Genesis Protocol: Next Generation Technology for Biological and Life Sciences (47 results)

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Paperback. Condition: new. Paperback. Unlock the secrets of the brain with "Computational Modeling of Neural Systems: Programming Biologically Realistic Simulations." This comprehensive guide immerses you in the world of neural simulations, providing you with the tools and knowledge to create biologically realistic models using Python.Key FeaturesComprehensive exploration of prominent neural models and theories.Step-by-step Python implementations for each model and concept.Covers both theoretical foundations and practical applications.Ideal for students, researchers, and professionals in computational neuroscience, AI, and machine learning.Detailed explanations of complex mathematical concepts made accessible.Book DescriptionDelve into the intricacies of neural modeling with this extensive resource, designed to equip you with the skills to simulate neural system dynamics accurately. From fundamental neuron models like Hodgkin-Huxley and FitzHugh-Nagumo to advanced topics in machine learning and Bayesian data analysis, this book spans an impressive array of computational techniques. Harness the power of Python to implement models and drive innovations at the intersection of neuroscience and technology. Elevate your understanding of neural coding, synchronization, plasticity, and more through this meticulously crafted guide.What You Will LearnDiscover the ionic mechanisms behind neuronal action potentials with Hodgkin-Huxley equations.Simplify neuronal excitability using the FitzHugh-Nagumo two-variable system.Utilize the Morris-Lecar model to capture oscillatory neural behaviors with calcium dynamics.Master the mathematical abstraction of neuronal firing via the integrate-and-fire model.Extend neuronal firing simulations with the Leaky Integrate-and-Fire model.Synthesize computational efficiency and realism using the Izhikevich neuron model.Model population dynamics with Wilson-Cowan equations for excitatory and inhibitory neurons.Apply the cable equation for dendritic voltage distribution in neurons.Integrate complex dendritic morphologies using Rall's dendritic cable model.Incorporate synaptic inputs with conductance models for realistic simulations.Implement Hebbian learning rules to model synaptic plasticity mathematically.Explore spike-timing-dependent plasticity (STDP) with temporal kernel models.Examine Bienenstock-Cooper-Munro (BCM) theory and its sliding threshold mechanism.Model synaptic facilitation and depression with dynamical systems.Analyze recurrent Hopfield networks for memory storage as attractor states.Study Boltzmann machines for energy-efficient unsupervised learning.Implement liquid state machines to harness transient dynamics for computational tasks.Utilize echo state networks for time series data processing with fixed recurrent dynamics.Apply dynamic causal modeling for neural connectivity inference using Bayesian methods.Simplify large-scale neural networks with mean-field approximation techniques.Use the Fokker-Planck equation to describe neuronal state probability densities.Model ion channel kinetics and synaptic states using Markov processes.Quantify information transmission in neural coding with information theory concepts.Decode neural signals with optimal estimation using Kalman filters.Introduce variability in neuronal responses with stochastic differential equations.Analyze synchronization phenomena in neural networks with the Kuramoto model.Explore synchronization and stability in coupled oscillator models within neural networks.Represent binary neurons using the Ising model for phase transi Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: new. Paperback. Unlock the secrets of the brain with "Computational Modeling of Neural Systems: Programming Biologically Realistic Simulations." This comprehensive guide immerses you in the world of neural simulations, providing you with the tools and knowledge to create biologically realistic models using Python.Key FeaturesComprehensive exploration of prominent neural models and theories.Step-by-step Python implementations for each model and concept.Covers both theoretical foundations and practical applications.Ideal for students, researchers, and professionals in computational neuroscience, AI, and machine learning.Detailed explanations of complex mathematical concepts made accessible.Book DescriptionDelve into the intricacies of neural modeling with this extensive resource, designed to equip you with the skills to simulate neural system dynamics accurately. From fundamental neuron models like Hodgkin-Huxley and FitzHugh-Nagumo to advanced topics in machine learning and Bayesian data analysis, this book spans an impressive array of computational techniques. Harness the power of Python to implement models and drive innovations at the intersection of neuroscience and technology. Elevate your understanding of neural coding, synchronization, plasticity, and more through this meticulously crafted guide.What You Will LearnDiscover the ionic mechanisms behind neuronal action potentials with Hodgkin-Huxley equations.Simplify neuronal excitability using the FitzHugh-Nagumo two-variable system.Utilize