White-noise analysis in neurophysiology

Sakai, Hiroko

doi:10.1152/physrev.1992.72.2.491

Cited by 70 publications

(79 citation statements)

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“…8A, inset) and past modulation filters is approximately exponential. It is interesting to note that the opposite character of present and past amplitude sensitivities is similar to the biphasic form of linear filters in other systems (Sakai, 1992).…”

Section: Linear Filter Analysis Of Electroreceptor Afferent Encodingmentioning

confidence: 80%

“…For input-output relationships that are approximately linear, only the first two terms in this expansion, the zeroth order (mean) and the first order (linear filter) terms, are needed to obtain useful predictions of the output of the system. Such linear filter models, either alone or in combination with a static nonlinearity (Hunter and Korenberg, 1986;Chichilnisky, 2001), have been widely deployed to model the instantaneous firing rate of neurons as a function of their recent history of stimulus input (Sakai, 1992).…”

Section: Linear Filter Analysis Of Electroreceptor Afferent Encodingmentioning

confidence: 99%

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Effects of Sensing Behavior on a Latency Code

Sawtell¹,

Williams²,

Roberts³

et al. 2006

J. Neurosci.

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Sensory information is often acquired through active exploration, yet relatively little is known about how neurons encode sensory stimuli in the context of natural patterns of sensing behavior. We examined the effects of sensing behavior on a spike latency code in the active electrosensory system of mormyrid fish. These fish actively probe their environment by emitting brief electric organ discharge (EOD) pulses. Nearby objects alter the spatial pattern of current flowing through the skin. These changes are encoded by small shifts in the latency of individual electroreceptor afferent spikes after the EOD. In nature, the temporal pattern of EOD intervals is highly structured and varies depending on the behavioral context. We performed experiments in which we varied both the EOD amplitude and the intervals between EODs to understand how sensing behavior affects afferent latency coding. We use white-noise stimuli and linear filter estimation methods to develop simple models characterizing the dependence of afferent spike latency on the preceding sequence of EOD intervals and amplitudes. Comparing the predictions of these models with actual afferent responses for natural patterns of EOD intervals and amplitudes reveals an unexpectedly rich interplay between sensing behavior and stimulus encoding. Implications of our results for how afferent spike latency is decoded at central stages of electrosensory processing are discussed.

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Section: Linear Filter Analysis Of Electroreceptor Afferent Encodingmentioning

confidence: 80%

Section: Linear Filter Analysis Of Electroreceptor Afferent Encodingmentioning

confidence: 99%

Effects of Sensing Behavior on a Latency Code

Sawtell¹,

Williams²,

Roberts³

et al. 2006

J. Neurosci.

View full text Add to dashboard Cite

show abstract

“…3B). We estimated the linear filter by computing the average stimulus that preceded a spike (Hunter and Korenberg, 1986;Sakai, 1992). We estimated the static nonlinearity by convolving the stimulus with the estimated filter and comparing the output of the filter with the observed firing rate (Chichilnisky, 2001) (Fig.…”

Section: Resultsmentioning

confidence: 99%

The Statistical Computation Underlying Contrast Gain Control

Bonin

Mante

Carandini

2006

Journal of Neuroscience

107

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In the early visual system, a contrast gain control mechanism sets the gain of responses based on the locally prevalent contrast. The measure of contrast used by this adaptation mechanism is commonly assumed to be the standard deviation of light intensities relative to the mean (root-mean-square contrast). A number of alternatives, however, are possible. For example, the measure of contrast might depend on the absolute deviations relative to the mean, or on the prevalence of the darkest or lightest intensities. We investigated the statistical computation underlying this measure of contrast in the cat's lateral geniculate nucleus, which relays signals from retina to cortex. Borrowing a method from psychophysics, we recorded responses to white noise stimuli whose distribution of intensities was precisely varied. We varied the standard deviation, skewness, and kurtosis of the distribution of intensities while keeping the mean luminance constant. We found that gain strongly depends on the standard deviation of the distribution. At constant standard deviation, moreover, gain is invariant to changes in skewness or kurtosis. These findings held for both ON and OFF cells, indicating that the measure of contrast is independent of the range of stimulus intensities signaled by the cells. These results confirm the long-held assumption that contrast gain control computes root-mean-square contrast. They also show that contrast gain control senses the full distribution of intensities and leaves unvaried the relative responses of the different cell types. The advantages to visual processing of this remarkably specific computation are not entirely known.

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“…One dynamic image used in these studies is a "white noise" image, described in [10] and [11]. Based on the white noise analysis technique, the lightsensitive region (the receptive field, RF) associated with each neuron can be measured.…”

Section: Some First Results On Retinal Organizationmentioning

confidence: 99%

What does the eye tell the brain?: Development of a system for the large scale recording of retinal output activity

Litke¹,

Bezayiff²,

Chichilnisky³

et al. 2003

2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515)

141

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Abstract-A multielectrode array system has been developed to study how the retina processes and encodes visual images. This system can simultaneously record the extracellular electrical activity from hundreds of retinal output neurons as a dynamic visual image is focused on the input neurons. The retinal output signals detected can be correlated with the visual input to study the neural code used by the eye to send information about the visual world to the brain. The system consists of the following components: 1) a 32 16 rectangular array of 512 planar microelectrodes with a sensitive area of 1.7 mm 2 ; the electrode spacing is 60 m and the electrode diameter is 5 m (hexagonal arrays with 519 electrodes are under development); 2) eight 64-channel custom-designed integrated circuits to platinize the electrodes and ac couple the signals; 3) eight 64-channel integrated circuits to amplify, band-pass filter, and analog multiplex the signals; 4) a data acquisition system; and 5) data processing software. This paper will describe the design of the system, the experimental and data analysis techniques, and some first results with live retina. The system is based on techniques and expertise acquired in the development of silicon microstrip detectors for high-energy physics experiments.

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White-noise analysis in neurophysiology

Cited by 70 publications

References 0 publications

Effects of Sensing Behavior on a Latency Code

Effects of Sensing Behavior on a Latency Code

The Statistical Computation Underlying Contrast Gain Control

What does the eye tell the brain?: Development of a system for the large scale recording of retinal output activity

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