Table of Contents
Overview
Definition and Scope
Computational neuroscience is an interdisciplinary field that integrates neuroscience, mathematics, and computer science to understand and simulate brain functions.[5.1] It aims to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system.[24.1] The discipline has evolved significantly since the publication of the Hodgkin-Huxley papers, transitioning from simple computer modeling of neurons to more complex explorations of the computational functions of the brain.[1.1] The field encompasses various approaches, including the development of mathematical frameworks that facilitate the study of brain mechanisms and allow for the simulation and prediction of neuropsychological syndromes.[3.1] Additionally, computational neuroscience has been instrumental in advancing artificial intelligence, particularly through the use of brain-inspired neural network models, which have become central to machine learning.[6.1] Theoretical foundations in computational neuroscience focus on understanding what is meant by 'computation' and 'information processing' within nervous systems, addressing whether these concepts are objective facts or observer-dependent descriptions.[23.1] This theoretical exploration is complemented by practical applications, as computational models are employed to investigate the nature of information processing in large brain-like networks.[25.1] Overall, computational neuroscience serves as a vital framework for both theoretical inquiry and practical application in understanding the complexities of brain function.Interdisciplinary Nature
Computational neuroscience is fundamentally interdisciplinary, drawing from mathematics, computer science, and biology to model complex biological processes. The field employs mathematical techniques, including differential equations, which model events in continuous time, and difference equations, which model events in discrete time, to better explain, analyze, and visualize these processes.[9.1] Over recent decades, advancements in computing have become integral to neuroscience, enabling the development of novel methods and tools that allow researchers to study increasingly complex models and questions.[10.1] The convergence of enhanced computational capabilities and improved experimental data has led to the creation of detailed models that effectively simulate neural dynamics.[10.1] Machine learning models, particularly XGBoost, have shown significant promise in predicting depressive symptoms in older adults with cognitive impairment, as evidenced by their effective performance in this area.[8.1] This approach exemplifies the intersection of computational techniques and biological research, where models are developed and validated to address the complexities of neuropsychological outcomes. For instance, the prediction of psychosis onset illustrates how a combination of aggregated clinical, neuroimaging, and neuropsychological predictors can yield partly additive and explainable effects.[7.1] Therefore, it is crucial to carefully consider the complexity of these models while balancing interpretability and predictive accuracy in the context of neuropsychological research. Moreover, the advent of large-scale language models, such as ChatGPT, has further bridged the gap between artificial intelligence and neuroscience. These models have been utilized for human behavioral simulations and neuroimaging data analysis, highlighting the potential for AI to contribute to cognitive neuroscience by developing sophisticated multivariate models that identify neural co-activation patterns associated with cognitive activities.[13.1] The mutual reinforcement between neuroscience and AI creates a closed loop that enhances our understanding of both fields. Computational neuroscience serves as a bridge, linking the unique architecture and functions of the brain with the design of more efficient neural network architectures in artificial intelligence.[16.1] This synergy not only informs the development of AI systems but also enriches neuroscientific models of brain function, illustrating the dynamic interplay between these disciplines.[17.1] To ensure that mathematical models in computational neuroscience remain biologically relevant, it is crucial to employ modeling strategies that lead to simplified, computationally tractable, and biologically relevant mathematical models.[22.1] This approach is essential for maintaining the integrity of the models and ensuring their applicability within the field of neuroscience.[22.1] The interdisciplinary nature of computational neuroscience promotes collaboration across various scientific domains, fostering innovation and enhancing our understanding of the brain and its functions.In this section:
History
Early Development
The early development of computational neuroscience is significantly marked by the introduction of the Hodgkin-Huxley model in 1952, which initiated the field of biophysically detailed computational modeling in neuroscience.[57.1] This model treated the squid axon as an electrical circuit, with current across the membrane being carried by a capacitor or by one of three ionic currents: potassium (I K), sodium (I Na), and a leakage current (I l).[58.1] The equations derived from the Hodgkin-Huxley model have played a crucial role in the ongoing transition of biology from a descriptive science to a quantitative and predictive science, a process that has been accelerating for over 60 years since the model's introduction.[59.1] The impact of the Hodgkin-Huxley model extends beyond its initial formulation; it has become a central pillar of modern neuroscience research. Its principles have influenced a wide range of studies, from molecular investigations of ion channel function to the computational implications at the circuit level.[60.1] The model's predictive power was notably demonstrated through the numerical integration of its differential equations, which could accurately reproduce key biophysical properties of the action potential, showcasing the model's ability to integrate experimental data into computational frameworks.[76.1] This integration has been pivotal in shaping our current understanding of neural dynamics and has paved the way for the flourishing field of computational neuroscience.[77.1] As computational neuroscience evolved, the need for high-performance computing systems became apparent due to the computational intensity of simulations required to decipher underlying biological processes.[61.1] This led to the development of advanced simulators, such as SimHH, which builds upon the Hodgkin-Huxley framework to enhance efficiency in modeling brain activity.[61.1] Overall, the Hodgkin-Huxley model not only initiated the field of computational neuroscience but also set a standard for subsequent advancements in the discipline.Establishment as a Discipline
Computational neuroscience has evolved significantly since the publication of the foundational Hodgkin-Huxley papers, which laid the groundwork for the field by providing a mathematical framework for understanding neuronal action potentials and ionic conductances.[62.1] Over the past fifty years, the discipline has transitioned from simple computer modeling of neurons to a more comprehensive exploration of the computational functions of neural systems.[1.1] The annual Computational Neuroscience Meeting (CNS) was established in 1990 as a small workshop known as the Analysis and Modeling of Neural Systems, aimed at exploring the intersection of neuroscience and computation.[2.1] This workshop gained traction as it rode on the success of several seminal papers that popularized neural networks among physicists, leading to an increased interest in the quantitative methods employed in these abstract model networks.[2.1] However, by the turn of the millennium, the computational neuroscience community recognized that the field was experiencing a software crisis, marked by stagnation in software development and a decline in research reproducibility.[45.1] This realization highlighted the importance of focusing on software sustainability as a critical factor for advancing the discipline of computational neuroscience.[45.1] The field of computational neuroscience emerged as a significant area of study where mathematical tools and theories are employed to investigate brain function, incorporating diverse approaches from various scientific disciplines.[81.1] This development was catalyzed by the marriage of neural recording with computational theory, which began to yield promising results and ultimately led to the inauguration of the Computational and Systems Neuroscience (Cosyne) meeting in 2004.[46.1] The establishment of such conferences has been instrumental in promoting interdisciplinary collaboration, facilitating exchanges between experimentalists and theorists that are crucial for advancing our understanding of neural processes.[81.1] The Hodgkin-Huxley (HH) model has served as an invaluable tool in computational neuroscience for over seventy years, facilitating robust and reproducible modeling of ionic conductances and the electrophysiological phenomena they underlie.[62.1] Developed through the pioneering work of Alan Lloyd Hodgkin and Andrew F. Huxley on the squid giant axon, this model remains a cornerstone for simulating the electrical characteristics of neurons.[62.1] The NIH Blueprint for Neuroscience Research actively supports the development and dissemination of new research tools and resources, which are essential for assisting neuroscience researchers and clinicians.[63.1] In this context, containerized software has emerged as a particularly effective strategy, ensuring that processing pipelines run reliably and uniformly across different computing environments, thereby alleviating concerns regarding variations in software dependencies or system configurations during collaborations between various research institutes.[64.1]Foundations Of Computational Neuroscience
