Time courses of neural responses underlie real-time sensory processing and perception. How these temporal dynamics change may be fundamental to how sensory systems adapt to different perceptual demands. By simultaneously recording from hundreds of neurons in mouse primary visual cortex, we examined neural population responses to visual stimuli at sub-second timescales, during different behavioural states. We discovered that during active behavioural states characterised by locomotion, single-neurons shift from transient to sustained response modes, facilitating rapid emergence of visual stimulus tuning. Differences in single-neuron response dynamics were associated with changes in temporal dynamics of neural correlations, including faster stabilisation of stimulus-evoked changes in the structure of correlations during locomotion. Using Factor Analysis, we examined temporal dynamics of latent population responses and discovered that trajectories of population activity make more direct transitions between baseline and stimulus-encoding neural states during locomotion. This could be partly explained by dampening of oscillatory dynamics present during stationary behavioural states. Functionally, changes in temporal response dynamics collectively enabled faster, more stable and more efficient encoding of new visual information during locomotion. These findings reveal a principle of how sensory systems adapt to perceptual demands, where flexible neural population dynamics govern the speed and stability of sensory encoding.
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http://dx.doi.org/10.1038/s41467-024-50563-y | DOI Listing |
J Neural Transm (Vienna)
January 2025
Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.
The majority of patients with cannabis use disorder (CUD) regularly take medication. Cannabinoids influence metabolism of some commonly prescribed drugs. However, little is known about the characteristics and frequency of potential cannabis-drug (CDIs) and drug-drug interactions (DDIs) in patients with CUD.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Science and Engineering, Hamad Bin Khalifa University, Ar-Rayyan, Qatar.
The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis of COVID-19 cases. As imaging technologies have advanced, 3D CNNs have emerged as a powerful tool for segmenting and classifying COVID-19 in medical images. These networks have demonstrated both high accuracy and rapid detection capabilities, making them crucial for effective COVID-19 diagnostics.
View Article and Find Full Text PDFSci Rep
January 2025
National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis patients with chronic kidney disease. Hyperkalemia, common in dialysis patients, can lead to life-threatening arrhythmias or sudden death if untreated. Therefore, real-time monitoring of hyperkalemia in this population is crucial.
View Article and Find Full Text PDFTrends Neurosci
January 2025
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA. Electronic address:
The evolution of vertebrates from protochordate ancestors marked the beginning of the gradual transition to predatory lifestyles. Enabled by the acquisition of multipotent neural crest and cranial placode cell populations, vertebrates developed an elaborate peripheral nervous system, equipped with paired sense organs, which aided in adaptive behaviors and ultimately, successful colonization of diverse environmental niches. Underpinning the enduring success of vertebrates is the highly adaptable nature of the peripheral nervous system, which is enabled by the exceptional malleability of the neural crest and placode developmental programs.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
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