the Morris-Lecar model to capture oscillatory neural behaviors with calcium dynamics.Master the mathematical abstraction of neuronal firing via the integrate-and-fire model.Extend neuronal firing simulations with the Leaky Integrate-and-Fire model.Synthesize computational efficiency and realism using the Izhikevich neuron model.Model population dynamics with Wilson-Cowan equations for excitatory and inhibitory neurons.Apply the cable equation for dendritic voltage distribution in neurons.Integrate complex dendritic morphologies using Rall's dendritic cable model.Incorporate synaptic inputs with conductance models for realistic simulations.Implement Hebbian learning rules to model synaptic plasticity mathematically.Explore spike-timing-dependent plasticity (STDP) with temporal kernel models.Examine Bienenstock-Cooper-Munro (BCM) theory and its sliding threshold mechanism.Model synaptic facilitation and depression with dynamical systems.Analyze recurrent Hopfield networks for memory storage as attractor states.Study Boltzmann machines for energy-efficient unsupervised learning.Implement liquid state machines to harness transient dynamics for computational tasks.Utilize echo state networks for time series data processing with fixed recurrent dynamics.Apply dynamic causal modeling for neural connectivity inference using Bayesian methods.Simplify large-scale neural networks with mean-field approximation techniques.Use the Fokker-Planck equation to describe neuronal state probability densities.Model ion channel kinetics and synaptic states using Markov processes.Quantify information transmission in neural coding with information theory concepts.Decode neural signals with optimal estimation using Kalman filters.Introduce variability in neuronal responses with stochastic differential equations.Analyze synchronization phenomena in neural networks with the Kuramoto model.Explore synchronization and stability in coupled oscillator models within neural networks.Represent binary neurons using the Ising model for Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Taschenbuch. Condition: Neu. Neuware - Discover the cutting-edge realm of Brain-Computer Interfaces (BCIs) with this comprehensive guide that delves deep into the programming and implementation of real-time neural interaction systems. Whether you're a seasoned researcher or an enthusiastic newcomer, this book offers a treasure trove of advanced techniques designed to transform how we interact with neural data. Packed with Python code for each chapter, this resource is perfect for those ready to turn theory into practice.

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Paperback. Condition: new. Paperback. Unleash the full potential of computational techniques in genetic engineering with this definitive guide to advanced programming algorithms. This comprehensive volume traverses the forefront of bioinformatics and computational biology, offering an in-depth exploration of sophisticated algorithms that are revolutionizing genetic engineering. Designed for researchers, practitioners, and advanced students, this authoritative resource delves into a vast spectrum of topics, including: Advanced Genome Assembly Algorithms: Innovative methods for assembling complex genomes from high-throughput sequencing data.Efficient de Bruijn Graph Construction: Techniques for optimizing graph-based models to enhance genome assembly efficiency.Machine Learning Models for Variant Calling: Integrating machine learning to improve the accuracy of genetic variant detection.Synthetic Gene Circuit Design Algorithms: Computational tools for designing synthetic gene circuits with desired functionalities.Deep Learning for Functional Genomics: Applying deep learning frameworks to interpret and predict genomic functions.Optimization Algorithms in Metabolic Engineering: Enhancing production yields in metabolic pathways using advanced optimization techniques.High-Performance Computing for Genomics: Strategies for leveraging parallel and distributed computing in large-scale genomic analysis. Each chapter provides: Theoretical Foundations: A meticulous examination of the underlying principles governing each algorithm.Practical Implementations: Detailed code examples and pseudocode to facilitate hands-on application.Real-World Applications: Case studies illustrating how these algorithms solve complex problems in genetic engineering.Performance Considerations: Discussions on optimization, scalability, and computational efficiency. This indispensable reference empowers readers to: Implement Cutting-Edge Algorithms: Apply sophisticated computational methods to advance genetic engineering projects.Innovate in Research: Leverage the latest algorithms to push the boundaries of bioinformatics and computational biology.Enhance Computational Expertise: Gain deep insights into algorithm design, optimization, and implementation in the context of genetics. Elevate your expertise and stay at the forefront of genetic engineering programming with this essential resource. Whether you're a seasoned professional or an aspiring academic, this comprehensive guide will equip you with the knowledge and tools to drive innovation in this rapidly evolving field. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.