A family of quasi-white random signals and its optimal use in biological system identification

A new family of random test signals that can be used in biological system identification is introduced. These signals approximate the statistical properties of white noise. Their use in connection with the crosscorrelation technique provides several advantages over the quasi-white test signals that have been used so far. The most important advantages are their easy generation and their effective error analysis that allows the optimal determination of the test parameters.

[1]  G. D. Mccann,et al.  Nonlinear identification theory models for successive stages of visual nervous systems of flies. , 1974, Journal of neurophysiology.

[2]  Panos Z. Marmarelis Nonlinear identification of Bioneuronal systems through white-noise stimulation , 1972 .

[3]  P Z Marmarelis,et al.  Nonlinear analysis and synthesis of receptive-field responses in the catfish retina. II. One-input white-noise analysis. , 1973, Journal of neurophysiology.

[4]  V. Honrubia,et al.  Functional and anatomical correlation of afferent responses from the isolated semicircular canal , 1974, Nature.

[5]  R. J. Hooper,et al.  Signals for transfer function measurements in non-linear systems , 1964 .

[6]  K. Naka,et al.  Identification of multi-input biological systems. , 1974, IEEE transactions on bio-medical engineering.

[7]  K. Naka,et al.  Nonlinear analysis and synthesis of receptive-field responses in the catfish retina. 3. Two-input white-noise analysis. , 1973, Journal of neurophysiology.

[8]  Roland Gemperlein,et al.  A study of the response properties of retinula cells of flies using nonlinear identification theory , 1975, Biological Cybernetics.

[9]  N. Wiener,et al.  Nonlinear Problems in Random Theory , 1964 .

[10]  H. Demuth,et al.  A Crosscorrelation Method for Measuring the Impulse Response of Reactor Systems , 1961 .

[11]  H. A. Barker,et al.  High-order autocorelation functions of pseudorandom signals based on m sequences , 1970 .

[12]  G. D. McCann,et al.  Development and application of white-noise modeling techniques for studies of insect visual nervous system , 1973, Kybernetik.

[13]  K. Naka,et al.  White-Noise Analysis of a Neuron Chain: An Application of the Wiener Theory , 1972, Science.

[14]  P Z Marmarelis,et al.  Nonlinear analysis and synthesis of receptive-field responses in the catfish retina. I. Horizontal cell leads to ganglion cell chain. , 1973, Journal of neurophysiology.

[15]  K. Naka,et al.  Morphological and functional identifications of catfish retinal neurons. III. Functional identification. , 1975, Journal of neurophysiology.

[16]  Charles Erwin Cohn,et al.  Neutron Noise, Waves and Pulse Propagation , 1968 .

[17]  Y. W. Lee,et al.  Measurement of the Wiener Kernels of a Non-linear System by Cross-correlation† , 1965 .

[18]  Kenkichi Fukurotani,et al.  A dynamic model of the receptive field of L-cells in the carp retina , 1975, Biological Cybernetics.