Mallot H. Computational Neuroscience. An Essential Guide...2ed 2025

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Mallot H. Computational Neuroscience. An Essential Guide...2ed 2025

Textbook in PDF format This book provides an essential introduction to modeling the nervous system at various levels. Readers will learn about the intricate mechanisms of neural activity, receptive fields, neural networks, and information coding. The chapters cover topics such as membrane potentials, the Hodgkin-Huxley theory, receptive fields and their specificity for important stimulus dimensions, Fourier analysis for neuroscientists, pattern recognition and self-organization in neural networks, and the structure of neural representations. The second edition includes revised text and figures for improved readability and completeness. Key points are highlighted throughout to help readers keep track of central ideas. Researchers in the field of neuroscience with backgrounds in biology, psychology, or medicine will find this book particularly beneficial. It is also an invaluable reference for all neuroscientists who use computational methods in their daily work. Whether you are a theoretical scientist approaching the field or an experienced practitioner seeking to deepen your understanding, "Computational Neuroscience — An Essential Guide to Membrane Potentials, Receptive Fields, and Neural Networks" offers a comprehensive guide to mastering the fundamentals of this dynamic discipline. Excitable Membranes and Neural Conduction Membrane Potentials The Hodgkin-Huxley Theory Approximations Passive Conduction Propagating Action Potentials Summary and Further Reading References Receptive Fields and the Specificity of Neuronal Firing Specificity and Reverse Correlation Linear Shift-Invariant (LSI) Systems Nonlinearities in Receptive Fields Summary and Further Reading References Functional Models of Receptive Fields Retinal Ganglion Cells: Isotropic Center-Surround Organization Primary Visual Cortex: Edge Orientation Simple and Complex Cells: The “Energy” Model Motion Detection Summary and Further Reading References Fourier Analysis for Neuroscientists Examples Why Are Sinusoidals Special? Fourier Decomposition The Convolution Theorem Factson Fourier Transforms Summary and Further Reading References Artificial Neural Networks and Classification Elements of Neural Networks Classification Supervised Learning and Error Minimization The Perceptron and the Brain Summary and Further Reading References Artificial Neural Networks with Interacting Output Units Tasks of Neural Information Processing Associative Memory Self-Organization and Competitive Learning Sparse Coding Continuous-Field Attractor Summary and Further Reading References Coding and Representation Specificity Revisited Population Code Topological Maps Summary and Further Reading References Index