Periodical cicadas exhibit an extraordinary capacity for self-organizing spatially synchronous breeding behavior. The regular emergence of periodical cicada broods across the United States is a phenomenon of longstanding public and scientific interest, as the cicadas of each brood emerge in huge numbers and briefly dominate their ecosystem. During the emergence, the 17-year periodical cicada species is found to form synchronized choruses, and we investigated their chorusing behavior from the standpoint of spatial synchrony.Cicada choruses were observed to form in trees, calling regularly every five seconds. In order to determine the limits of this self-organizing behavior, we set out to quantify the spatial synchronization between cicada call choruses in different trees, and how and why this varies in space and time.We performed 20 simultaneous recordings in Clinton State Park, Kansas, in June 2015 (Brood IV), with a team of citizen-science volunteers using consumer equipment (smartphones). We use a wavelet approach to show in detail how spatially synchronous, self-organized chorusing varies across the forest.We show how conditions that increase the strength of audio interactions between cicadas also increase the spatial synchrony of their chorusing. Higher forest canopy light levels increase cicada activity, corresponding to faster and higher-amplitude chorus cycling and to greater synchrony of cycles across space. We implemented a relaxation-oscillator-ensemble model of interacting cicadas, finding that a tendency to call more often, driven by light levels, results in all these effects.Results demonstrate how the capacity to self-organize in ecology depends sensitively on environmental conditions. Spatially correlated modulation of cycling rate by an external driver can also promote self-organization of phase synchrony.
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http://dx.doi.org/10.1002/ece3.6213 | DOI Listing |
J Neuroeng Rehabil
December 2024
Laboratory for Neuro- & Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium.
Background: The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved.
View Article and Find Full Text PDFFront Plant Sci
December 2024
Key Laboratory of Humid Subtropical Eco-geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, China.
The coordination between leaf and root traits is crucial for plants to synchronize their strategies for acquiring and utilizing above- and belowground resources. Nevertheless, the generality of a whole plant conservation gradient is still controversial. Such testing has been conducted mainly among communities at large spatial scales, and thus evidence is lacking within communities.
View Article and Find Full Text PDFHeliyon
December 2024
Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, 121205, Moscow, Russia.
Mental imagery is a crucial cognitive process, yet its underlying neural mechanisms remain less understood compared to perception. Furthermore, within the realm of mental imagery, the somatosensory domain is particularly underexplored compared to other sensory modalities. This study aims to investigate the influence of tactile imagery (TI) on cortical somatosensory processing.
View Article and Find Full Text PDFJ Neural Eng
December 2024
School of Life Sciences, Tiangong University, NO.399, Binshuixi Road, Xiqing District, Tianjin, P.R.China., Tianjin, Tianjin, 300387, CHINA.
Objective: Automatic detection and prediction of epilepsy are crucial for improving patient care and quality of life. However, existing methods typically focus on single-dimensional information and often confuse the periodic and aperiodic components in electrophysiological signals.
Approach: We propose a novel deep learning framework that integrates temporal, spatial, and frequency information of EEG signals, in which periodic and aperiodic components are separated in the frequency domain.
Brain Inform
December 2024
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Affective computing is a key research area in computer science, neuroscience, and psychology, aimed at enabling computers to recognize, understand, and respond to human emotional states. As the demand for affective computing technology grows, emotion recognition methods based on physiological signals have become research hotspots. Among these, electroencephalogram (EEG) signals, which reflect brain activity, are highly promising.
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