Dynamics of sensory integration of olfactory and mechanical stimuli within the response patterns of moth antennal lobe neurons

被引:9
|
作者
Tuckman, Harrison [1 ]
Kim, Jungmin [2 ]
Rangan, Aaditya [3 ]
Lei, Hong [4 ]
Patel, Mainak [1 ]
机构
[1] William & Mary Williamsburg, Dept Math, Williamsburg, VA 23187 USA
[2] Univ Colorado Denver, Dept Pharmacol, Aurora, CO 80045 USA
[3] NYU, Courant Inst Math Sci, New York, NY 10012 USA
[4] Arizona State Univ, Sch Life Sci, Tempe, AZ 85281 USA
基金
美国国家科学基金会;
关键词
Neuronal network models; Antennal lobe dynamics; Computational neuroscience; Olfactory modeling; SPHINX MOTH; PROJECTION NEURONS; ODOR MIXTURES; DEVELOPMENTAL-CHANGES; PHEROMONE COMPONENTS; RECEPTOR NEURONS; MANDUCA-SEXTA; UPWIND FLIGHT; INHIBITION; BRAIN;
D O I
10.1016/j.jtbi.2020.110510
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Odors emanating from a biologically relevant source are rapidly embedded within a windy, turbuluent medium that folds and spins filaments into fragmented strands of varying sizes. Environmental odor plumes therefore exhibit complex spatiotemporal dynamics, and rarely yield an easily discernible concentration gradient marking an unambiguous trail to an odor source. Thus, sensory integration of chemical input, encoding odor identity or concentration, and mechanosensory input, encoding wind speed, is a critical task that animals face in resolving the complex dynamics of odor plumes and tracking an odor source. In insects, who employ olfactory navigation as their primary means of foraging for food and finding mates, the antennal lobe (AL) is the first brain structure that processes sensory odor information. Although the importance of chemosensory and mechanosensory integration is widely recognized, the AL itself has traditionally been viewed purely from the perspective of odor encoding, with little attention given to its role as a bimodal integrator. In this work, we seek to explore the AL as a model for studying sensory integration - it boasts well-understood architecture, well-studied olfactory responses, and easily measurable cells. Using a moth model, we present experimental data that clearly demonstrates that AL neurons respond, in dynamically distinct ways, to both chemosensory and mechanosensory input; mechanosensory responses are transient and temporally precise, while olfactory responses are long-lasting but lack temporal precision. We further develop a computational model of the AL, show that our model captures odor response dynamics reported in the literature, and examine the dynamics of our model with the inclusion of mechanosensory input; we then use our model to pinpoint dynamical mechanisms underlying the bimodal AL responses revealed in our experimental work. Finally, we propose a novel hypothesis about the role of mechanosensory input in sculpting AL dynamics and the implications for biological odor tracking. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:18
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