SYSTEMS IDENTIFICATION USING PSEUDORANDOM NOISE APPLIED TO A SENSORINEURAL SYSTEM.

Wiener kernel analysis may be used to study the ear and the periphery of the auditory nervous system when pseudorandom noise is used as the input. In this case the system is assumed to consist of a linear filter and a nonmemory nonlinearity. The degree of nonlinearity may be determined, and information obtained about the type of nonlinearity using pseudorandom noise based on ternary m-sequences by modeling the linear portion of the system and utilizing the inverse-repeat feature of the noise. The effect on the results of anomalies in the noise is also discussed. In sensorineural systems, such as those for hearing, vision and somatosensation, signals are processed in one way or another before the information they contain reaches the portion of the brain in which it is interpreted and is either put in memory or results in motor activity of one sort or another. These systems can be viewed as multichannel systems, in which cascades of boxes, each acting as a complex filter, represent, respectively, transmission of the sensory stimulus to the receptor, transduction in the sensory transducer and transformation of the neural code in the chain of nuclei that constitutes the ascending neural pathway. The individual channels consist of nerve fibers connecting arrays of receptors to arrays of nerve cells. In addition, neural circuitry makes interaction between channels possible. The result is that sensory signals are modified in one way or another as they are .passed from the periphery to higher brain centers. The stimuli which activate hearing (sounds) and the somatosensory system (vibrations) are time- varying signals, and the processing that occurs in these systems operates on the time pattern of the input signal. The electrical activity that carries the information in the nervous system is in the form of neural discharges. Responses from single nerve cells or nerve fibers are studied by placing fine electrodes (microelectrodes) near the nerve fibers or nerve cells or inside nerve cells. Nerve impulses can be treated as point processes since the shape of each individual nerve impulse is unimportant; only the temporal pattern of these nerve impulses is important in the transmission of information. Recordings of nerve impulses from a single neural element may show in great detail how particular nerve cells respond to different types of stimulation and how the neural discharges are correlated with the time pattern of the stimulus. The neural discharge pattern has been analyzed using statistical signal analysis methods, and described, using a variety of approaches, by many investigators (for review see (1)). The summed responses of large groups of neurons, which can be recorded by placing larger electrodes on the various nerve tracts or nuclei, also provide important information about the function of the nervous system. These potentials are known as gross potentials or evoked potentials and are fundamentally different from the activity which is recorded from single nerve cells using microelectrodes. Statistical signal analysis methods are used infrequently in the analysis of evoked potentials from groups of neurons, but mathematical methods of various sorts (correlation analysis, spectral analysis, etc.) have been used for a long time in the analysis of the spontaneous ongoing activity of the brain that is known as the electroencephalogram (EEG). This paper presents methods of studying the auditory system by using noise as the input signal, both in connection with recording of the electrical activity from single nerve fibers and recording with larger electrodes from nerves and nuclei. In the auditory system the stimulus (sound) is transmitted from the outer ear, through the ear canal and middle ear, to the cochlea. The sensory receptors are the hair cells located in the cochlea, and neural transduction occurs in the fibers of the cochlear nerve. The neural signals from the cochlea are transformed in the :~y',ia~ ~ :6A--