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Our brain constantly combines sensory information in unitary percept to build coherent representations of the environment. Even though this process could appear smooth, integrating sensory inputs from various sensory modalities must overcome several computational issues, such as recoding and statistical inferences problems. Following these assumptions, we developed a neural architecture replicating humans' ability to use audiovisual spatial representations. We considered the well-known ventriloquist illusion as a benchmark to evaluate its phenomenological plausibility. Our model closely replicated human perceptual behavior, proving a truthful approximation of the brain's ability to develop audiovisual spatial representations. Considering its ability to model audiovisual performance in a spatial localization task, we release our model in conjunction with the dataset we recorded for its validation. We believe it will be a powerful tool to model and better understand multisensory integration processes in experimental and rehabilitation environments.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9994749 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0280987 | PLOS |
AJNR Am J Neuroradiol
September 2025
From the Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America (J.S.S., B.M., S.H., A.H., J.S.), and Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India (H.S.).
Background And Purpose: The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies, these choroidal metastases are often missed. To improve their detection, we aimed to use artificial intelligence to distinguish between brain MRI scans containing normal orbits and choroidal metastases.
Materials And Methods: We present a novel hierarchical deep learning framework for sequential cropping and classification on brain MRI images to detect choroidal metastases.
Cell Syst
September 2025
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address:
Spatial transcriptomics allows for the measurement of gene expression within the native tissue context. However, despite technological advancements, computational methods to link cell states with their microenvironment and compare these relationships across samples and conditions remain limited. To address this, we introduce Tissue Motif-Based Spatial Inference across Conditions (TissueMosaic), a self-supervised convolutional neural network designed to discover and represent tissue architectural motifs from multi-sample spatial transcriptomic datasets.
View Article and Find Full Text PDFNeural Netw
September 2025
Dept. of CSE, Konkuk University, Seoul, 05029, Republic of Korea. Electronic address:
Neural network compression problems have been extensively studied to overcome the limitations of compute-intensive deep learning models. Most of the state-of-the-art solutions in this context are based on network pruning that identify and remove unimportant weights, filters or channels. However, existing methods often lack actual speedup or require complex pruning criteria and additional training (fine-tuning) overhead.
View Article and Find Full Text PDFPLoS One
September 2025
School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, Hunan, China.
Knowledge tracing can reveal students' level of knowledge in relation to their learning performance. Recently, plenty of machine learning algorithms have been proposed to exploit to implement knowledge tracing and have achieved promising outcomes. However, most of the previous approaches were unable to cope with long sequence time-series prediction, which is more valuable than short sequence prediction that is extensively utilized in current knowledge-tracing studies.
View Article and Find Full Text PDFJ Food Sci
September 2025
Department of Food Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, Bangi, Selangor Darul Ehsan, Malaysia.
Advanced intelligent systems are becoming a significant trend, especially in the classification of tropical fruits due to their unique flavor and taste. As one of the most popular tropical fruits worldwide, pineapple (Ananas comosus) has a great chemical composition and is high in nutritional value. A non-destructive method for the determination of pineapple varieties was developed, which utilized thermal imaging and deep learning techniques.
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