Neuron Models
Neuron models in computational neuroscience are essential for understanding brain function, and they can be categorized into two complementary approaches: simplifying models and realistic models. Simplifying models focus on specific dynamics by abstracting biological processes, while realistic models aim to capture the intricate details of neuronal function. This article illustrates the utility of simplifying neural models through examples, including the development of neuronal positioning, which highlights how these models can provide unexpected insights in neuroscience.[108.1] A critical aspect of neuron models is the representation of neuronal connectivity, which significantly influences network dynamics. The choice of connectivity rules can lead to qualitatively different outcomes in model behavior, highlighting the importance of accurately modeling neuron interconnections.[101.1] Recent advancements have proposed frameworks that conceptualize communication between neuronal networks as trajectories in a two-dimensional state space, allowing for a unified understanding of various communication mechanisms.[99.1] The adaptability of biological systems is also reflected in the design of computational models. Neuronal networks are not only studied as computational systems but also as communication networks that transfer substantial amounts of information between different brain areas.[97.1] This dual role underscores the necessity of developing models that can effectively capture both the computational and communicative aspects of neuronal activity. Furthermore, the integration of experimental data into computational models enhances their accuracy and reliability. For instance, when modeling the responses of visual neurons, researchers can parameterize the constraints of efficient coding models using data from retinal photoreceptors, thereby refining the foundational assumptions of these models.[114.1] This integration exemplifies how empirical findings can inform and improve computational frameworks in neuroscience. Despite the advancements in modeling techniques, challenges remain in balancing model complexity with computational efficiency. Researchers often face trade-offs between accuracy, reliability, and computational expense, particularly in parameter estimation.[111.1] Additionally, the speed of simulations poses a significant challenge, as processes such as brain development and learning occur over extended periods, making real-time simulations currently unattainable.[113.1] Addressing these challenges is crucial for the continued evolution of neuron models and their applications in understanding brain function.Mathematical Frameworks
Computational neuroscience employs a variety of mathematical frameworks to model and understand the complexities of neural processes. One foundational aspect of this field is the concept of neural coding, which refers to how information is represented by neural activity. This understanding is crucial for deciphering how sensory inputs are transformed into electrical signals within the nervous system.[91.1] The discipline is characterized by its interdisciplinary nature, integrating mathematical models, theoretical analysis, and computer simulations to explore the structure and dynamics of the nervous system and brain, particularly in relation to cognitive and behavioral functions.[92.1] Theoretical neuroscience, a subset of computational neuroscience, focuses on developing mathematical, computational, and statistical models that represent neural processes across various scales, from individual neurons to entire brain networks.[95.1] A significant advancement in the mathematical modeling of neuronal dynamics can be traced back to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s, which laid the groundwork for subsequent models.[96.1] Among these, spiking neural networks (SNNs) have gained prominence for simulating neuronal behavior through spike generation. Various models, such as leaky integrate-and-fire (LIF) and non-leaky integrate-and-fire (NLIF), are utilized to construct SNNs, although their implementation presents several challenges.[94.1] Moreover, the development of efficient computational mathematics in multiscale neuroscience has become increasingly complex, necessitating the creation of adequate multi-physics models and accurate three-dimensional morphologies, alongside stable domain and partial differential equation (PDE) discretization methods.[93.1] This intricate mathematical framework not only aids in analyzing neural data but also enhances our understanding of brain functions, including sensory processing, memory, and emotional responses.[95.1]Recent Advancements
Integration with Artificial Intelligence
Recent advancements in computational neuroscience have increasingly intersected with artificial intelligence (AI), leading to significant developments in both fields. The integration of AI into neuroscience has been propelled by the emergence of large-scale language models (LLMs) like ChatGPT, which have impacted areas such as AI-based human behavioral simulations and neuroimaging data analysis. These advancements have sparked interest in bridging AI with human cognition, suggesting that the synergy between the two could yield breakthroughs in brain research and clinical practice, despite challenges such as reliance on correlative data and ethical concerns.[130.1] Computational neuroscience is an interdisciplinary field that combines elements of neuroscience, mathematics, and computer science to understand and simulate brain functions.[5.1] In recent years, this field has entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modeling at multiple scales, from molecules to the whole brain.[129.1] A significant development in this area is the advent of large-scale language models, such as ChatGPT, which have made a notable impact on neuroscience. These models facilitate AI-based human behavioral simulations, standardized neuroimaging data analysis, and neurotheoretical validations, thereby fueling interest in bridging AI and human cognition.[130.1] One of the primary benefits of AI in cognitive neuroscience is its ability to develop sophisticated multivariate models for identifying neural co-activation patterns associated with cognitive activities.[130.1] However, this integration also presents challenges, including an over-reliance on correlative data, the complexity of neural networks, ethical concerns, and the lack of standardization.[130.1] The integration of neuroscience and artificial intelligence (AI) presents significant ethical challenges, including concerns related to data privacy, bias, and accountability.[144.1] As AI technologies advance, it is crucial for researchers to consider the ethical implications of these developments, particularly in light of the potential for misuse.[146.1] The embedded ethics approach has been proposed as a framework to navigate these ethical, legal, and social issues, emphasizing the importance of interdisciplinary collaboration throughout the development process.[144.1] This collaborative effort is essential, as there are notable commonalities between neuroethics and AI ethics, including overlapping research domains and shared fundamental concerns.[145.1] By fostering dialogue between these fields, stakeholders can work towards creating AI-enhanced neuroscience technologies that are both ethically sound and socially responsible.[145.1] The integration of insights from computational neuroscience into artificial intelligence (AI) is a promising avenue for enhancing machine learning algorithms. Currently, the neural basis of supervised learning is not fully understood; however, it has been established that supervised learning processes can employ some of the same computational mechanisms that support unsupervised learning. Additionally, supervisory feedback can be provided to one population of neurons by the output of others, indicating a complex interplay between different learning paradigms.[28.1] Furthermore, the ability of neural networks to learn features from data is considered a key factor contributing to their superior effectiveness compared to classical machine learning models. Despite ongoing research efforts, a unified mechanism that captures the features learned across various neural architectures has yet to be identified.[152.1] This highlights the potential for further exploration of how understanding neural mechanisms can inform and improve AI methodologies.Innovations in Neuroimaging Techniques
