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| viXra.org > Mind Science > 2501 |
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[4] viXra:2501.0151 [pdf] submitted on 2025-01-28 01:11:36
Authors: Hyunho Shin
Comments: 15 Pages.
This study proposes specific experimental approaches to validate the theory presented in "Self as the Core of Biological Feedback Systems: An Ideogrammatic Model of Memory and Recognition." First, it discusses the premise that information must possess at least a one-dimensional structure in physical form. If a material responsible for memory exists within cells, it is highly likely to be linear proteins (e.g., talin, actin, microtubules) with one-dimensional structures that store information. To test this hypothesis, nanopore and Raman spectroscopy-based analytical techniques are designed. To apply nanopore technology derived from DNA sequencing to protein analysis, the study explores methods for either preserving the three-dimensional structure of proteins or converting them to their one-dimensional form during preprocessing, determining the physical characteristics of nanopores, and analyzing the resulting signals. Particularly, surface-enhanced Raman spectroscopy (SERS) is proposed as a method to analyze structural changes in proteins in real-time and evaluate their potential for information storage. This research presents a novel experimental approach to understand memory and information storage mechanisms at the protein level, providing concrete directions to validate the theoretical hypothesis.
Category: Mind Science
[3] viXra:2501.0118 [pdf] submitted on 2025-01-21 21:36:33
Authors: Max Myakishev-Rempel
Comments: 41 Pages. (Note by viXra Admin: An abstract in the article is required - Please conform)
This chapter presents a theoretical model called "Frare" (FRActal REndering) that explores the role of DNA in consciousness. The author proposes that DNA, through its sequence-specific vibrational properties and dynamic chromatin reorganization, serves as an interface between universal and individual consciousness. The model suggests that DNA, in conjunction with neuronal networks, helps create individual consciousness through a process of filtering and reducing universal consciousness. The author integrates concepts from quantum physics, biology, and consciousness research, emphasizing the importance of imperfection and perpetual self-organization in biological systems.The model incorporates evidence from twin studies showing genetic contributions to mental traits, and draws on research in quantum biology, morphic fields, and psi phenomena. It proposes that DNA functions both as a chemical sequence and as a dynamic hologram, with its vibrational properties playing a key role in consciousness and morphogenesis. The author discusses potential experimental approaches to test these hypotheses, particularly focusing on millimeter wave interactions with DNA and sequence-specific resonance effects.The chapter examines parallels between artificial intelligence and biological consciousness, suggesting that modern AI systems may approach human-like consciousness in certain aspects. The author concludes by outlining specific experimental protocols to investigate the proposed DNA-consciousness connection, including studies of electromagnetic interactions and chromatin dynamics. The work aims to bridge gaps between traditional genetic approaches and quantum theories of consciousness while providing testable hypotheses for future research.
Category: Mind Science
[2] viXra:2501.0067 [pdf] submitted on 2025-01-11 22:28:43
Authors: Arturo Tozzi
Comments: 8 Pages.
A Markov chain (MC) is a mathematical model used to describe a system where the probability of moving to the next state depends solely on the current state and not on the sequence of the preceding states. A Markov blanket (MB) for a node includes its parents, children and other parents of its children, capturing the minimal set of nodes required to make the node conditionally independent from the rest of the network. We examined EEG data from healthy individuals to assess MC and MB connectivity patterns associated with two representative electrodes. The electrode FP1, associated with cognitive functions, displayed connections predominantly with frontal and central regions. The electrode C3, located in the primary motor cortex, displayed connections with bilateral motor and parietal regions. The two electrodes had shared connections, highlighting integration between cognitive and motor networks, while also retaining distinct connections that underscored their specialized roles and functions. Temporal analysis demonstrated significant MB fluctuations across time segments, highlighting phases of increased neural reorganization and stability. Entropy analysis showed significant variability in MC and MB dynamics over time. FP1 exhibited greater entropy variability, reflecting its neural flexibility and involvement in cognitive processes, while C3 showed more stable entropy patterns, aligning with its motor-related functionality. We demonstrate the utility of MC and MBs in capturing the dynamic complexity of the nervous activity, underscoring the distinct and overlapping roles of brain regions in balancing dynamic flexibility and functional specialization. Our findings have implications for cognitive neuroscience and brain-computer interface design.
Category: Mind Science
[1] viXra:2501.0027 [pdf] submitted on 2025-01-06 10:04:34
Authors: Arturo Tozzi
Comments: 8 Pages.
The neural mechanisms underlying individual differences in intelligence are a central focus in neuroscience. We investigated the effectiveness of Monte Carlo simulations in predicting real EEG patterns and uncovering potential neural differences between individuals with high and low intelligence. EEG data were collected from two groups of volunteers categorized by IQ, namely, a high-IQ group and a low-IQ group. A univariate normal distribution was fitted to each EEG channel using Maximum Likelihood Estimation, after which synthetic datasets were generated based on the estimated parameters. Statistical analyses including Root Mean Square Error (RMSE) calculations assessed the alignment between real and simulated data. We showed that Monte Carlo simulations effectively replicated the statistical properties of the EEG data from both the groups, closely matching the real central tendencies, variability and overall distribution shapes. Specific EEG channels, particularly in the frontal and temporal bilateral regions, exhibited significant differences between the two groups, pointing to potential neural markers of cognitive abilities. Further, the low-IQ group exhibited higher predictability and more consistent neural patterns, reflected by lower RMSE values and smaller standard deviations across several EEG channels. Conversely, the high-IQ group displayed greater variability and larger RMSE values, reflecting complex neural dynamics that are less predictable by Monte Carlo simulations. Our findings underscore the utility of Monte Carlo simulations as a robust tool for replicating EEG patterns, identifying cognitive differences and predicting EEG activity associated with intelligence levels. These insights can inform predictive modeling, neurocognitive research, educational strategies and clinical interventions of targeted cognitive enhancement.
Category: Mind Science
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