Wired Neuroscience最新文献

Title: Modeling Normal and Imbalanced Neural Avalanches: A Computational Approach to Understanding Criticality in the Brain and Its Potential Role in Schizophrenia 标题：正常和失衡神经崩解建模：理解大脑临界状态及其在精神分裂症中潜在作用的计算方法

Wired Neuroscience Pub Date : 2024-04-05 DOI: 10.1101/2024.04.20.24306133

Richard Murdoch Montgomery

{"title":"Title: Modeling Normal and Imbalanced Neural Avalanches: A Computational Approach to Understanding Criticality in the Brain and Its Potential Role in Schizophrenia","authors":"Richard Murdoch Montgomery","doi":"10.1101/2024.04.20.24306133","DOIUrl":"https://doi.org/10.1101/2024.04.20.24306133","url":null,"abstract":"Neural avalanches, characterized by bursts of activity followed by periods of quiescence, have been observed in the brain and are thought to reflect the critical dynamics necessary for optimal information processing. Deviations from normal avalanche behavior have been hypothesized to underlie various neurological disorders, particularly schizophrenia. Schizophrenia is a complex psychiatric disorder associated with altered perception, cognition, and behavior, and recent theories suggest that disruptions in the brain's critical dynamics may contribute to its pathophysiology. In this study, we present a computational model to investigate the properties of normal and imbalanced neural avalanches, with a focus on understanding the potential role of criticality in schizophrenia. We generate avalanche sizes using the Pareto distribution with a power law exponent of -3/2, which is consistent with experimental observations. The model incorporates increasing avalanche sizes over time to simulate the growth of neural activity. We introduce imbalance by adding lateness or earliness to the avalanche sizes, mimicking the potential disruptions in critical dynamics that may occur in schizophrenia. The mean and standard deviation of avalanche sizes are calculated to characterize the normal and imbalanced behavior. The results are visualized using line plots, with shaded areas representing the standard deviation range. Our model provides a framework for understanding the differences between normal and imbalanced neural avalanches, offering insights into the potential mechanisms underlying the altered critical dynamics in schizophrenia. By exploring the relationship between neural avalanches and schizophrenia, this study contributes to the ongoing efforts to elucidate the neurobiological basis of this disorder and may inform future research on potential diagnostic markers and therapeutic interventions targeting the brain's critical dynamics.","PeriodicalId":516928,"journal":{"name":"Wired Neuroscience","volume":"4 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stochastic Modeling of Gene Expression Dynamics: Integration of Regulatory Interactions and Noise 基因表达动态的随机建模：调控相互作用与噪声的整合

Wired Neuroscience Pub Date : 2024-04-05 DOI: 10.62162/1060831

Richard Montgomery

{"title":"Stochastic Modeling of Gene Expression Dynamics: Integration of Regulatory Interactions and Noise","authors":"Richard Montgomery","doi":"10.62162/1060831","DOIUrl":"https://doi.org/10.62162/1060831","url":null,"abstract":"In this study, I present a computational framework to simulate the dynamics of gene expression incorporating both regulatory interactions and stochastic variations. My model integrates deterministic components, representing the basal synthesis and degradation of gene products, along with the effects of regulatory interactions among multiple genes, and stochastic components that introduce biological noise into the system. Specifically, I define a system of stochastic differential equations (SDEs) where each gene's expression level is influenced by an interaction matrix capturing the activating or repressing effects among genes, basal synthesis rates, degradation rates, and random fluctuations modeled by Gaussian noise. This approach allows us to capture the complex dynamics of gene expression, highlighting how regulatory networks and noise jointly shape the behavior of genetic systems over time. Through numerical simulation over 10,000-time units with a fine-grained temporal resolution, we explore the temporal evolution of gene expression levels, providing insights into the interplay between deterministic regulatory mechanisms and stochasticity in gene expression. My findings underscore the importance of considering both deterministic interactions and stochastic effects to understand the dynamics of gene networks, offering a valuable tool for the study of genetic regulation and expression variability. Key Concepts: Stochastic Gene Expression, Noise, Modeling","PeriodicalId":516928,"journal":{"name":"Wired Neuroscience","volume":"40 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Neuroprosthetic Control through AI-Powered Adaptive Learning: A Simulation Study 通过人工智能驱动的自适应学习增强神经假体控制：模拟研究