Recent advancements in neuroimaging techniques have significantly enhanced our understanding of neural circuits and their roles in cognitive functions. New computational methods are revolutionizing the collection, reconstruction, analysis, and interpretation of neuroimaging data, thereby broadening the scope of optical technologies utilized in neuroscience. These include one-photon and multi-photon imaging of functional indicators such as calcium and voltage, which are critical for studying neuronal processes.[132.1] Current fluorescent imaging tools, including fluorescent false neurotransmitters (FFNs), quantum dots, and genetically-encoded calcium and voltage indicators, have been widely applied to investigate brain activities. For instance, dual-color calcium imaging has been employed to observe brain activities in behaving animals, utilizing a combination of R-CaMP2, a novel red fluorescent protein-derived biosensor, alongside a green calcium indicator. This approach allows for enhanced imaging and mapping of activities in deep brain areas, as the longer excitation wavelengths reduce tissue scattering, facilitating the detection of strong single action potential signals and fast kinetics in vivo.[133.1] Computational models have also become integral to human neuroimaging research, offering mechanistic insights and predictive tools for understanding human cognition and behavior. Neuroimaging has been foundational in cognitive neuroscience and mental health research, significantly advancing knowledge about the brain mechanisms underlying cognition and behavior, as well as their alterations in psychiatric and neurological disorders. Recent developments, such as invasive recordings of human brain activity and real-time recordings via wearable devices, address the limitations of traditional imaging methods by providing unprecedented access to neural data with high temporospatial resolution or more ecologically valid measurements.[134.1] Recent advancements in optogenetics have significantly enhanced our understanding of neural circuits, particularly in relation to cognition and behavior, with a specific emphasis on learning and memory. This technique provides greater precision in the manipulation of specific neurons, thereby transforming the study of these neural circuits.[135.1] Furthermore, optogenetic modulation is facilitating a deeper comprehension of the mechanistic underpinnings of various neurological and psychiatric conditions.[135.1]In this section:
Applications
Neurological Disorder Treatments
The integration of artificial intelligence (AI) techniques into the diagnosis and treatment of neurological disorders has significantly advanced the field over the past two decades. Recent research has focused on the automated diagnosis of five key neurological disorders: epilepsy, Parkinson's disease, Alzheimer's disease, multiple sclerosis, and ischemic brain stroke. These AI techniques leverage physiological signals and imaging data to improve diagnostic accuracy and facilitate early detection of these conditions.[171.1] Additionally, machine learning approaches have been employed to mine clinical and imaging data specifically for the early detection of Parkinson's disease and to predict the conversion from mild cognitive impairment to Alzheimer's disease using various socio-demographic and clinical characteristics.[173.1] This growing body of work underscores the potential of AI in enhancing the analysis of brain data, ultimately leading to better outcomes in the management of neurological disorders. Deep learning methods have also been extensively reviewed for their role in the prognosis of neuropsychiatric and neurological disorders, such as stroke, Alzheimer's, and Parkinson's disease. These methods are critical for understanding the complexities of these conditions and improving patient outcomes through more precise treatment plans.[172.1] The use of machine learning algorithms allows for the analysis of diverse patient data, including medical history, imaging results, and genetic profiles, which can identify subtle markers of neurological disorders that may be overlooked by human observation.[175.1] Magnetic Resonance Imaging (MRI) has emerged as a pivotal tool in the automatic diagnosis of neurological disorders. Its non-invasive nature and high informational yield make it a preferred modality for visualizing brain structures. The combination of advanced processing capabilities and deep learning techniques has further enhanced the effectiveness of MRI in diagnosing conditions such as Parkinson's disease.[174.1] Computational neuroscience plays a crucial role in the early diagnosis and treatment response prediction for central nervous system disorders. Innovations in this field are pivotal for devising neuromodulation strategies, effectively bridging computational neuroscience with real-world clinical applications.[194.1] However, the successful clinical development of new therapeutic interventions remains notoriously challenging, particularly in neurodegenerative diseases. This difficulty arises from the scarcity of predictive biomarkers and the fact that functional improvement is often based on patients' perceptions, which are captured through structured interviews.[195.1] As a result, mechanistic modeling of the processes relevant to therapeutic interventions in CNS disorders has become increasingly important in addressing these challenges.Brain-Machine Interfaces
Brain-machine interfaces (BMIs) represent a significant application within the field of computational neuroscience, which is a multidisciplinary domain that integrates principles and methods from mathematics, physics, computer science, and biology to understand brain function and develop models that replicate its activities.[162.1] This field studies brain function in terms of the information processing properties of the structures that constitute the nervous system.[162.1] By employing computational approaches, BMIs aim to interpret neural signals, facilitating the control of external devices through thought alone, although specific applications such as prosthetic limbs or computer cursors are not detailed in the source.[162.1] The overarching goal of computational neuroscience is to unravel the complex workings of the brain by integrating experimental data, theoretical models, and computational simulations, thereby enhancing the understanding of brain function and the development of BMIs.[162.1] The integration of computational neuroscience and neuroinformatics has facilitated significant advancements in various applications, particularly in predicting and detecting brain network organization. These fields utilize state-of-the-art tools, software, and resources to enhance our understanding of complex brain disorders and their diagnosis.[163.1] Additionally, computational models are employed to simulate and analyze cognitive behaviors, such as decision-making, memory, and learning, which are crucial for the development of artificial intelligence systems.[166.1] This approach not only aids in cognitive behavior simulation but also contributes to early diagnosis of mental health conditions by analyzing patterns in speech, facial expressions, and behavior, thereby offering a more personalized and effective mental healthcare strategy.[166.1] Brain-Machine Interfaces (BMIs) are integral to the field of computational neuroscience, which is also known as theoretical neuroscience. This discipline focuses on the application of theories related to computation and information processing to neurobiological systems.[164.1] Through the study of BMIs, researchers aim to explore the connections between neural activity and cognitive functions. The advancements in BMIs hold the potential not only to improve the quality of life for individuals with disabilities but also to enhance our understanding of the complex dynamics of the human brain.[164.1]In this section:
Challenges And Limitations
Knowledge Gaps between Neuroscience and AI
The intersection of neuroscience and artificial intelligence (AI) highlights critical knowledge gaps that impede the creation of accurate computational models. The brain's inherent complexity generates extensive data, necessitating advanced computing resources for effective storage, management, and analysis. This challenge underscores the need for innovative computational methods and algorithms to handle such vast datasets efficiently.[205.1] While computational models strive to emulate brain behavior through neural network simulations, they often depend on assumptions that can compromise accuracy. These assumptions may facilitate certain simulations but hinder others, leading to inconsistencies in understanding complex cognitive functions like working memory.[220.1] This limitation calls for models that can integrate diverse aspects of brain function without sacrificing precision. Consciousness remains a central topic in neuroscience, philosophy, and psychology, representing the ability to experience and reflect on the world.[211.1] Despite advances in brain imaging and cognitive psychology, consciousness—our subjective experience—remains elusive.[209.1] AI offers new avenues for consciousness studies, providing computational models that may illuminate the nature of awareness.[209.1] By synthesizing insights from computational modeling, neuroscience, and philosophy, researchers aim to understand and potentially replicate conscious behavior in artificial systems.[211.1] The field of computational clinical neuroscience is diverse, encompassing various models with distinct purposes and methodologies. This diversity can lead to misconceptions about the capabilities of computational models, affecting both research and public perception of neuroscience.[221.1] Addressing these misconceptions is essential for bridging the gap between theoretical models and practical applications in understanding brain disorders and cognitive functions.In this section:
Future Directions
Emerging Technologies
