A computational model for angular velocity integration in a locust heading circuit.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

PLoS Computational Biology Pub Date : 2024-12-20 eCollection Date: 2024-12-01 DOI:10.1371/journal.pcbi.1012155

Kathrin Pabst, Evripidis Gkanias, Barbara Webb, Uwe Homberg, Dominik Endres

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引用次数: 0

Abstract

Accurate navigation often requires the maintenance of a robust internal estimate of heading relative to external surroundings. We present a model for angular velocity integration in a desert locust heading circuit, applying concepts from early theoretical work on heading circuits in mammals to a novel biological context in insects. In contrast to similar models proposed for the fruit fly, this circuit model uses a single 360° heading direction representation and is updated by neuromodulatory angular velocity inputs. Our computational model was implemented using steady-state firing rate neurons with dynamical synapses. The circuit connectivity was constrained by biological data, and remaining degrees of freedom were optimised with a machine learning approach to yield physiologically plausible neuron activities. We demonstrate that the integration of heading and angular velocity in this circuit is robust to noise. The heading signal can be effectively used as input to an existing insect goal-directed steering circuit, adapted for outbound locomotion in a steady direction that resembles locust migration. Our study supports the possibility that similar computations for orientation may be implemented differently in the neural hardware of the fruit fly and the locust.

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蝗虫航向回路中角速度积分的计算模型。

准确的导航通常需要保持相对于外部环境的航向的可靠内部估计。我们提出了一个沙漠蝗航向回路的角速度积分模型，将哺乳动物航向回路的早期理论工作概念应用于昆虫的新生物学背景。与果蝇的类似模型相比，该电路模型使用单一的360°方向表示，并通过神经调节角速度输入进行更新。我们的计算模型是使用具有动态突触的稳态放电速率神经元来实现的。电路连接受到生物数据的限制，剩余的自由度通过机器学习方法进行优化，以产生生理上合理的神经元活动。结果表明，该电路中航向和角速度的积分对噪声具有较好的鲁棒性。航向信号可以有效地用作现有昆虫目标导向转向电路的输入，该电路适用于类似蝗虫迁徙的稳定方向的出站运动。我们的研究支持了一种可能性，即果蝇和蝗虫的神经硬件中类似的方向计算可能实现不同。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.