Signal Processing for Neuroscientists

  • Format
  • Bog, hardback
  • Engelsk

Beskrivelse

Signal Processing for Neuroscientists, Second Edition provides an introduction to signal processing and modeling for those with a modest understanding of algebra, trigonometry and calculus. With a robust modeling component, this book describes modeling from the fundamental level of differential equations all the way up to practical applications in neuronal modeling. It features nine new chapters and an exercise section developed by the author. Since the modeling of systems and signal analysis are closely related, integrated presentation of these topics using identical or similar mathematics presents a didactic advantage and a significant resource for neuroscientists with quantitative interest. Although each of the topics introduced could fill several volumes, this book provides a fundamental and uncluttered background for the non-specialist scientist or engineer to not only get applications started, but also evaluate more advanced literature on signal processing and modeling.

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Detaljer
  • SprogEngelsk
  • Sidetal740
  • Udgivelsesdato15-05-2018
  • ISBN139780128104828
  • Forlag Academic Press Inc
  • FormatHardback
Størrelse og vægt
  • Vægt1300 g
  • coffee cup img
    10 cm
    book img
    15,2 cm
    22,9 cm

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    Information Mobility Ar Sampling Entropy Hysteresis Ion channels Evoked Potentials Medical imaging Resonance Interpolation Nonlinear systems Dynamics Fourier series Average Chaos Expectation Ma Arma Scaling Input impedance Hum Transfer function DFT Data assimilation Biomedical Signals Ergodicity FFT Phase Portrait Signal-to-noise ratio Dynamic System Superposition Multiresolution analysis Aliasing Poisson process Runge-Kutta method Complexity Haar wavelet ICA Euler's Formula Attractor Rule» Mexican Hat Embedding Frequency Response Euler's method Fourier transform Electrocardiogram Correlation FIR Analog Filter Hopf Bifurcation Covariance matrix Taper Support Power spectrum Correlation dimension Mutual Information Radon transform Hammerstein System Eigenvalue PCA Electroencephalogram Taylor series Analog to Digital Conversion Lyapunov Exponent CFT Neural Mass Models Gillespie algorithm Volterra Series IIR Normal equations (auto)regression Stationarity Lag-Operator Characteristic Equation 2-D wavelet transform -3-dB point A posteriori estimate A priori estimate Bayes' Bayesian analysis Blending factor Capacity Dimension Continuous Wavelet Transform Derived Wiener kernel Edge Detector Even function Filter Bank Directed transfer function (DTF)Granger causality Function generator Fano Factor Forcing Term File Formats Filter characteristic Cross-correlation method Gaussian white noise (GWN)Nonhomogeneous operators Fourier Slice Theorem Daubechies wavelet Delta function Hodgkin and Huxley model Duality property Ising Spin Impulse input instantaneous phase Integrate-and-fire neuron LIF neuron Maclaurin Series Measurement chain Linear inhomogeneous equation Logistic Equation Multichannel data Nonrandom noise effects Odd Function Null cline Nyquist Frequency Partial differential equation (PDE)Phase space Periodic function Ordinary differential equation (ODE)Oscilloscope nonlinear ODE � 2-D Fourier transform QIF neuron Region of convergence (ROC)Transfer function Recursive Algorithm Reverse-correlation function Laplace transform table Spectral leakage Scaling signal Spike output Heisenberg uncertainty Scalogram Second-order Volterra system Step Response Ideal filter Spectrogram Infomax Unevenly sampled data Unit impulse response Kolmogorov entropy von Neumann criterion Wiener-Khinchin theorem Time-frequency resolution Linear homogeneous equation LNL-Cascade Morris and Lecar model Stochastic network models Neural field equation Neural population models Wilson-Cowan model periodogram Phase Spectrum Slepian sequences Spike triggered average Partial Fraction Expansion Peak detection Poisson-Wiener kernel Volterra Kernel Wiener kernel Wiener System z-Transform table time constant Probability Density Function Sinc function Wavelet Packet Transform System characterization SNIC Time-locked signals

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