Advanced cognitive tasks are encoded in distributed neocortical circuits that span multiple forebrain areas. Nonetheless, synaptic plasticity and neural network theories hypothesize that essential information for performing these tasks is encoded in specific ensembles within these circuits. Relatively simpler subcortical areas contain specific ensembles that encode learning, suggesting that neocortical circuits contain such ensembles. Previously, using localized gene transfer of a constitutively active protein kinase C (PKC), we established that a genetically-modified circuit in rat postrhinal cortex, part of the hippocampal formation, can encode some essential information for performing specific visual shape discriminations. However, these studies did not identify any specific neurons that encode learning; the entire circuit might be required. Here, we show that both learning and recall require fast neurotransmitter release from an identified ensemble within this circuit, the transduced neurons; we blocked fast release from these neurons by coexpressing a Synaptotagmin I siRNA with the constitutively active PKC. During learning or recall, specific signaling pathways required for learning are activated in this ensemble; during learning, calcium/calmodulin-dependent protein kinase II, MAP kinase, and CREB are activated; and, during recall, dendritic protein synthesis and CREB are activated. Using activity-dependent gene imaging, we showed that during learning, activity in this ensemble is required to recruit and activate the circuit. Further, after learning, during image presentation, blocking activity in this ensemble reduces accuracy, even though most of the rest of the circuit is activated. Thus, an identified ensemble within a neocortical circuit encodes essential information for performing an advanced cognitive task.
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Sci Rep
January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
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January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
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January 2025
United States Department of Agriculture-Agriculture Research Service, Grassland Soil and Water Research Laboratory, Temple, TX 76502, USA.
Efficient and reliable corn ( L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-scale area, better representing real-world agricultural conditions and offering more practical relevance for farmers.
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January 2025
GSK Carbon Neutral Laboratories for Sustainable Chemistry, Jubilee Campus, University of Nottingham, Triumph Road, Nottingham NG7 2TU, UK.
The range of chemical databases available has dramatically increased in recent years, but the reliability and quality of their data are often negatively affected by human-error fidelity. The size of chemical databases can make manual data curation/checking of such sets time consuming; thus, automated tools to help this process are highly desirable. Herein, we propose the use of Graph Neural Networks (GNNs) to identifying potential stereochemical misassignments in the primary asymmetric catalysis literature.
View Article and Find Full Text PDFBiomolecules
December 2024
Departmento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Mexico City C.P. 09310, Mexico.
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