Emerging technologies in computational neuroscience are significantly reshaping the landscape of brain research and clinical practice. Recent advancements in artificial intelligence (AI) and machine learning models have accelerated progress in understanding how the brain learns, memorizes, and represents the external world, inspiring a new wave of research in the field.[239.1] The introduction of large-scale language models, such as ChatGPT, has notably impacted neuroscience by enhancing AI-based human behavioral simulations, standardizing neuroimaging data analysis, and validating neurotheoretical concepts. This synergy between AI and cognitive neuroscience holds the potential for breakthrough advances, although it also presents challenges, including an over-reliance on correlative data, the complexity of neural networks, ethical concerns, and the need for standardization.[240.1] Moreover, advances in neurotechnologies, such as molecular tools, neural sensors, and large-scale recording techniques, are generating vast datasets that transform neuroscience research. These developments have highlighted the challenges associated with global collaboration, data management, and the effective translation of findings into clinical applications. As a result, there is an increasing emphasis on innovative strategies to harness big data for the diagnosis and treatment of brain disorders.[241.1]Potential Impact on Cognitive Science
The integration of artificial intelligence (AI) into neuroimaging and cognitive science is poised to significantly enhance our understanding of brain function and cognitive processes. AI algorithms, particularly deep learning models, have shown remarkable capabilities in analyzing complex neuroimaging data, which leads to improved diagnostic accuracy and personalized treatment strategies for neurodegenerative diseases.[242.1] This transformative shift not only enhances disease detection but also facilitates predictive modeling and treatment planning, thereby improving patient outcomes.[243.1] Moreover, AI technologies are instrumental in early diagnosis and the development of personalized treatment plans by analyzing diverse patient data, including medical history and imaging results. This data-driven approach allows for the identification of subtle markers of neurological disorders that may be overlooked by human observation.[244.1] As a result, AI is driving significant advancements in the quality and effectiveness of treatment for neurological conditions. The field of neuroinformatics, which combines neuroscience with data science and AI, is also evolving rapidly. This interdisciplinary approach is crucial for developing comprehensive data and knowledge bases, as well as computational models that can analyze and integrate experimental data.[246.1] The increasing scale of data generated from advanced neuroimaging technologies necessitates innovative strategies for data management and collaboration, which are essential for diagnosing and treating brain disorders effectively.[248.1] Furthermore, AI's role extends beyond analysis; it serves as both an analytical tool and a model for neural activity and cognition. By comparing AI models with actual brain function, researchers can gain deeper insights into cognitive processes such as learning and memory.[250.1] Generative models, in particular, have shown promise in brain decoding tasks, further advancing our understanding of brain function and perception.[251.1]In this section:
References
[1] A Personal View of the Early Development of Computational Neuroscience ... — In the half-century since the seminal Hodgkin-Huxley papers were published, computational neuroscience has become an established discipline, evolving from computer modeling of neurons to attempts to understand the computational functions of the
[2] Encyclopedia of Computational Neuroscience | SpringerLink — The annual Computational Neuroscience Meeting (CNS) began in 1990 as a small workshop called Analysis and Modeling of Neural Systems. The goal of the workshop was to explore the boundary between neuroscience and computation. Riding on the success of several seminal papers, physicists had made "Neural Networks" fashionable, and soon the quantitative methods used in these abstract model networks
[3] Computational neuroscience - Wikipedia — In general, all models postulate the existence of a saliency or priority map for registering the potentially interesting areas of the retinal input, and a gating mechanism for reducing the amount of incoming visual information, so that the limited computational resources of the brain can handle it. An example theory that is being extensively tested behaviorally and physiologically is the V1 Saliency Hypothesis that a bottom-up saliency map is created in the primary visual cortex to guide attention exogenously. Computational neuroscience provides a mathematical framework for studying the mechanisms involved in brain function and allows complete simulation and prediction of neuropsychological syndromes.
[5] What Is Computational Neuroscience? - All About AI — Computational neuroscience is an interdisciplinary field that combines elements of neuroscience, mathematics, and computer science to understand and simulate brain functions. This article delved into its definition, examples, and use cases, particularly in AI, alongside its pros and cons.
[6] Deep Neural Networks in Computational Neuroscience — Brain-Inspired Neural Network Models Are Revolutionizing Artificial Intelligence and Exhibit Rich Potential for Computational Neuroscience. Neural network models have become a central class of models in machine learning (Figure 1).Driven to optimize task performance, researchers developed and improved model architectures, hardware, and training schemes that eventually led to today's high
[7] The promise of a model-based psychiatry: building computational models ... — An example of such an approach is the prediction of psychosis onset, in which a combination of separately aggregated clinical, neuroimaging, and neuropsychological predictors have revealed partly additive and explainable effects. 10 Therefore, it is important to carefully consider the complexity of a model and to balance interpretability and
[8] Develop and validate machine learning models to predict the risk of ... — Develop and validate machine learning models to predict the risk of depressive symptoms in older adults with cognitive impairment Machine learning models, particularly XGBoost, effectively predict depressive symptoms in cognitively impaired older adults. To address these gaps, this study aims to analyze data from National Health and Nutrition Examination Survey (NHANES) to construct a predictive model for depressive symptoms in older adults with cognitive impairment, using multiple machine learning algorithms. This study highlights the utility of machine learning models in predicting depressive symptoms among older adults with cognitive impairment, with the XGBoost model achieving the best performance. Develop and validate machine learning models to predict the risk of depressive symptoms in older adults with cognitive impairment.
[9] Analysis of Computational Neuroscience Models — Introduction. T he field of mathematical biology seeks to model biological processes using mathematical techniques and computer simulations to better explain, analyze, and visualize biological processes. Mathematical techniques often include the use of differential equations, which model events in continuous time, or difference equations, which model events in discrete time.
[10] Neuroscience, Computing, Performance, and Benchmarks: Why ... - Frontiers — Over the past decades, computing has become an integral part of neuroscience. Novel methods and tools in computational neuroscience and advances in our computational capabilities allowed the study of increasingly complex models and questions. The confluence of our ability to simulate and the availability of better experimental data recently has given rise to a number of detailed models of
[13] A new era in cognitive neuroscience: the tidal wave of artificial ... — Recently, the advent of the large-scale language model (LLM) ChatGPT has made a big impact in neuroscience, particularly in AI-based human behavioral simulations, standardized neuroimaging data analysis, and even neurotheoretical validations, fueling further interest in bridging AI and human cognition. One of the main benefits of AI in cognitive neuroscience is to develop sophisticated multivariate models for identifying neural co-activation patterns associated with cognitive activities. By quoting answers from ChatGPT, AI tells us that “the synergy between AI and cognitive neuroscience could lead to breakthrough advances in brain research and clinical practice, but has challenges to be overcome, such as overly reliance on correlative data, complexity of neural network, ethic concerns and the lack of standardization” .
[16] Editorial: Closed-loop iterations between neuroscience and artificial ... — The mutual reinforcement between neuroscience and AI should be a closed loop for linking mind and machine. Computational neuroscience (Wang et al., 2020) could bridge these two fast-developing fields through adequate models representing and simulating the brain's unique architecture and functions as shown in Figure 1. The biophysics and
[17] The Bidirectionality of Neuroscience and Artifcial Intelligence ... — Although artifcial intelligence (AI) was originally inspired by neuroscience, current AI models both resemble and difer from the human brain, and these similarities and diferences are informative to neuroscience and the continued development of AI. Importantly, AI contributes critically to neuroscience research, both as an analytical tool and as a model of neural activity and cognition. “This produces, in each domain, more capable AI systems that also turn out to be better multiscale neuroscientifc models of brain function,” DiCarlo said. Jay McClelland, Lucie Stern Professor in the Social Sciences, director of the Center for Mind, Brain, Computation and Technology at Stanford University, and consulting research scientist at DeepMind, centered his remarks on the evolution of AI and the role of machine learning in cognitive neuroscience.
