Advances in Diagnostic Tools and Therapeutic Approaches for Gliomas: A Comprehensive Review

Sensors, Journal Year: 2023, Volume and Issue: 23(24), P. 9842 - 9842

Published: Dec. 15, 2023

Gliomas, a prevalent category of primary malignant brain tumors, pose formidable clinical challenges due to their invasive nature and limited treatment options. The current therapeutic landscape for gliomas is constrained by "one-size-fits-all" paradigm, significantly restricting efficacy. Despite the implementation multimodal strategies, survival rates remain disheartening. conventional approach, involving surgical resection, radiation, chemotherapy, grapples with substantial limitations, particularly in addressing gliomas. Conventional diagnostic tools, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission (PET), play pivotal roles outlining tumor characteristics. However, they face such as poor biological specificity distinguishing active regions. ongoing development tools approaches represents multifaceted promising frontier battle against this challenging tumor. aim comprehensive review address recent advances These innovations minimize invasiveness while enabling precise, targeting localized Researchers are actively developing new colorimetric techniques, electrochemical biosensors, optical coherence tomography, reflectometric interference spectroscopy, surface-enhanced Raman biosensors. regulate progression develop precise methods Recent technological advancements, coupled bioelectronic sensors, open avenues modalities, minimizing unprecedented precision. next generation strategies holds potential precision medicine, aiding early detection effective management solid tumors. offer promise adopting medicine methodologies, disease detection, improving management. This comprehensively recognizes critical role pioneering interventions, holding significant revolutionize therapeutics.

Language: Английский

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Neuromorphic computing for modeling neurological and psychiatric disorders: implications for drug development

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(12)

Published: Oct. 10, 2024

Abstract The emergence of neuromorphic computing, inspired by the structure and function human brain, presents a transformative framework for modelling neurological disorders in drug development. This article investigates implications applying computing to simulate comprehend complex neural systems affected conditions like Alzheimer’s, Parkinson’s, epilepsy, drawing from extensive literature. It explores intersection with neurology pharmaceutical development, emphasizing significance understanding processes integrating deep learning techniques. Technical considerations, such as circuits into CMOS technology employing memristive devices synaptic emulation, are discussed. review evaluates how optimizes discovery improves clinical trials precisely simulating biological systems. also examines role models comprehending disorders, facilitating targeted treatment Recent progress is highlighted, indicating potential therapeutic interventions. As advances, synergy between neuroscience holds promise revolutionizing study brain’s complexities addressing challenges.

Language: Английский

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Enhancing Assistive Technologies With Neuromorphic Computing

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 207 - 232

Published: Oct. 4, 2024

The development of intelligent neuroprosthetics, which promise to augment human brain function is vital for augmentative assistive technologies. Neuromorphic sensors and processors are particularly adept at mimicking the brain's efficient sensory processing, offering devices an advanced capability perceive interpret complex environmental stimuli. application these technologies in computer interfaces suggests a future where transformative advancements not only possible but imminent, facilitating novel methods human-computer interaction providing insights into intricate workings through AI machine learning techniques. This paper explores integration neuromorphic with brain-computer (BCIs), highlighting potential enhance revolutionize communication healthcare. However, realization computing's full within BCIs contingent upon overcoming significant technological ethical challenges.

Language: Английский

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An accurate and fast learning approach in the biologically spiking neural network

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 24, 2025

Computations adapted from the interactions of neurons in nervous system have potential to be a strong foundation for building computers with cognitive functions including decision-making, generalization, and real-time learning. In this context, proposed intelligent machine is built on mechanisms. As result, output middle layer made up population pyramidal interneurons, AMPA/GABA receptors, excitatory inhibitory neurotransmitters. The input derived retinal model. A structure appropriate biological evidence needs learn based evidence. Similar this, PSAC (Power-STDP Actor-Critic) learning algorithm was developed as new mechanism unsupervised reinforcement procedure. Four datasets MNIST, EMNIST, CIFAR10, CIFAR100 were used confirm performance compared deep spiking networks, respectively accuracies 97.7%, 97.95% (digits) 93.73% (letters), 93.6%, 75% been obtained, which shows an improvement accuracy previous networks. suggested strategy not only outperforms earlier spike-based techniques terms but also exhibits faster rate convergence throughout training phase.