Wired Neuroscience Pub Date : 2024-04-05 DOI: 10.62162/1000000

Richard Murdoch Montgomery

{"title":"Enhancing Neuroprosthetic Control through AI-Powered Adaptive Learning: A Simulation Study","authors":"Richard Murdoch Montgomery","doi":"10.62162/1000000","DOIUrl":"https://doi.org/10.62162/1000000","url":null,"abstract":"Neuroprosthetic devices have the potential to restore motor function and improve the quality of life for individuals with limb loss or motor impairments. However, the control of these devices remains challenging due to the complex nature of brain-machine interfaces and the variability in individual brain wave patterns. This study presents a simulation framework that combines electroencephalogram (EEG) analysis with artificial intelligence (AI) to enhance the control and adaptation of neuroprosthetic devices. We simulate brain wave patterns across key motor regions, including the motor cortex, premotor cortex, supplementary motor area, parietal lobe, and cerebellum, during a simplified arm movement task. The simulation demonstrates the synchronization of brain waves over time, representing the neural dynamics underlying motor control. I propose an AI-powered approach that leverages machine learning algorithms to continuously learn from the user's brain wave patterns and adapt the control strategies of the neuroprosthetic device. The AI system enables personalized calibration, continuous learning, and improved movement control, leading to more intuitive and efficient use of the prosthesis. Furthermore, I discuss the potential for personalized feedback and training, as well as continuous upgrades and enhancements to the AI-powered neuroprosthetic devices. My simulation study highlights the promising role of AI in advancing neuroprosthetic control and lays the foundation for future research and development in this field. By harnessing the power of AI and adaptive learning, I envision a future where individuals with motor impairments can regain a higher level of independence and functionality through seamless integration with intelligent neuroprosthetic devices.","PeriodicalId":516928,"journal":{"name":"Wired Neuroscience","volume":"64 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modulating Neural Field Dynamics: Insights from rTMS Coupling Simulations 调节神经场动态：经颅磁刺激耦合模拟的启示

Wired Neuroscience Pub Date : 2024-03-05 DOI: 10.62162/1060731

Richard Murdoch Montgomery

{"title":"Modulating Neural Field Dynamics: Insights from rTMS Coupling Simulations","authors":"Richard Murdoch Montgomery","doi":"10.62162/1060731","DOIUrl":"https://doi.org/10.62162/1060731","url":null,"abstract":"This article presents a computational investigation into the effects of repetitive Transcranial Magnetic Stimulation (rTMS) on neural field dynamics, elucidating the mechanisms by which external magnetic fields modulate neural activity across spatial and temporal dimensions. Leveraging a modified neural field model, we integrate the principles of magnetic neuromodulation with continuous descriptions of cortical activity to explore the entrainment of neural oscillations and the modulation of activity patterns. Our simulations reveal oscillatory dynamics induced by rTMS, characterized by temporal patterns that mirror the stimulation frequency, and spatially distributed activity changes that suggest potential therapeutic and research applications. Through a combination of temporal analysis and spatial visualization, we demonstrate how rTMS can entrain specific neural oscillations, offering insights into its role in modulating cognitive states and treating neuropsychiatric disorders. This work contributes to the growing field of computational neuroscience by providing a framework for understanding the complex interactions between external neuromodulation techniques and intrinsic neural dynamics, paving the way for optimized therapeutic strategies and enhanced understanding of brain function.","PeriodicalId":516928,"journal":{"name":"Wired Neuroscience","volume":"125 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140078890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Biological Constraints to Unbounded Artificial Evolution: Exploring the Implications of AI's Accelerated Advancement 从生物制约到无界人工进化：探索人工智能加速发展的影响

Wired Neuroscience Pub Date : 2024-03-05 DOI: 10.62162/1062011

Richard Murdoch Montgomery

{"title":"From Biological Constraints to Unbounded Artificial Evolution: Exploring the Implications of AI's Accelerated Advancement","authors":"Richard Murdoch Montgomery","doi":"10.62162/1062011","DOIUrl":"https://doi.org/10.62162/1062011","url":null,"abstract":"This article explores the fundamental differences between biological evolution and the evolution of artificial intelligence (AI), focusing on the implications of AI's potential for unbounded advancement. Biological evolution, as exemplified by the human brain, is constrained by various factors such as physical limitations, environmental pressures, and the need to maintain a delicate balance within the organism's overall structure. These constraints result in a gradual, stepwise process of adaptation and optimization over millions of years. In contrast, AI systems are not bound by the same limitations as biological organisms. They can learn from vast amounts of data, optimize their own performance, and potentially engage in recursive self-improvement, leading to rapid and open-ended progress. This unbounded evolution of AI raises both exciting possibilities and significant challenges for human society. The article discusses the potential benefits of AI's accelerated evolution, such as groundbreaking advancements in science, technology, and solutions to global problems. However, it also highlights the risks associated with the emergence of uncontrollable or misaligned AI systems, which could have unintended consequences and pose existential risks to humanity. To navigate the complex landscape of AI evolution, the article emphasizes the need for collaboration across disciplines, including researchers, policymakers, and society as a whole. Developing frameworks for AI safety and ethics, as well as technical approaches to ensure AI alignment with human values and goals, is crucial to mitigating potential risks.","PeriodicalId":516928,"journal":{"name":"Wired Neuroscience","volume":"46 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140264580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

