A complete neuroscience requires multilevel theories that address phenomena ranging from higher-level cognitive behaviors to activities within a cell. We propose an extension to the level of mechanism approach where a computational model of cognition sits in between behavior and brain: It explains the higher-level behavior and can be decomposed into lower-level component mechanisms to provide a richer understanding of the system than any level alone. Toward this end, we decomposed a cognitive model into neuron-like units using a neural flocking approach that parallels recurrent hippocampal activity. Neural flocking coordinates units that collectively form higher-level mental constructs. The decomposed model suggested how brain-scale neural populations coordinate to form assemblies encoding concept and spatial representations and why so many neurons are needed for robust performance at the cognitive level. This multilevel explanation provides a way to understand how cognition and symbol-like representations are supported by coordinated neural populations (assemblies) formed through learning.
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http://dx.doi.org/10.1126/sciadv.ade6903 | DOI Listing |
Poult Sci
November 2024
Koret School of Veterinary Medicine, The Robert H. Smith Faculty of Agricultural, Food, and Environmental Sciences, The Hebrew University of Jerusalem, Rehovot, 7610001, Israel. Electronic address:
Hatchability rates in broilers pose a significant challenge in the poultry industry. Despite advancements in breeding and incubation technology, hatch rates remain suboptimal due to factors like genetics, egg management, environmental stress, nutrition, and breeder age. Understanding the mechanisms behind compromised hatchability is crucial for improving broiler production.
View Article and Find Full Text PDFZool Res
November 2024
Hebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, Hebei 050024, China.
In group-living animals, chronic juvenile social isolation stress (SIS) can profoundly affect behavior and neuroendocrine regulation. However, its impact on social behavior in avian species, particularly regarding sex-specific neural circuit differences, remains underexplored. This study focused on zebra finches, a species known for its social clustering and cognitive abilities, to elucidate these influences.
View Article and Find Full Text PDFSci Rep
October 2024
Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, 650093, Yunnan, China.
When a hydropower unit operates in a sediment-laden river, the sediment accelerates hydro-turbine wear, leading to efficiency loss or even shutdown. Therefore, wear fault diagnosis is crucial for its safe and stable operation. A hydro-turbine wear fault diagnosis method based on improved WT (wavelet threshold algorithm) preprocessing combined with IWSO (improved white shark optimizer) optimized CNN-LSTM (convolutional neural network-long-short term memory) is proposed.
View Article and Find Full Text PDFNetwork
October 2024
Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Sathyamangalam, India.
The increasing volume of online reviews and tweets poses significant challenges for sentiment classification because of the difficulty in obtaining annotated training data. This paper aims to enhance sentiment classification of Twitter data by developing a robust model that improves classification accuracy and computational efficiency. The proposed method named Tree Hierarchical Deep Convolutional Neural Network optimized with Sheep Flock Optimization Algorithm for Sentiment Classification of Twitter Data (SCTD-THDCNN-SFOA) utilizes the Stanford Sentiment Treebank dataset.
View Article and Find Full Text PDFTrop Anim Health Prod
September 2024
Nigde Omer Halisdemir University, Bor Vocational School, Bor/Niğde, Turkey.
This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, type of flock, birth weight, and weaning weight was analyzed. The data was collected from a total of 25,316 Akkaraman lambs raised at multiple farms in the Çiftlik District of Niğde province.
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