Language: Английский

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A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms

Published: July 1, 2024

Neuromorphic computing, a brain inspired non-Von Neumann computing system, addresses the challenges posed by Moore’s law memory wall phenomenon. It has capability to increasingly enhance performance while maintaining power efficiency. chip architecture requirements vary depending on application and optimizing it for large-scale applications remains be challenge. chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, on-device learning. Widely used neuron models include Hodgkin-Huxley Model, Izhikevich model, integrate-and-fire model spike response model. Hardware implementation platforms of follow three approaches: analog, digital, or combination both. Each platform can implemented various topologies interconnects learning mechanism. Current neuromorphic systems typically use unsupervised timing-dependent plasticity algorithms. However, algorithms voltage-dependent synaptic have potential performance. This review summarizes specifications highlights they suitable for.

Language: Английский

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Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey

Biomimetics, Journal Year: 2024, Volume and Issue: 9(7), P. 444 - 444

Published: July 20, 2024

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements event camera technology neuromorphic offer promising opportunities overcome these limitations. Event inspired by biological vision systems capture the scenes asynchronously, consuming minimal power but with higher temporal resolution. Neuromorphic processors, are designed mimic parallel capabilities human brain, efficient computation real-time data event-based streams. This paper provides comprehensive overview research efforts integrating processors into VSLAM systems. It discusses principles behind highlighting their advantages over traditional sensing methods. Furthermore, an in-depth survey was conducted state-of-the-art approaches including feature extraction, motion estimation, map reconstruction techniques. Additionally, integration focusing synergistic benefits terms energy efficiency, robustness, performance, explored. The open questions this emerging field, such sensor calibration, fusion, algorithmic development. Finally, potential applications future directions SLAM outlined, ranging from robotics vehicles augmented reality.

Language: Английский

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Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey

Published: May 16, 2024

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements event camera technology neuromorphic offer promising opportunities overcome these limitations. Event inspired by biological vision systems capture the scenes asynchronously consuming minimal power but with higher temporal resolution. Neuromorphic processors, are designed mimic parallel capabilities human brain, efficient computation real-time data event-based streams. This paper provides comprehensive overview research efforts integrating processors into VSLAM systems. It discusses principles behind highlighting their advantages over traditional sensing methods. Furthermore, an in-depth survey was conducted state-of-the-art approaches including feature extraction, motion estimation, map reconstruction techniques. Additionally, integration focusing synergistic benefits terms energy efficiency, robustness, performance explored. The open questions this emerging field, such sensor calibration, fusion, algorithmic development. Finally, potential applications future directions SLAM outlined, ranging from robotics vehicles augmented reality.

Language: Английский

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A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms

Electronics, Journal Year: 2024, Volume and Issue: 13(15), P. 2963 - 2963

Published: July 26, 2024

Neuromorphic computing, a brain-inspired non-Von Neumann computing system, addresses the challenges posed by Moore’s law memory wall phenomenon. It has capability to enhance performance while maintaining power efficiency. chip architecture requirements vary depending on application and optimising it for large-scale applications remains challenge. chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, on-device learning. Widely used neuron models include Hodgkin–Huxley Model, Izhikevich model, integrate-and-fire spike response model. Hardware implementation platforms of follow three approaches: analogue, digital, or combination both. Each platform can be implemented various topologies interconnect learning mechanism. Current neuromorphic systems typically use unsupervised timing-dependent plasticity algorithms. However, algorithms voltage-dependent synaptic have potential performance. This review summarises specifications highlights they suitable for.

Language: Английский

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Encoding and decoding models

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 668 - 686

Published: June 19, 2024

Language: Английский

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Neuromorphic Software Tools and Development Environments

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 391 - 410

Published: Oct. 4, 2024

We are seeing a technological transformation now that was unthinkable ten years ago. Although introducing artificial intelligence (AI) in contemporary business theoretically permits unrestricted expansion, the dreaded power-wall issue parallel computing paradigm prevents us from fully using AI's potential. Because they expected to operate at extremely low power, modern Neuromorphic accelerators provide profitable substitute for conventional neural network (ANN) deep learning (DL). centred on Spiking Neural Networks (SNN), which seek mimic energy-efficient mechanism operating our brains. This chapter covers general overview of software tools and development environments, including platforms, frameworks, best practices.

Language: Английский

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