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An Introduction To The Mathematics Of Neurons /
Hoppensteadt, Frank C.
An Introduction To The Mathematics Of Neurons / Frank C. Hoppensteadt - 2nd edition - Cambridge , 1997 - 232
Signal processing aspects of neural networks are presented and studied in this book. Background material in electronic circuits, mathematical modeling and analysis, signal processing, and neurosciences is presented first, followed by three chapters of applications. Throughout, the focus is on the network's behavior near places where phase changes can occur. In mathematical terminology, this is reduction to analysis of canonical models near bifurcation points. The canonical problem derived here near a saddle-node bifurcations is the VCON model that is equivalent to an electronic circuit, similar to a phase-locked loop and it is described in the frequency domain rather than the time domain. This is an excellent text for advanced undergraduates and graduates in the physical sciences, mathematics, engineering, medicine, and life sciences
9780521599290
Mathematics
004.6 / HOP
An Introduction To The Mathematics Of Neurons / Frank C. Hoppensteadt - 2nd edition - Cambridge , 1997 - 232
Signal processing aspects of neural networks are presented and studied in this book. Background material in electronic circuits, mathematical modeling and analysis, signal processing, and neurosciences is presented first, followed by three chapters of applications. Throughout, the focus is on the network's behavior near places where phase changes can occur. In mathematical terminology, this is reduction to analysis of canonical models near bifurcation points. The canonical problem derived here near a saddle-node bifurcations is the VCON model that is equivalent to an electronic circuit, similar to a phase-locked loop and it is described in the frequency domain rather than the time domain. This is an excellent text for advanced undergraduates and graduates in the physical sciences, mathematics, engineering, medicine, and life sciences
9780521599290
Mathematics
004.6 / HOP