"Heterogeneity and Criticality in Neural Networks: A Computational Model of the Griffiths Phase" "神经网络中的异质性和临界性：格里菲斯阶段的计算模型"

Wired Neuroscience Pub Date : 2024-02-05 DOI: 10.62162/1060531

Richard Murdoch Motgomery

{"title":"\"Heterogeneity and Criticality in Neural Networks: A Computational Model of the Griffiths Phase\"","authors":"Richard Murdoch Motgomery","doi":"10.62162/1060531","DOIUrl":"https://doi.org/10.62162/1060531","url":null,"abstract":"This article presents a computational exploration into the dynamics of neural networks, focusing on the manifestation of heterogeneity and critical behavior akin to the Griffiths phase observed in condensed matter physics. Drawing inspiration from the rich tapestry of neural activities within the human neocortex, we develop a simplified model to investigate how networks with diverse propensities for critical behavior can exhibit complex, adaptive functionalities. Utilizing an Erdős–Rényi random graph as the foundation, we introduce a proportion of nodes designated as \"critical,\" possessing a higher likelihood of activation, to simulate the nuanced interplay between different regions of a theoretical brain network. Through iterative simulations, we examine the activation patterns over time, paying close attention to the influence of critical nodes on the overall network dynamics. The model integrates fundamental principles from network theory and neuroscience, offering insights into the potential mechanisms underlying the brain's remarkable ability to process information efficiently, adapt to new stimuli, and maintain robustness in the face of perturbations. Our findings highlight the importance of heterogeneity and criticality in enhancing the computational capabilities of neural networks, providing a conceptual bridge between theoretical physics and cognitive neuroscience. This work contributes to a deeper understanding of the brain's operational principles and opens avenues for further research into the applications of criticality and Griffiths phases in artificial intelligence and neural network design.","PeriodicalId":516928,"journal":{"name":"Wired Neuroscience","volume":"113 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Modeling of Neural Cell Network Formation: Some Insights into Early Brain Development 神经细胞网络形成的动态模型：早期大脑发育的一些启示

Wired Neuroscience Pub Date : 2024-02-05 DOI: 10.62162/1060431

Richard Murdoch Montgomery, Paula Sakuramoto

{"title":"Dynamic Modeling of Neural Cell Network Formation: Some Insights into Early Brain Development","authors":"Richard Murdoch Montgomery, Paula Sakuramoto","doi":"10.62162/1060431","DOIUrl":"https://doi.org/10.62162/1060431","url":null,"abstract":"This study presents a computational simulation that illustrates the early stages of neural cell network formation, a fundamental process in brain development. We developed a two-phase model to simulate the dynamic and complex interactions of neural cells during and after migration. In the first phase, we focused on the radial and tangential migration of neural cells, incorporating movements influenced by both genetic programming and environmental factors. This phase was characterized by increased radial and tangential migration strengths, allowing cells to disperse and form initial connections. In the second phase, we shifted our focus to network development, simulating the reinforcement of existing connections and the formation of new ones, leading to the emergence of complex network structures. My simulation employed a rule-based approach to model cell movement and adhesion, capturing the essence of cell migration patterns and early network formation such a rich clubs and small worlds. The results highlight how neural cells, starting from random positions, migrate directionally, forming rudimentary networks that evolve into more complex structures. The study provides a visual and conceptual framework for understanding the initial formation of neural networks and offers insights into the interplay between genetic predispositions and environmental stimuli in brain development. This model serves as a foundation for future research exploring the intricacies of neural network formation and its implications for neurological development and disorders.","PeriodicalId":516928,"journal":{"name":"Wired Neuroscience","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

“Is the Brain an Over-the-Top Evolution Basis Organ? The Adaptive Significance of Neurodevelopmental Traits: A Hypothetical Examination of ADHD” "大脑是进化的基础器官吗？神经发育特征的适应意义：多动症的假设性研究"

