{"product_id":"modeling-neural-circuits-made-simple-with-python-isbn-9780262548083","title":"Modeling Neural Circuits Made Simple with Python","description":"\u003cb\u003eAn accessible undergraduate textbook in computational neuroscience that provides an introduction to the mathematical and computational modeling of neurons and networks of neurons.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eUnderstanding the brain is a major frontier of modern science. Given the complexity of neural circuits, advancing that understanding requires mathematical and computational approaches. This accessible undergraduate textbook in computational neuroscience provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Starting with the biophysics of single neurons, Robert Rosenbaum incrementally builds to explanations of neural coding, learning, and the relationship between biological and artificial neural networks. Examples with real neural data demonstrate how computational models can be used to understand phenomena observed in neural recordings. Based on years of classroom experience, the material has been carefully streamlined to provide all the content needed to build a foundation for modeling neural circuits in a one-semester course.\u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eProven in the classroom\u003c\/li\u003e\n\u003cli\u003eExample-rich, student-friendly approach\u003c\/li\u003e\n\u003cli\u003eIncludes Python code and a mathematical appendix reviewing the requisite background in calculus, linear algebra, and probability\u003c\/li\u003e\n\u003cli\u003eIdeal for engineering, science, and mathematics majors and for self-study\u003c\/li\u003e\n\u003c\/ul\u003eContents\u003cbr\u003e List of Figures  ix\u003cbr\u003e Preface  xi\u003cbr\u003e\u003cbr\u003e 1    Modeling Single Neurons  1\u003cbr\u003e 1.1    The Leaky Integrator Model  1\u003cbr\u003e 1.2    The EIF Model  5\u003cbr\u003e 1.3    Modeling Synapses  10\u003cbr\u003e\u003cbr\u003e 2    Measuring and Modeling Neural Variability  15\u003cbr\u003e 2.1    Spike Train Variability, Firing Rates, and Tuning  15\u003cbr\u003e 2.2    Modeling Spike Train Variability with Poisson Processes  21\u003cbr\u003e 2.3    Modeling a Neuron with Noisy Synaptic Input  25\u003cbr\u003e\u003cbr\u003e3    Modeling Networks of Neurons  33\u003cbr\u003e 3.1    Feedforward Spiking Networks and Their Mean-Field Approximation  33\u003cbr\u003e 3.2    Recurrent Spiking Networks and Their Mean-Field Approximation  37\u003cbr\u003e 3.3    Modeling Surround Suppression with Rate Network Models  43\u003cbr\u003e\u003cbr\u003e 4    Modeling Plasticity and Learning  49\u003cbr\u003e 4.1    Synaptic Plasticity  49\u003cbr\u003e 4.2    Feedforward Artificial Neural Networks  54\u003cbr\u003e\u003cbr\u003eAppendix A: Mathematical Background  61\u003cbr\u003e A.1   Introduction to ODEs  61\u003cbr\u003e A.2   Exponential Decay as a Linear, Autonomous ODE  63\u003cbr\u003e A.3   Convolutions  65\u003cbr\u003e A.4   One-Dimensional Linear ODEs with Time-Dependent Forcing  69\u003cbr\u003e A.5   The Forward Euler Method  71\u003cbr\u003e A.6   Fixed Points, Stability, and Bifurcations in One-Dimensional ODEs  74\u003cbr\u003e A.7   Dirac Delta Functions  78\u003cbr\u003e A.8   Fixed Points, Stability, and Bifurcations in Systems of ODEs  81\u003cbr\u003e\u003cbr\u003eAppendix B: Additional Models and Concepts  89\u003cbr\u003e B.1    Ion Channel Currents and the HH Model  89\u003cbr\u003e B.2    Other Simplified Models of Single Neurons  97\u003cbr\u003e B.3    Conductance-Based Synapse Models  113\u003cbr\u003e\u003cbr\u003e B.4    Neural Coding  115\u003cbr\u003e B.5    Derivations and Alternative Formulations of Rate Network Models  124\u003cbr\u003e B.6    Hopfield Networks  127\u003cbr\u003e B.7    Training Readouts from Chaotic RNNs  131\u003cbr\u003e B.8    DNNs and Backpropagation  136\u003cbr\u003e\u003cbr\u003e References  141\u003cbr\u003e Index  147Robert Rosenbaum is Associate Professor of Applied and Computational Mathematics and Statistics at the University of Notre Dame. His research in computational neuroscience is focused on using computational models of neural circuits to help understand the dynamics and statistics of neural activity underlying sensory processing and learning.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46304441270501,"sku":"NP9780262548083","price":45.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262548083.jpg?v=1767732856","url":"https:\/\/k12savings.com\/products\/modeling-neural-circuits-made-simple-with-python-isbn-9780262548083","provider":"K12savings","version":"1.0","type":"link"}