The White-Noise Method in System Identification

Marmarelis, Panos Z.; Marmarelis, Vasilis Z.

doi:10.1007/978-1-4613-3970-0_4

Cited by 93 publications

(113 citation statements)

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“…Next, the ensemble averages of input power spectral density [SXX ( f )], output power spectral density [SYY ( f )], and cross spectral density between the input and output [SYX ( f )] were calculated over the 8 segments. Finally, the transfer function from input to output [H ( f )] was calculated as: 21 To quantify the linear dependence between the input and output signals in the frequency domain, a magnitude-squared coherence function [Coh ( f )] was also calculated as: 21 In Protocol 4, the performance of the feedback controller was evaluated by the time required for the AP response to reach 90% of the target AP decrease and by the standard deviation of the steady-state error between the target and measured AP values during the last 5 min of the 10-min feedback control. These 2 values were calculated based on the 2-s moving averaged data of AP.…”

Section: Discussionmentioning

confidence: 99%

Servo-Controlled Hind-Limb Electrical Stimulation for Short-Term Arterial Pressure Control

Kawada¹,

Shimizu²,

Yamamoto

et al. 2009

Circ J

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Section: Discussionmentioning

confidence: 99%

Servo-Controlled Hind-Limb Electrical Stimulation for Short-Term Arterial Pressure Control

Kawada¹,

Shimizu²,

Yamamoto

et al. 2009

Circ J

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“…Inspired by signal processing theory, the pioneering studies of Bryant and Segundo (1976) and of Marmarelis and Marmarelis (1978) used white-noise stimulation instead of step currents. However, if the aim is to drive a neuron with a stimulus that resembles as much as possible the input it would receive an in vivo situation, a white-noise stimulus is not sufficient.…”

Section: What Is a Good Stimulus To Probe Neurons?mentioning

confidence: 99%

Can We Predict Every Spike?

Naud¹,

Gerstner²

2013

Frontiers in Neuroscience

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Is it possible to predict the spike times of a neuron with millisecond precision? In the classical picture of rate coding (Adrian, 1928), single spikes do not play a role, and the question would have to be answered negatively. For rate coding in a singleneuron, the relevant quantity to encode a stimulus such as pressure onto a touch sensor in the skin (Adrian, 1928) or presence of a light bar in the receptive field of a visual neuron (Hubel and Wiesel, 1959) is the number of spikes a neuron emits in a short time window of, e.g., 100ms. The timing of the spikes is considered as irrelevant. However, over the last 20 years many researchers have shown that it is not only the temporally averaged firing rate that carries information about the stimulus, but also the exact timing of spikes. For example, spike timing has shown to be relevant to encode force amplitude and direction in touch sensors of the skin (Johansson and Birznieks, 2004) as well as the whole-field visual movements (Bialek et al., 1991) or object movement (Gollisch and Meister, 2008) in visual neurons.If spike timing is important, a whole series of questions arises: What is the precision of spike timing if the same stimulus is repeated several times? Do spikes always appear at the same time? What would be a sensible measure of spike timing precision and reliability? Can a neuron model match the spike timing precision of a real neuron? Does it matter which neuron or what stimulus we take? If so, what would be a useful stimulus?To answer these related questions, let us think of the following experimental protocol. An experimentalist injects a time-dependent input of, say, 20 second duration into a single neuron. The neuron responds with spikes. The experimentalist now repeats the same stimulus sequence several times. At each repetition, the neuron responds with a spike train that may or may not look similar to the previous one: some spikes appear at exactly the same time during the stimulus sequence, some are missing, some are shifted by a few millisecond or appear at a completely different time. The information derived from this type of experiment which dates back to Bryant and Segundo (1976) and has been popularized by should be sufficient to answer questions regarding precision and reliability of spike timing.

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“…White noise signals are commonly used in both linear and nonlinear system identification of physiological systems [3], [4]. By considering the brain in simplified form as a linear system, with isolated events as input and EEG as output, the average event related potentials (ERPs) can be said to approximate the system's time-domain impulse Foxe@NKI.RFMH.ORG response functions, which is the linear part of the eventrelated dynamics.…”

Section: Introductionmentioning

confidence: 99%

A Spectrum of Colors: Investigating the Temporal Frequency Characteristics of the Human Visual System Using a System Identification Approach

Lalor

Reilly

Pearlmutter

et al. 2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Noise input signals are commonly used in both linear and nonlinear system identification of physiological systems. This method can be applied to electrophysiological analysis of the human visual system by controlling the modulation of the contrast of a checkerboard stimulus using a pre-computed noise waveform. In this study we describe how one can obtain an estimate of the linear impulse response of the visual system using noise waveforms. Furthermore, we examine the impulse responses obtained using noise signals with different frequency characteristics, in an attempt to investigate the temporal frequency characteristics of the human visual system. We show that noise signals with frequency content greater than 15 Hz are more effective at evoking these responses than those with little or no power at high frequencies.

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The White-Noise Method in System Identification

Cited by 93 publications

References 0 publications

Servo-Controlled Hind-Limb Electrical Stimulation for Short-Term Arterial Pressure Control

Servo-Controlled Hind-Limb Electrical Stimulation for Short-Term Arterial Pressure Control

Can We Predict Every Spike?

A Spectrum of Colors: Investigating the Temporal Frequency Characteristics of the Human Visual System Using a System Identification Approach

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