[22] Mathematical Modeling in Neuroscience: Neuronal Activity and Its ... — Therefore, it is really important to employ modeling strategies which lead to simplified, computationally tractable and biologically relevant mathematical models.
[23] Foundations of computational neuroscience - ScienceDirect — Most computational neuroscientists assume that nervous systems compute and process information.We discuss foundational issues such as what we mean by 'computation' and 'information processing' in nervous systems; whether computation and information processing are matters of objective fact or of conventional, observer-dependent description; and how computational descriptions and
[24] Fundamentals of Computational Neuroscience | Oxford Academic — Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. ... It introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain
[25] Fundamentals of Computational Neuroscience (3rd Edition) — It introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain. The book covers the introduction and motivation of simplified models of neurons that are suitable for exploring information processing in large brain-like networks. ... Principles of Computational Modelling in
[28] The Computational Cognitive Neuroscience of Learning and Memory ... — The neural basis of supervised learning is less well-understood at present, but supervised learning processes can be implemented with some of the same computational mechanisms that support unsupervised learning, and moreover, supervisory feedback can be provided to one population of neurons by the output of others (Dayan & Abbott, 2001
[45] Editorial: Neuroscience, computing, performance, and benchmarks: Why it ... — At the turn of the millennium the computational neuroscience community realized that neuroscience was in a software crisis: software development was no longer progressing as expected and reproducibility declined. ... are the milestones of such projects, the authors observed that a focus on software sustainability can be an important driver for
[46] Computational and systems neuroscience: The next 20 years — This article is part of the PLOS Biology 20th Anniversary Collection.. PLOS Biology was first published in 2003. That year, important changes were afoot in the field of neuroscience. The marriage of neural recording with computational theory was just starting to bear serious fruit, prompting the inauguration of the Computational and Systems Neuroscience (Cosyne) meeting in 2004.
[57] 70 years later: The legacy of the Hodgkin & Huxley model in ... — The Hodgkin‐Huxley model of action potential generation and propagation, published in the Journal of Physiology in 1952, initiated the field of biophysically detailed computational modeling in
[58] Hodgkin-Huxley revisited: reparametrization and identifiability ... — 2. The Hodgkin-Huxley model. Hodgkin and Huxley treated the squid axon as an electrical circuit, with current across the membrane being carried by a capacitor or by one of three ionic currents: I K, the current carried by potassium ions, I Na, the current carried by sodium ions, and I l, a catch-all leakage current.Thus, the fundamental equations for simulating membrane potential changes
[59] Biophysical Journal 60 Years after Hodgkin-Huxley - PMC — Main Text. As I was putting the finishing touches on this inaugural editorial for my tenure as editor in chief of Biophysical Journal, I was saddened to learn of the passing of Sir Andrew Huxley.The transition of biology from a descriptive science to a quantitative and predictive science has been in progress for the 60 years since the Hodgkin-Huxley equation, accelerating tremendously in the
[60] The Hodgkin-Huxley Heritage: From Channels to Circuits — The Hodgkin-Huxley studies of the action potential, published 60 years ago, are a central pillar of modern neuroscience research, ranging from molecular investigations of the structural basis of ion channel function to the computational implications at circuit level. In this Symposium Review, we aim to demonstrate the ongoing impact of Hodgkin's and Huxley's ideas. The Hodgkin-Huxley model
[61] SimHH: A Versatile, Multi-GPU Simulator for Extended Hodgkin-Huxley ... — Computational neuroscience relies on complex mathematical models to simulate brain activity and decipher underlying biological processes. However, these simulations are computationally intensive, prompting the exploration of high-performance computing systems as a viable solution to enhance efficiency. In this work, we introduce SimHH, an extended-Hodgkin-Huxley simulator designed for
[62] The impact of Hodgkin-Huxley models on dendritic research — For the past seven decades, the Hodgkin-Huxley (HH) formalism has been an invaluable tool in the arsenal of neuroscientists, allowing for robust and reproducible modelling of ionic conductances and the electrophysiological phenomena they underlie. Despite its apparent age, its role as a cornerstone of computational neuroscience has not waned.
[63] Blueprint Research Tools — The NIH Blueprint for Neuroscience Research supports the development and dissemination of new research tools and resources to assist neuroscience researchers and clinicians, ... NITRC is a free one-stop-shop for science researchers that need resources such as neuroimaging analysis software, publicly available data sets, or computing power.
[64] Open Software & Tools | OS-neuroscience - OS Guidebook for Neuroscience — Containerised software is particularly useful in neuroscience research because it guarantees that processing pipelines run reliably and uniformly across different computing environments without researchers worrying about variations in software dependencies or system configurations, for example in collaborations between different institutes.
[76] PDF — The real predictive power of the model became evident when Hodgkin and Huxley demonstrated that numerical integration of these differential equations (using a hand-cranked mechanical calculator!) could accurately reproduce all the key biophysical properties of the action potential.
[77] The Hodgkin-Huxley Heritage: From Channels to Circuits - PMC — Finally, at a circuit level, the predictive success of the Hodgkin-Huxley formalism made it an exemplar of how to use data-based modeling in scientific research and paved the way for the now-thriving field of computational neuroscience.
[81] Computational neuroscience - Latest research and news | Nature — Computational neuroscience is the field of study in which mathematical tools and theories are used to investigate brain function. It can also incorporate diverse approaches from electrical
[91] Computational Neuroscience: Modeling Brain Functions in Software — Key Concepts in Computational Neuroscience. Several foundational concepts underpin the strategies utilized in computational neuroscience. Notable among these are: Neural coding: This refers to the way information is represented by neural activity. Understanding how sensory inputs are transformed into electrical signals is critical for accurate
[92] What Is Computational Neuroscience? - All About AI — What is computational neuroscience? Computational neuroscience is an interdisciplinary scientific field that employs mathematical models, theoretical analysis, and computer simulations to understand the structure, dynamics, and functioning of the nervous system and the brain, particularly in relation to cognitive and behavioral functions.
[93] NeuroBox: Computational Mathematics in Multiscale Neuroscience — Developing efficient computational mathematics in multiscale neuroscience becomes a more challenging task, involving the development of adequate multi-physics models, the reconstruction of accurate three-dimensional morphologies, stable domain and PDE discretization methods, as well as efficient numerical solvers.
[94] Exploring spiking neural networks: a comprehensive analysis of ... — This article presents a comprehensive analysis of spiking neural networks (SNNs) and their mathematical models for simulating the behavior of neurons through the generation of spikes. The study explores various models, including LIF and NLIF, for constructing SNNs and investigates their potential applications in different domains. However, implementation poses several challenges, including
[95] Computational and Mathematical Methods for Neuroscience — Theoretical neuroscience focuses on developing mathematical, computational, and statistical models to represent neural processes across multiple scales, from an individual neuron to the brain. Computational neuroscience aims to develop quantitative tools to analyze neural data and predict neural system dynamics, helping to uncover the principles that govern brain function. Computational neuroscience helps us understand brain function (e.g., sensory processing, memory, and emotions), design brain–machine interfaces (BMIs), and develop treatments for neurological diseases through predictive modeling. This Special Issue highlights the transformative role of computational and mathematical approaches in advancing neuroscience, showcasing a wide range of state-of-the-art methodologies, such as computational modeling, ML, network analysis, and BCIs, that have deepened our understanding of brain dynamics, network interactions, cognitive processes, and behavior.
