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Characterizing Quorum Sensing-Induced Bioluminescence in Variable Volumes With Vibrio fischeri Using Computer Processing Methods

Abdel-Azim et al. | Jun 22, 2020

Characterizing Quorum Sensing-Induced Bioluminescence in Variable Volumes With <em>Vibrio fischeri</em> Using Computer Processing Methods

Understanding how bacteria respond to other bacteria could facilitate their ability to initiate and maintain their infectiousness. The phenomenon by which bacteria signal to each other via chemical signals is called quorum sensing, which could be targeted to deter bacterial infection in some cases if better understood. In this article, the authors study how a bacterium called V. fischeri uses quorum sensing to change bioluminescence, an easy readout that facilitates studying quorum sensing in this strain.

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Identifying Neural Networks that Implement a Simple Spatial Concept

Zirvi et al. | Sep 13, 2022

Identifying Neural Networks that Implement a Simple Spatial Concept

Modern artificial neural networks have been remarkably successful in various applications, from speech recognition to computer vision. However, it remains less clear whether they can implement abstract concepts, which are essential to generalization and understanding. To address this problem, the authors investigated the above vs. below task, a simple concept-based task that honeybees can solve, using a conventional neural network. They found that networks achieved 100% test accuracy when a visual target was presented below a black bar, however only 50% test accuracy when a visual target was presented below a reference shape.

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