Wired Neuroscience Pub Date : 2024-02-05 DOI: 10.62162/1060631

Richard Montgomery

{"title":"“Is the Brain an Over-the-Top Evolution Basis Organ? The Adaptive Significance of Neurodevelopmental Traits: A Hypothetical Examination of ADHD”","authors":"Richard Montgomery","doi":"10.62162/1060631","DOIUrl":"https://doi.org/10.62162/1060631","url":null,"abstract":"This discussion delves into the intriguing hypothesis that the human brain may had to evolve, at least, in part, as an over-the-top organ, due to its inherent complexity and boundaries. It discusses certain traits associated with neurodevelopmental conditions, particularly Attention-Deficit/Hyperactivity Disorder (ADHD), which may have held adaptive value in ancestral human populations. By considering the evolutionary context and potential advantages conferred by traits such as impulsivity, creativity, and vigilance, I explore the possibility that ADHD traits contributed to the survival and success of individuals and communities in dynamic and challenging environments. Drawing from evolutionary psychology, developmental neuroscience, and cognitive science, I examine hypothetical scenarios in which features of ADHD may have facilitated exploration, innovation, and adaptability in ancestral contexts. While acknowledging the complexities and nuances of neurodevelopmental conditions and their impact on individuals in contemporary society, this discussion aims to stimulate thoughtful consideration of the evolutionary origins of human diversity and behavior.","PeriodicalId":516928,"journal":{"name":"Wired Neuroscience","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Modeling of Neural Cell Network Formation: Some Insights into Early Brain Development 神经细胞网络形成的动态模型：早期大脑发育的一些启示

Wired Neuroscience Pub Date : 2024-02-05 DOI: 10.62162/1060431

Richard Murdoch Montgomery, Paula Sakuramoto

{"title":"Dynamic Modeling of Neural Cell Network Formation: Some Insights into Early Brain Development","authors":"Richard Murdoch Montgomery, Paula Sakuramoto","doi":"10.62162/1060431","DOIUrl":"https://doi.org/10.62162/1060431","url":null,"abstract":"This study presents a computational simulation that illustrates the early stages of neural cell network formation, a fundamental process in brain development. We developed a two-phase model to simulate the dynamic and complex interactions of neural cells during and after migration. In the first phase, we focused on the radial and tangential migration of neural cells, incorporating movements influenced by both genetic programming and environmental factors. This phase was characterized by increased radial and tangential migration strengths, allowing cells to disperse and form initial connections. In the second phase, we shifted our focus to network development, simulating the reinforcement of existing connections and the formation of new ones, leading to the emergence of complex network structures. My simulation employed a rule-based approach to model cell movement and adhesion, capturing the essence of cell migration patterns and early network formation such a rich clubs and small worlds. The results highlight how neural cells, starting from random positions, migrate directionally, forming rudimentary networks that evolve into more complex structures. The study provides a visual and conceptual framework for understanding the initial formation of neural networks and offers insights into the interplay between genetic predispositions and environmental stimuli in brain development. This model serves as a foundation for future research exploring the intricacies of neural network formation and its implications for neurological development and disorders.","PeriodicalId":516928,"journal":{"name":"Wired Neuroscience","volume":"117 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

“Evolutionary Dynamics of Sickle Cell Trait and Malaria: A Multifaceted Exploration from Genetic Adaptation to Modern Implications – Chapter 1“ "镰状细胞性状和疟疾的进化动态：从遗传适应到现代影响的多方面探索 - 第 1 章"

Wired Neuroscience Pub Date : 2024-02-05 DOI: 10.62162/1060131

Richard Murdoch Montgomery

{"title":"“Evolutionary Dynamics of Sickle Cell Trait and Malaria: A Multifaceted Exploration from Genetic Adaptation to Modern Implications – Chapter 1“","authors":"Richard Murdoch Montgomery","doi":"10.62162/1060131","DOIUrl":"https://doi.org/10.62162/1060131","url":null,"abstract":"This comprehensive study (divided in 2) delves into the complex relationship between the sickle cell trait and malaria, examining it from evolutionary, environmental, cultural, and modern perspectives. We explore the natural selection mechanisms that have favored the sickle cell trait as an adaptive response to malaria, particularly the concept of heterozygote advantage. The study also investigates the influence of environmental factors, such as climate and geography, on the prevalence of both the trait and the disease. We consider the role of genetic drift and gene flow in shaping the distribution of the sickle cell trait across different populations. Cultural factors, including marriage practices and social stigma, are examined for their impact on the prevalence and management of the trait and disease. The paper also discusses the modern implications of this relationship, including medical advances and ethical considerations. Additionally, we delve into the historical impact of malaria on ancient civilizations, using the Roman Empire as a case study. Through a multidisciplinary approach, this study aims to provide a holistic understanding of the intricate interplay between genetic adaptation, environmental pressures, and human society in the context of the sickle cell trait and malaria.","PeriodicalId":516928,"journal":{"name":"Wired Neuroscience","volume":"106 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0