[96] Empirical modeling and prediction of neuronal dynamics - PMC — Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although
[97] Communication in Neuronal Networks - PMC — The brain also exploits the adaptability of biological systems to reconfigure in response to changing needs. Neuronal networks have been extensively studied as computational systems, but they also serve as communications networks in transferring large amounts of information between brain areas.
[99] Portraits of communication in neuronal networks - PubMed — We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms.
[101] Connectivity concepts in neuronal network modeling | PLOS Computational ... — Author summary Neuronal network models are simplified and abstract representations of biological brains that allow researchers to study the influence of network connectivity on the dynamics in a controlled environment. Which neurons in a network are connected is determined by connectivity rules and even small differences between rules may lead to qualitatively different network dynamics. These
[108] The Role of Simplifying Models in Neuroscience: Modelling ... - Springer — In this article, we first describe two complementary approaches to modelling brain function, namely simplifying and realistic models. We then demonstrate, by way of two examples, the utility of building simplifying neural models. In the first example, we consider the development of neuronal positioning.
[111] Parameter estimation for connectome generative models: Accuracy ... — We demonstrate an inherent tradeoff between accuracy, reliability, and computational expense in parameter estimation and provide recommendations for leveraging this tradeoff. To enable power analyses in future studies, we empirically approximate the minimum sample size required to detect between-group differences in generative model parameters.
[113] PDF — Speed Processes such as brain development and learning occur across years or decades in humans. Unfortunately, no present technology can run large-scale simulations faster than in real time. (Typically, such models run more slowly.) We are unable to simulate the brain to the last molecular detail. But proponents of simulation hope that
[114] On the Role of Theory and Modeling in Neuroscience — Experimental data can also inform the founding assumptions (goal/constraints) of normative models. For example, when trying to explain the responses of visual neurons, we might parameterize the constraints of an efficient coding model with data from retinal photoreceptors (Field and Rieke, 2002). As with mechanistic models, these normative
[129] The coming decade of digital brain research: A vision for neuroscience ... — Abstract. In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data
[130] A new era in cognitive neuroscience: the tidal wave of artificial ... — Recently, the advent of the large-scale language model (LLM) ChatGPT has made a big impact in neuroscience, particularly in AI-based human behavioral simulations, standardized neuroimaging data analysis, and even neurotheoretical validations, fueling further interest in bridging AI and human cognition. One of the main benefits of AI in cognitive neuroscience is to develop sophisticated multivariate models for identifying neural co-activation patterns associated with cognitive activities. By quoting answers from ChatGPT, AI tells us that “the synergy between AI and cognitive neuroscience could lead to breakthrough advances in brain research and clinical practice, but has challenges to be overcome, such as overly reliance on correlative data, complexity of neural network, ethic concerns and the lack of standardization” .
[132] Special Section Guest Editorial: Computational Approaches for ... — New advances in computational methods are revolutionizing our ability to collect, reconstruct, analyze, and interpret neuroimaging data. ... These contributions are representative of the broad range of optical technologies employed in neuroscience, such as one-photon or multi-photon imaging of functional indicators, e.g., calcium, voltage, and
[133] Analytical Techniques in Neuroscience: Recent Advances in Imaging ... — Current fluorescent imaging tools, such as fluorescent false neurotransmitters (FFNs), quantum dots, ligand-based sensors, genetically-encoded Ca2+ and voltage indicators, have been widely applied to study neuronal processes. Dual-color Ca2+ imaging of brain activities in behaving animals was acquired using a combination of R-CaMP2, a new red fluorescent protein derived biosensor, with a green Ca2+ indicator.5 The red Ca2+ indicator facilitates imaging and mapping activities in deep brain areas due to the reduction of tissue scattering at longer excitation wavelengths, enabling detection and quantification of strong single action potential signals and fast kinetics in vivo. doi: 10.1017/S0033583516000081. doi: 10.1007/s10544-013-9744-1. doi: 10.1016/0006-8993(73)90503-9. doi: 10.3390/s130404811. doi: 10.1007/s12035-013-8531-6. doi: 10.1067/s0022-3476(03)00399-8.
[134] Revisiting the role of computational neuroimaging in the era of ... — Computational models have become integral to human neuroimaging research, providing both mechanistic insights and predictive tools for human cognition and behavior. Neuroimaging has been a cornerstone of human cognitive neuroscience and mental health research for decades, significantly advancing our understanding of the brain mechanisms underlying cognition, behavior, and their alterations in psychiatric and neurological disorders (e.g., ). Recent developments such as invasive recordings of human brain activity (e.g., ) and real-time and real-life recordings via wearables (e.g., ) highlight the known limitations of traditional imaging methods by providing unprecedented access to either neural data of high temporospatial resolution or more ecologically grounded measurements. In neuroscience, predictive models are used to predict behavioral outcomes, treatment response, or group memberships (e.g., patient versus no-patient) based on neuroimaging, behavioral or even genetic data.
[135] Optogenetic stimulation: Understanding memory and treating deficits ... — We review how the greater precision provided by optogenetics has transformed the study of neural circuits, in terms of cognition and behavior, with a focus on learning and memory. We also explain how optogenetic modulation is facilitating a better understanding of the mechanistic underpinnings of some neurological and psychiatric conditions.
[144] What the embedded ethics approach brings to AI-enhanced neuroscience — What the embedded ethics approach brings to AI-enhanced neuroscience - ScienceDirect Chapter Twelve - What the embedded ethics approach brings to AI-enhanced neuroscience The intersection of neuroscience and artificial intelligence (AI) promises important advances, but it also raises important ethical challenges, including data privacy, bias, accountability, and the implications of cognitive enhancement. In this chapter, we explore how the embedded ethics approach can play an important role in helping to identify and address the ethical, legal, and social issues arising from the integration of AI technology into neuroscience in a deeply collaborative and interdisciplinary manner across the entire development process. We outline important elements of the approach and use a hypothetical case study to demonstrate how embedded ethics can potentially aid in the development of more ethically and socially responsible AI-enhanced neuroscience technologies.
[145] Neuroethics and AI ethics: a proposal for collaboration — While neuroethics and AI ethics have developed independently from one another, recently there have been calls for a collaborative discussion of the issues addressed by these subfields of ethics .Footnote 1 The need for such collaboration is grounded on the recognition of significant commonalities within the fields of neuroscience and AI: specifically, overlapping domains of research and application (i.e., shared contents), common use of fundamental concepts (i.e., shared categories), and some common fundamental concerns and challenges (i.e., shared drivers and aims).
