{"product_id":"computational-modeling-of-neural-systems-programming-biologically-realistic-simulations-with-python-9798346016489","title":"Computational Modeling of Neural Systems: Programming Biologically Realistic Simulations With Python","description":"\u003cp\u003e • Author(s): Jamie Flux\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Biotechnology\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eUnlock 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.\u003c\/p\u003e\u003cb\u003eKey Features\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eComprehensive exploration of prominent neural models and theories.\u003c\/li\u003e\n\u003cli\u003eStep-by-step Python implementations for each model and concept.\u003c\/li\u003e\n\u003cli\u003eCovers both theoretical foundations and practical applications.\u003c\/li\u003e\n\u003cli\u003eIdeal for students, researchers, and professionals in computational neuroscience, AI, and machine learning.\u003c\/li\u003e\n\u003cli\u003eDetailed explanations of complex mathematical concepts made accessible.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eBook Description\u003c\/b\u003e\u003cp\u003eDelve 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.\u003c\/p\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eDiscover the ionic mechanisms behind neuronal action potentials with Hodgkin-Huxley equations.\u003c\/li\u003e\n\u003cli\u003eSimplify neuronal excitability using the FitzHugh-Nagumo two-variable system.\u003c\/li\u003e\n\u003cli\u003eUtilize the Morris-Lecar model to capture oscillatory neural behaviors with calcium dynamics.\u003c\/li\u003e\n\u003cli\u003eMaster the mathematical abstraction of neuronal firing via the integrate-and-fire model.\u003c\/li\u003e\n\u003cli\u003eExtend neuronal firing simulations with the Leaky Integrate-and-Fire model.\u003c\/li\u003e\n\u003cli\u003eSynthesize computational efficiency and realism using the Izhikevich neuron model.\u003c\/li\u003e\n\u003cli\u003eModel population dynamics with Wilson-Cowan equations for excitatory and inhibitory neurons.\u003c\/li\u003e\n\u003cli\u003eApply the cable equation for dendritic voltage distribution in neurons.\u003c\/li\u003e\n\u003cli\u003eIntegrate complex dendritic morphologies using Rall's dendritic cable model.\u003c\/li\u003e\n\u003cli\u003eIncorporate synaptic inputs with conductance models for realistic simulations.\u003c\/li\u003e\n\u003cli\u003eImplement Hebbian learning rules to model synaptic plasticity mathematically.\u003c\/li\u003e\n\u003cli\u003eExplore spike-timing-dependent plasticity (STDP) with temporal kernel models.\u003c\/li\u003e\n\u003cli\u003eExamine Bienenstock-Cooper-Munro (BCM) theory and its sliding threshold mechanism.\u003c\/li\u003e\n\u003cli\u003eModel synaptic facilitation and depression with dynamical systems.\u003c\/li\u003e\n\u003cli\u003eAnalyze recurrent Hopfield networks for memory storage as attractor states.\u003c\/li\u003e\n\u003cli\u003eStudy Boltzmann machines for energy-efficient unsupervised learning.\u003c\/li\u003e\n\u003cli\u003eImplement liquid state machines to harness transient dynamics for computational tasks.\u003c\/li\u003e\n\u003cli\u003eUtilize echo state networks for time series data processing with fixed recurrent dynamics.\u003c\/li\u003e\n\u003cli\u003eApply dynamic causal modeling for neural connectivity inference using Bayesian methods.\u003c\/li\u003e\n\u003cli\u003eSimplify large-scale neural networks with mean-field approximation techniques.\u003c\/li\u003e\n\u003cli\u003eUse the Fokker-Planck equation to describe neuronal state probability densities.\u003c\/li\u003e\n\u003cli\u003eModel ion channel kinetics and synaptic states using Markov processes.\u003c\/li\u003e\n\u003cli\u003eQuantify information transmission in neural coding with information theory concepts.\u003c\/li\u003e\n\u003cli\u003eDecode neural signals with optimal estimation using Kalman filters.\u003c\/li\u003e\n\u003cli\u003eIntroduce variability in neuronal responses with stochastic differential equations.\u003c\/li\u003e\n\u003cli\u003eAnalyze synchronization phenomena in neural networks with the Kuramoto model.\u003c\/li\u003e\n\u003cli\u003eExplore synchronization and stability in coupled oscillator models within neural networks.\u003c\/li\u003e\n\u003cli\u003eRepresent binary neurons using the Ising model for phase transitions and system dynamics.\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":45559708156055,"sku":"9798346016489","price":3508.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798346016489.webp?v=1768595787","url":"https:\/\/atlanticbooks.com\/products\/computational-modeling-of-neural-systems-programming-biologically-realistic-simulations-with-python-9798346016489","provider":"Atlantic Books","version":"1.0","type":"link"}