Learning disabilities affect a significant proportion of children worldwide, with far-reaching consequences for their academic, professional, and personal lives. Here we develop digital twins – biologically plausible personalized Deep Neural Networks (pDNNs) – to investigate the neurophysiological mechanisms underlying learning disabilities in children. Our pDNN reproduces behavioral and neural activity patterns observed in affected children, including lower performance accuracy, slower learning rates, neural hyper-excitability, and reduced neural differentiation of numerical problems. Crucially, pDNN models reveal aberrancies in the geometry of manifold structure, providing a comprehensive view of how neural excitability influences both learning performance and the internal structure of neural representations. Our findings not only advance knowledge of the neurophysiological underpinnings of learning differences but also open avenues for targeted, personalized strategies designed to bridge cognitive gaps in affected children. This work reveals the power of digital twins integrating AI and neuroscience to uncover mechanisms underlying neurodevelopmental disorders.
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The Lombard Effect: From Acoustics to Neural Mechanisms
Understanding the neural underpinnings of vocal–motor control in humans and other animals remains a major challenge in neurobiology. The Lombard effect – a rise incall amplitude in response to background noise – hasbeen demonstratedin a wide range of vertebrates. Here, we review both behavioral and neurophysio- logical data and propose that the Lombard effect is driven by a subcortical neural network, which can be modulated by cortical processes. The proposed framework offers mechanistic explanations for two fundamental features of the Lombard effect: its widespread taxonomic distribution across the vertebrate phylogenetic tree and the widely observed variations in compensation magnitude. We highlight the Lombard effect as a model behavioral paradigm for unraveling some of the neural underpinnings of audiovocal integration.
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- Award ID(s):
- 1734744
- PAR ID:
- 10104430
- Date Published:
- Journal Name:
- Trends in cognitive sciences
- Volume:
- 41
- Issue:
- 12
- ISSN:
- 1879-307X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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