than three times higher synaptic gain in order to elicit similar levels of facilitation, despite having only 20% fewer synapses. Our data support a potential role for NMDA receptors in target tracking and also demonstrate the feasibility of combining biologically plausible dendritic computations with more abstract computational models for basic processing as used in earlier studies.
蜻蜓是高度熟练和成功的空中捕食者，甚至能够有选择地关注群体中的一个目标。从几个昆虫群中识别出的小目标运动探测器 (STMD) 神经元可能驱动了对猎物的检测和跟踪。先前的工作表明，蜻蜓 STMD 反应通过在连续路径上移动的目标得到促进，从而增强了目标当前和预测未来位置的响应增益。在这项研究中，我们结合了详细的形态数据和计算建模，以测试树突形态和 NMDA 受体的非线性特性的组合是否可以解释这些观察结果。我们开发了一种蜻蜓光叶神经元的混合计算模型，该模型集成了数值和形态组件。该模型能够为在连续轨迹上移动的目标产生强大的促进作用，包括靠近最后一次看到目标位置的局部最大灵敏度聚光灯，这也在记录过程中进行了测量。然而，该模型没有包括产生促进作用的移动或传播波的机制。我们的数据支持在蜻蜓神经元中看到的高密度树突在增强非线性促进方面的重要作用。基于来自双翅目蝇的一种不同类型的运动处理神经元的形态的替代模型，尽管只有 20%的突触较少，但需要高出三倍以上的突触增益才能引起类似水平的促进作用。我们的数据支持 NMDA 受体在目标跟踪中的潜在作用，并证明了将具有生物学意义的树突计算与早期研究中使用的更抽象的基本处理计算模型相结合的可行性。
