One of the most controversial debates in cognitive neuroscience concerns the cortical locus of semantic knowledge and processing in the human brain. Experimental data revealed the existence of various cortical regions relevant for meaning processing, ranging from semantic hubs generally involved in semantic processing to modality-preferential sensorimotor areas involved in the processing of specific conceptual categories. and the brain uses such complex organization for conceptualization can be investigated using biologically constrained neurocomputational models. Here, we improve pre-existing neurocomputational models of semantics by incorporating spiking neurons and a rich connectivity structure between the model 'areas' to mimic important features of the underlying neural substrate. Semantic learning and symbol grounding in action and perception were simulated by associative learning between co-activated neuron populations in frontal, temporal and occipital areas. As a result of Hebbian learning of the correlation structure of symbol, perception and action information, distributed cell assembly circuits emerged across various cortices of the network. These semantic circuits showed category-specific topographical distributions, reaching into motor and visual areas for action- and visually-related words, respectively. All types of semantic circuits included large numbers of neurons in multimodal connector hub areas, which is explained by cortical connectivity structure and the resultant convergence of phonological and semantic information on these zones. Importantly, these semantic hub areas exhibited some category-specificity, which was less pronounced than that observed in primary and secondary modality-preferential cortices. The present neurocomputational model integrates seemingly divergent experimental results about conceptualization and explains both semantic hubs and category-specific areas as an emergent process causally determined by two major factors: neuroanatomical connectivity structure and correlated neuronal activation during language learning.
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http://dx.doi.org/10.3389/fncom.2018.00088 | DOI Listing |
Sci Rep
December 2024
Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Republic of Korea.
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December 2024
Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.
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December 2024
Department of Microbiology, Faculty of Sciences, CEI·MAR-International Campus of Excellence in Marine Science, University of Malaga, Málaga, Spain.
The inclusion of microalgae in functional fish diets has a notable impact on the welfare, metabolism and physiology of the organism. The microbial communities associated with the fish are directly influenced by the host's diet, and further understanding the impact on mucosal microbiota is needed. This study aimed to analyze the microbiota associated with the skin and gills of Sparus aurata fed a diet containing 10% microalgae.
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December 2024
School of Computer and Information Engineering, Hubei Normal University, Huangshi, 435002, China.
For finely representation of complex reservoir units, higher computing overburden and lower spatial resolution are limited to traditional stochastic simulation. Therefore, based on Generative Adversarial Networks (GANs), spatial distribution patterns of regional variables can be reproduced through high-order statistical fitting. However, parameters of GANs cannot be optimized under insufficient training samples.
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December 2024
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, US.
The correlational structure of brain activity dynamics in the absence of stimuli or behavior is often taken to reveal intrinsic properties of neural function. To test the limits of this assumption, we analyzed peripheral contributions to resting state activity measured by fMRI in unanesthetized, chemically immobilized male rats that emulate human neuroimaging conditions. We find that perturbation of somatosensory input channels modifies correlation strengths that relate somatosensory areas both to one another and to higher-order brain regions, despite the absence of ostensible stimuli or movements.
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