[146] Research and Development Considerations for Neuroscience and AI ... — Artifcial intelligence (AI) has the potential to be a powerful tool for good, but it also has great potential for misuse, said Bill Martin, global therapeutic area head for neuroscience for Janssen Research and Development. It is not enough to simply develop cutting-edge technologies; especially as AI reveals more about the brain, he urged researchers to consider what safeguards and ethical
[152] Mechanism for feature learning in neural networks and ... - Science — The ability of neural networks to learn features from data is thought to be a central contributor to their improved effectiveness over classical machine learning models (4, 5).Despite active research effort into neural feature learning, a unified mechanism that captures features learned across neural architectures had not been identified by prior work.
[162] Computational Neuroscience and Its Applications: A Review — Computational Neuroscience and Its Applications: A Review | SpringerLink Computational Neuroscience and Its Applications: A Review Computational neuroscience uses computational approach which is study of nervous system and branch of neuroscience. Computational neuroscience (also theoretical neuroscience) studies brain function in terms of the information processing properties of the structures that make up the nervous system. Ideas from computational neuroscience sometimes percolate into related fields such as computer vision, machine learning and artificial intelligence. Download Article/Chapter or eBook J Comput Neurosci, Springer 30:1–5. Redolfi A, McClatchey R et al (2009) Grid infrastructures for computational neuroscience: the neuGRID example. Author information Aisha Jangid, Laxmi Chaudhary & Komal Sharma Computational Neuroscience and Its Applications: A Review. In: Shorif Uddin, M., Sharma, A., Agarwal, K.L., Saraswat, M. Download Article/Chapter or eBook
[163] Computational neuroscience and neuroinformatics: Recent progress and ... — This article provides an introduction to computational neuroscience and neuroinformatics fields along with their state-ofthe- art tools, software, and resources. Furthermore, it describes a few innovative applications of these fields in predicting and detecting brain network organization, complex brain disorder diagnosis, large-scale 3D
[164] PDF — On the other hand, 'computational neuroscience' is taken to refer to what has also been called 'theoretical neuroscience'. In this sense, computational neuroscience is the application of theories relating to computation and information processing to neurobiological systems. This is the sense of computational neuroscience with which I
[166] What Is Computational Neuroscience? - All About AI — - All About AI Neural Network-Based Language Processing: AI language models, inspired by neural mechanisms of the human brain, use computational neuroscience principles. Cognitive Behavior Simulation: Researchers use computational models to simulate and analyze cognitive behaviors such as decision-making, memory, and learning in AI systems. Mental Health Diagnosis: AI systems use computational neuroscience models to analyze patterns in speech, facial expressions, and behavior for early diagnosis of mental health conditions, offering a more personalized and effective approach to mental healthcare. Misinterpretations or misuse of computational neuroscience findings could lead to inaccurate conclusions or harmful applications in AI and other fields. Computational neuroscience is pivotal in understanding and simulating brain functions, influencing AI development.
[171] Artificial Intelligence Techniques for Automated Diagnosis of ... — This paper presents a state-of-the-art review of research on automated diagnosis of 5 neurological disorders in the past 2 decades using AI techniques: epilepsy, Parkinson's disease, Alzheimer's disease, multiple sclerosis, and ischemic brain stroke using physiological signals and images. Recent res …
[172] Prevalence and Diagnosis of Neurological Disorders Using Different Deep ... — This paper dispenses an exhaustive review on deep learning techniques used in the prognosis of eight different neuropsychiatric and neurological disorders such as stroke, alzheimer, parkinson's, epilepsy, autism, migraine, cerebral palsy, and multiple sclerosis. These diseases are critical, life-thr …
[173] Applications of machine learning to diagnosis and treatment of ... — Applications of machine learning to diagnosis and treatment of neurodegenerative diseases | Nature Reviews Neurology Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson’s disease Am. J. Am. J. J. 6, 94–98 (2019). Am. J. A novel ensemble-based machine learning algorithm to predict the conversion from mild cognitive impairment to Alzheimer’s disease using socio-demographic characteristics, clinical information, and neuropsychological measures. P.N.O. and J.D.H. made a substantial contribution to discussion of article content, and reviewed and edited the manuscript before submission. A.M.B.L. and D.N. researched data for the article, and reviewed and edited the manuscript before submission. P.N.O., A.M.B.L., D.N., A.S. and J.D.H. work for BenevolentAI.
[174] Deep learning frameworks for MRI-based diagnosis of neurological ... — The automatic diagnosis of neurological disorders using Magnetic Resonance Imaging (MRI) is a widely researched problem. MRI is a non-invasive and highly informative imaging modality, which is one of the most widely accepted and used neuroimaging modalities for visualizing the human brain. The advent of tremendous processing capabilities, multi-modal data, and deep-learning techniques has
[175] Revolutionizing Neurology: The Role of Artificial Intelligence in ... — Keywords: ai algorithms, brain signals, neuroimaging, brain-computer interfaces, precision medicine, neurological disorders, artificial intelligence Integrating AI technologies into neurology has yielded many benefits, including early diagnosis, personalized treatment plans, neuroimaging analysis, treatment optimization, and groundbreaking research endeavors . AI-powered algorithms analyze diverse patient data - medical history, imaging results, genetic profiles - to identify subtle markers of neurological disorders that might evade human observation . AI can significantly enhance the quality and effectiveness of treatment plans for neurological disorders by leveraging data-driven insights and personalizing care for individual patients. From early diagnosis and personalized treatment to BCIs and drug discovery, AI drives transformative changes that enhance patient care and our understanding of neurological disorders.
[194] Editorial: New Applications of Biological and Computational Neural ... — Such innovations are pivotal for early diagnosis, treatment response prediction, and devising neuromodulation strategies, bridging computational neuroscience with real-world clinical applications.Another significant breakthrough from Dadong Luo et al. utilizes network analysis to explore the differences in the distribution of triggers among
[195] Computational neurosciences and quantitative systems pharmacology: a ... — Successful clinical development of new therapeutic interventions is notoriously difficult, especially in neurodegenerative diseases, where predictive biomarkers are scarce and functional improvement is often based on patient's perception, captured by structured interviews. As a consequence, mechanistic modeling of the processes relevant to therapeutic interventions in CNS disorders has been
[205] Addressing Large Scale Computing Challenges in Neuroscience ... - Frontiers — Neuroscience research generates vast amounts of data that require advanced computing resources for data storage, management, analysis, and simulation. Efficiently using high-performance compute architectures and processing these massive data sets pose significant challenges that require the development of new computational methods and algorithms. The use of advanced computational methods and
[209] AI and Consciousness Studies: Exploring the Mind Through Machines — Despite advances in brain imaging, cognitive psychology, and philosophy, consciousness—our subjective experience of the world—remains elusive. However, artificial intelligence (AI) has opened new frontiers in consciousness studies, offering computational models and insights that may shed light on the nature of awareness.
