Developing animals must begin to interact with the world before their neural development is complete. This means they must build neural codes appropriate for turning sensory inputs into motor outputs adaptively as their neural hardware matures. We review some recent progress in the understanding of the relationship between neural coding and neural circuit development. We focus particularly on neural coding in the context of topographic maps and spontaneous activity, as well as receptive field and circuit development, drawing on examples from both mammalian visual cortex and fish optic tectum. Overall we suggest that neural coding strategies during development may be highly dynamic.
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http://dx.doi.org/10.1016/j.tins.2018.05.011 | DOI Listing |
iScience
January 2025
Division of Optometry, Health Sciences, City University of London, London EC1V 0HB, UK.
A key property of our environment is the mirror symmetry of many objects, although symmetry is an abstract global property with no definable shape template, making symmetry identification a challenge for standard template-matching algorithms. We therefore ask whether Deep Neural Networks (DNNs) trained on typical natural environmental images develop a selectivity for symmetry similar to that of the human brain. We tested a DNN trained on such typical natural images with object-free random-dot images of 1, 2, and 4 symmetry axes.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea.
Our naturalistic experiences are organized into memories through multiple processes, including novelty encoding, memory formation, and retrieval. However, the neural mechanisms coordinating these processes remain elusive. Using fMRI data acquired during movie viewing and subsequent narrative recall, we examine hippocampal neural subspaces associated with distinct memory processes and characterized their relationships.
View Article and Find Full Text PDFJ Neurosci
January 2025
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742
When we listen to speech, our brain's neurophysiological responses "track" its acoustic features, but it is less well understood how these auditory responses are enhanced by linguistic content. Here, we recorded magnetoencephalography (MEG) responses while subjects of both sexes listened to four types of continuous-speech-like passages: speech-envelope modulated noise, English-like non-words, scrambled words, and a narrative passage. Temporal response function (TRF) analysis provides strong neural evidence for the emergent features of speech processing in cortex, from acoustics to higher-level linguistics, as incremental steps in neural speech processing.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
School of Information Science and Technology, Fudan University, Shanghai, 200433, China; Key Laboratory of Medical Imaging, Computing and Computer Assisted Intervention, Shanghai, 200433, China. Electronic address:
Background And Objective: Utilizing AI to mine tumor microenvironment information in whole slide images (WSIs) for glioma molecular subtype and prognosis prediction is significant for treatment. Existing weakly-supervised learning frameworks based on multi-instance learning have potential in WSIs analysis, but the large number of patches from WSIs challenges the effective extraction of key local patch and neighboring patch microenvironment info. Therefore, this paper aims to develop an automatic neural network that effectively extracts tumor microenvironment information from WSIs to predict molecular typing and prognosis of glioma.
View Article and Find Full Text PDFMol Autism
January 2025
Department of Special Education, University of Haifa, Haifa, Israel.
Background: Alterations in sensory perception, a core phenotype of autism, are attributed to imbalanced integration of sensory information and prior knowledge during perceptual statistical (Bayesian) inference. This hypothesis has gained momentum in recent years, partly because it can be implemented both at the computational level, as in Bayesian perception, and at the level of canonical neural microcircuitry, as in predictive coding. However, empirical investigations have yielded conflicting results with evidence remaining limited.
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