A number of conceptual difficulties arise when considering the evolutionary origin of consciousness from the pre-conscious condition. There are parallels here with biological pattern formation, where, according to Alan Turing's original formulation of the problem, the statistical properties of molecular-level processes serve as a source of incipient pattern. By analogy, the evolution of consciousness can be thought of as depending in part on a competition between alternative variants in the microstructure of synaptic networks and/or the activity patterns they generate, some of which then serve as neural correlates of consciousness (NCCs). Assuming that NCCs perform this function only if reliably ordered in a particular and precise way, Turing's formulation provides a useful conceptual framework for thinking about how this is achieved developmentally, and how changes in neural structure might correlate with change at the level of conscious experience. The analysis is largely silent concerning the nature and ultimate source of conscious experience, but shows that achieving sentience is sufficient to begin the process by which evolution elaborates and shapes that first experience. By implication, much of what evolved consciousness achieves in adaptive terms can in principle be investigated irrespective of whether or not the ultimate source of real-time experience is known or understood. This includes the important issue of how precisely NCCs must be structured to ensure that each evokes a particular experience as opposed to any other. Some terminological issues are clarified, including that of "noise," which here refers to the statistical variations in neural structure that arise during development, not to sensory noise as experienced in real time.
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http://dx.doi.org/10.3389/fnbeh.2020.598561 | DOI Listing |
Bioinformatics
January 2025
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning models primarily relied on primary or secondary protein structural and related properties, which have limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context around the target site.
View Article and Find Full Text PDFBioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Sichuan 611756, China.
Motivation: The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant.
View Article and Find Full Text PDFPhysiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Urban Construction Center of Lucheng District of Wenzhou, Wenzhou, 325000, China.
The identification of vibration and reconstruction of sound fields of plate structures are important for understanding the vibroacoustic characteristics of complex structures. This paper presents a data-physics driven (DPD) model integrated with transfer learning (DPDT) for high-precision identification and reconstruction of vibration and noise radiation of plate structures. The model combines the Kirchhoff-Helmholtz integral equation with convolutional neural networks, leveraging physical information to reduce the need for extensive data.
View Article and Find Full Text PDFBiotechniques
January 2025
Department of Cardiothoracic Surgery, Stanford University, Stanford, California, USA.
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