03584nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001400172050001600186072001700202072002300219072001600242100008900258245016500347250001800512264006900530300006700599336002600666337002600692338003600718347002400754505040200778520128901180650003202469650003902501650002602540650004302566650002902609650003202638650003902670650002502709650003302734650002802767710003402795773002602829776003602855776003602891776003602927856004602963912001403009912001403023999001703037978-3-031-75705-1DE-He21320251030114912.0cr nn 008mamaa241231s2024    sz |    s    |||| 0|eng d  a97830317570519978-3-031-75705-17 a10.1007/978-3-031-75705-12doi 4aRC321-580 4aQH324.2-.25 7aPSAN2bicssc 7aSCI0890002bisacsh 7aPSAN2thema1 aMallot, Hanspeter A.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut98995410aComputational Neuroscienceh[electronic resource] :bAn Essential Guide to Membrane Potentials, Receptive Fields, and Neural Networks /cby Hanspeter A. Mallot.  a2nd ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIII, 280 p. 109 illus., 74 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aChapter 1. Excitable Membranes and Neural Conduction -- Chapter 2. Receptive Fields and the Specificity of Neuronal Firing -- Chapter 3. Functional Models of Receptive Fields -- Chapter 4. Fourier Analysis for Neuroscientists -- Chapter 5. Artificial Neural Networks and Classification -- Chapter 6. Artificial Neural Networks With Interacting Output Units -- Chapter 7. Coding and Representation.  aThis 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. . 0aComputational neuroscience. 0aComputational intelligence.964052 0aNeurosciences.968384 0aNeural networks (Neurobiology).979376 0aNeural circuitry.98618114aComputational Neuroscience.24aComputational Intelligence.96405224aNeuroscience.96838624aSystems Neuroscience.97937724aNeural Circuits.9861822 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303175704408iPrinted edition:z978303175706808iPrinted edition:z978303175707540uhttps://doi.org/10.1007/978-3-031-75705-1  aZDB-2-SBL  aZDB-2-SXB  c66231d66231