Machine learning models identify gene predictors of waggle dance behaviour in honeybees

Abstract

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An artificial neural network explains how bats might use vision for navigation

“…Future studies could also use network architectures that are more reminiscent of the mammalian brain, they could be generalized to other sensory modalities and organisms, and they could be elaborated to study complex forms of navigation such as map-based navigation. Moreover, artificial neural networks can also be used to study additional behaviors, as a few studies already did 12 , 24 – 27 . We thus anticipate a rapid increasing use of the power of machine learning to study behavior and we point to an increasing need to develop ways to carefully interpret their results.…”

“…The network is composed of several layers of convolution filters which extract feature from the visual scene, similar to the mammalian visual cortex. On the other hand, the network only performs feedforward computation, thus making it less powerful in comparison to the recurrent mammalian brain 24 – 27 . We refer to the units in the network as neurons and analyze their encoding of space.…”

“…We refer to the units in the network as neurons and analyze their encoding of space. A handful of previous studies used machine learning algorithms to study animal navigation, e.g., in ants, honeybees, and rats 12 , 24 – 27 , mostly focusing on short-range navigation in the order of dozens of meters 24 (which sometimes might be considered long-range navigation for the animal conducting the task). Herein this study, we use artificial neural networks to study visual navigation over a larger scale of dozens of kilometers using the actual visual information available to a fruit bat in the wild.…”

Assessing Anatomical Changes in Male Reproductive Organs in Response to Larval Crowding Using Micro-computed Tomography Imaging

“…Recent technological advances have enabled large-scale studies of molecular and behavioral responses to ecological conditions. For instance, the advent of the RNAseq technique has provided insights into how organisms respond to key ecological conditions, including temperature (Smith et al 2013;Kumar et al 2020), pesticides (Christen et al 2018;Colgan et al 2019), Edited by Roberto Romani social status (Veiner et al 2022), and diet (Xu et al 2018). Likewise, the advent of computer visions and automated tracking algorithms has enabled studies that identify key behavioral responses in controlled and natural conditions of individuals and groups (Jover et al 2009;Weinstein 2018;Shreesha et al 2020;Lürig et al 2021), allowing for a deeper understanding of behavioral responses across ecological environments.…”

Assessing anatomical changes in male reproductive organs in response to larval crowding using micro-computed tomography imaging

“…Recent technological advances have enabled large-scale studies of molecular and behavioural responses to ecological conditions. For instance, the advent of the RNAseq technique has provided insights into how organisms respond to key ecological conditions, including temperature (Smith et al 2013; Kumar et al 2020), pesticides (Christen et al 2018; Colgan et al 2019), social status (Veiner et al 2021), and diet (Xu et al 2018). Likewise, the advent of computer visions and automated tracking alrogithms has enabled studies that identify key behavioural responses in controlled and natural conditions of individuals and groups (Ferre et al 2009; Weinstein 2018; Shreesha et al 2020 anatomical, physiological, and developmental ;Lürig et al 2021), allowing for a deeper understanding of behavioural responses across ecological environments.…”