Titlebook: Analysis of Physiological Systems; The White-Noise Appr Panos Z. Marmarelis,Vasilis Z. Marmarelis Book 19781st edition Springer Science+Bus

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The White-Noise Method in System Identification,onlinear. In much the same way that nonlinearities optimize the design of artificial systems, nonlinearities seem to be necessary for the optimal functioning of physiological systems from the behavioral point of view. There are many such examples: the logarithmic transformation of sensory input in o

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Methods of Computation of System Kernels,n this chapter we examine some of the computational considerations and present techniques that can be employed in estimating kernels. In this whole discussion, the crosscorrelation approach to kernel estimation is used, for reasons concerning the theoretical advantages of this approach (cf. Sec. 4.3

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Tests and Analyses Preliminary to Identification Experiment,count the characteristics of the system and the experimental limitations inherent in its study by stimulus—response experiments. This is especially important in the study of such physiological systems for which the measurement and even the choice of input and output is a difficult task (e.g., neural

itinerary

Peeking into the Black Box,on concerns the . of the system. That is, how is . realized by the different interacting components of the system? How do changes in the characteristics of a particular component affect the system function .? Paradoxically, subanalysis of the system structure often becomes possible if the system is

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Book 19781st editionask is usually carried out through the performance of stimulus-response experiments. In the past, the design of such experiments has been ad hoc, incomplete, and certainly inefficient. Worse yet, bioscientists have failed to take advantage of advances in fields directly related to their problems (sp

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ps. This task is usually carried out through the performance of stimulus-response experiments. In the past, the design of such experiments has been ad hoc, incomplete, and certainly inefficient. Worse yet, bioscientists have failed to take advantage of advances in fields directly related to their pr

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