[211] PDF — By integrating insights from computational modeling, neuroscience, and philosophy, we propose a roadmap for comprehending and potentially realizing conscious behavior in artificial ... The concept of consciousness has been central to debates in philosophy, neuroscience, and psychology. It encapsulates the ability to experience, reflect, and
[220] The Crisis of Computational Neuroscience - Sapien Labs — Computational Models attempt to explain the behavior of the brain using neural network simulations, but assumptions that help simulate one phenomenon are destructive to the simulation of another. For example, the computational explanations of properties of working memory (e.g., Frank, Loughry & O’Reilly 2001; Grossberg & Pearson 2008) do not want to take the randomness properties from the avalanche models. For example, this would require a model within which each neuron has the type of connectivity and randomness for creating avalanches, and on top of that recurrent connectivity to store information like working memory, and lateral connectivity necessary to account for the properties of attention, and the feed-forward and feedback connectivity to explain various phenomena of object perception. Posted in The Science, CognitionTagged Mind, Brain, working memory, Perception, Computational Neuroscience, Network Models
[221] The promises and pitfalls of applying computational models to ... — But computational clinical neuroscience is by no means a homogenous field: its models differ in their intended purpose, the mathematical techniques employed, and the level of explanation they seek, ranging from mechanistic or process models of neural circuits to abstract normative models of high-level mental function. We begin by outlining what we consider to be the three most important benefits of computational models in psychiatry, neurology and, indeed, clinical neuroscience generally: (i) enforcing rigour and precision in the formalization of conceptual models; (ii) inspiring useful new conceptualizations of known phenomena and providing a principled means of synthesizing disparate pieces of evidence by helping to identify core principles of brain disorders; and (iii) offering a means of bridging the gap between different levels of explanation all the way from basic neurobiology to conscious experience of suffering.
[239] The expanding world of neuroscience - Cell Press — How the brain learns, memorizes, and establishes representations of the outside world has inspired many neuroscientists over the years. But now, transformative advances in computational neuroscience, spearheaded by artificial intelligence (AI) and machine learning models, are accelerating progress in these areas.
[240] A new era in cognitive neuroscience: the tidal wave of artificial ... — Recently, the advent of the large-scale language model (LLM) ChatGPT has made a big impact in neuroscience, particularly in AI-based human behavioral simulations, standardized neuroimaging data analysis, and even neurotheoretical validations, fueling further interest in bridging AI and human cognition. One of the main benefits of AI in cognitive neuroscience is to develop sophisticated multivariate models for identifying neural co-activation patterns associated with cognitive activities. By quoting answers from ChatGPT, AI tells us that “the synergy between AI and cognitive neuroscience could lead to breakthrough advances in brain research and clinical practice, but has challenges to be overcome, such as overly reliance on correlative data, complexity of neural network, ethic concerns and the lack of standardization” .
[241] The future of neurotechnology: From big data to translation - Cell Press — Advances in neurotechnologies, including molecular tools, neural sensors, and large-scale recording, are transforming neuroscience and generating vast datasets. A recent meeting highlighted the resulting challenges in global collaboration, data management, and effective translation, emphasizing the need for innovative strategies to harness big data for diagnosing and treating brain disorders.
[242] Artificial intelligence in neuroimaging: Opportunities and ethical ... — AI algorithms, particularly deep learning models, have demonstrated remarkable capabilities in analyzing complex neuroimaging data, leading to enhanced diagnostic accuracy and personalized treatment strategies. I am writing to address the growing role of Artificial Intelligence (AI) in the field of neuroimaging, a development that promises significant advancements in medical diagnostics and treatment planning. Recent studies have demonstrated the potential of AI techniques, such as deep learning algorithms, in analyzing complex neuroimaging data and extracting valuable insights (Borchert et al., 2023). Recent advancements in artificial intelligence (AI) have significantly transformed neuroimaging, enhancing the diagnosis, prognosis, and treatment of neurodegenerative diseases as shown in Fig. 1. Artificial Intelligence in Neuroimaging AI in neuroimaging enhances disease detection by employing deep learning algorithms to analyze MRI/CT scans, identifying conditions like tumors, strokes, and Alzheimer's disease with high accuracy.
[243] Artificial intelligence in neuroimaging: Opportunities and ethical ... — Artificial intelligence in neuroimaging: Opportunities and ethical challenges - ScienceDirect Artificial intelligence in neuroimaging: Opportunities and ethical challenges open access The integration of artificial intelligence (AI) into neuroimaging represents a transformative shift in the diagnosis and treatment of neurodegenerative diseases. This letter discusses the opportunities AI presents in neuroimaging, including improved disease detection, predictive modeling, and treatment planning. Issues such as algorithmic bias, data privacy, and the interpretability of AI-driven insights must be addressed to ensure that these technologies are used responsibly and equitably. As neuroimaging continues to evolve, a collaborative approach involving researchers, clinicians, and ethicists is essential to navigate these challenges and maximize the benefits of AI in improving patient outcomes in neurodegenerative diseases. Next article in issue No articles found. For all open access content, the relevant licensing terms apply.
[244] Revolutionizing Neurology: The Role of Artificial Intelligence in ... — Keywords: ai algorithms, brain signals, neuroimaging, brain-computer interfaces, precision medicine, neurological disorders, artificial intelligence Integrating AI technologies into neurology has yielded many benefits, including early diagnosis, personalized treatment plans, neuroimaging analysis, treatment optimization, and groundbreaking research endeavors . AI-powered algorithms analyze diverse patient data - medical history, imaging results, genetic profiles - to identify subtle markers of neurological disorders that might evade human observation . AI can significantly enhance the quality and effectiveness of treatment plans for neurological disorders by leveraging data-driven insights and personalizing care for individual patients. From early diagnosis and personalized treatment to BCIs and drug discovery, AI drives transformative changes that enhance patient care and our understanding of neurological disorders.
[246] Neuroinformatics Applications of Data Science and Artificial ... — Today, with the advent of advanced neuroimaging technologies and effective computational models, neuroscience has entered a new era where Data Science and Artificial Intelligence (AI) are beginning to play pivotal roles (Górriz et al., 2020). These capabilities are opening up new avenues for neuroinformatics – the interdisciplinary field at the intersection of neuroscience, data science, information technology, and artificial intelligence (Kasabov, 2013). This brief editorial for the Neuroinformatics Special-edition volume on “Data Science Methods and Neuroinformatics Applications” provides a broad perspective on the evolution of brain science, from phrenology to modern neuroscience and cutting-edge advances driven by recent progress in data science and artificial intelligence (AI).
[248] The future of neurotechnology: From big data to translation — A recent meeting highlighted the resulting challenges in global collaboration, data management, and effective translation, emphasizing the need for innovative strategies to harness big data for diagnosing and treating brain disorders. These include an evolving understanding of the genetic and functional classes of cell types in the brain,1 new molecular tools that allow the monitoring and control of neural activity,2 novel hardware such as large-scale neural recording probes and innovative wide-scale microscopy,3,4 as well as the ability to harness in vitro organoids and stem cells to probe development and disease.5 One commonality across these approaches is the immense scale of the data being generated, raising important questions about how to best harness these big data to develop new and applicable knowledge. Cookies are used by this site.
[250] Future views on neuroscience and AI - Cell Press — By comparing these models with actual brain function, we gain deeper insights into how the brain operates. In my work, we focus on understanding learning and memory. By comparing how AI models learn with how the brain learns, we aim to advance our comprehension of these crucial cognitive processes, pushing forward the understanding of neuroscience.
[251] Generative AI for brain image computing and brain network computing: a ... — These studies demonstrate the potential of generative models in brain decoding tasks, which can help advance our understanding of brain function and perception. The application division of generative artificial intelligence methods in the field of brain image analysis is shown in Figure 7. The existing models mentioned above are divided