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Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.
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http://dx.doi.org/10.3389/fncel.2024.1366200 | DOI Listing |
J Immunother Cancer
September 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Neoadjuvant immunochemotherapy (nICT) has demonstrated significant potential in improving pathological response rates and survival outcomes for patients with locally advanced esophageal squamous cell carcinoma (ESCC). However, substantial interindividual variability in therapeutic outcomes highlights the urgent need for more precise predictive tools to guide clinical decision-making. Traditional biomarkers remain limited in both predictive performance and clinical feasibility.
View Article and Find Full Text PDFBrief Bioinform
August 2025
School of Information and Artificial Intelligence, Anhui Agricultural University, 130 Changjiang Road, Shushan District, Hefei, Anhui 230036, China.
Protein-nucleic acid binding sites play a crucial role in biological processes such as gene expression, signal transduction, replication, and transcription. In recent years, with the development of artificial intelligence, protein language models, graph neural networks, and transformer architectures have been adopted to develop both structure-based and sequence-based predictive models. Structure-based methods benefit from the spatial relationship between residues and have shown promising performance.
View Article and Find Full Text PDFTransl Vis Sci Technol
September 2025
Department of Ophthalmology, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania, USA.
Purpose: To evaluate choroidal vasculature using a novel three-dimensional algorithm in fellow eyes of patients with unilateral chronic central serous chorioretinopathy (cCSC).
Methods: Patients with unilateral cCSC were retrospectively included. Automated choroidal segmentation was conducted using a deep-learning ResUNet model.
Neuroimage
September 2025
Center for Bioelectric Interfaces, Higher School of Economics, Moscow, Russia; LLC "Life Improvement by Future Technologies Center", Moscow, Russia; AIRI, Artificial Intelligence Research Institute, Moscow, Russia. Electronic address:
Objective: Upcoming neuroscientific research will require bidirectional and context dependent interaction with nervous tissue. To facilitate the future neuroscientific discoveries we have created HarPULL, a genuinely real-time system for tracking oscillatory brain state.
Approach: The HarPULL technology ensures reliable, accurate and affordable real-time phase and amplitude tracking based on the state-space estimation framework operationalized by Kalman filtering.
PLoS Comput Biol
September 2025
School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.
Drug-induced liver injury is a leading cause of high attrition rates for both candidate drugs and marketed medications. Previous in silico models may not effectively utilize biological drug property information and often lack robust model validation. In this study, we developed a graph convolutional network embedded with a biological graph learning (BioGL) module-named BioGL-GCN(Biological Graph Learning-Graph Convolutional Network)-for drug-induced liver injury prediction using toxicogenomic profiles.
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