Search PubMed⌕ Search

Biomedical subjects

J D Victor

Publications and source records attributed to J D Victor.

At least 73 records · Page 4Linked to original sources
PubMed1988

Evaluation of poor performance and asymmetry in the Farnsworth-Munsell 100-hue test.

A statistical method for the analysis of errors on the Farnsworth-Munsell 100-hue test is introduced. The extent of asymmetry of errors are summarized by two indices, I1 and I2, derived from Fourier analysis of the error scores for the individual caps. The second index, I2, describes the (bipolar) color axis; the first index, I1, describes (monopolar) asymmetry of performance. The present analysis differs from previous approaches based on Fourier analysis of the errors in two ways: (1) a procedure is introduced which corrects the indices I1 and I2 for the biases that result from the segmentation of the test into four boxes; (2) statistics for the significance of I1 and I2 are derived by a Monte Carlo procedure, which properly handles the complex interdependence of individual error scores for each cap.

Color Perception Tests↗
PubMed1987

The fractal dimension of a test signal: implications for system identification procedures.

The experimental identification of a non-linear biologic transducer is often approached via consideration of its response to a stochastic test ensemble, such as Gaussian white noise (Marmarelis and Marmarelis 1978). In this approach, the input-output relationship a deterministic transducer is described by an orthogonal series of functionals. Laboratory implementation of such procedures requires the use of a particular test signal drawn from the idealized stochastic ensemble; the statistics of the particular test signal necessarily deviate from the statistics of the ensemble. The notion of a fractal dimension (specifically the capacity dimension) is a means to characterize a complex time series. It characterizes one aspect of the difference between a specific example of a test signal and the test ensemble from which it is drawn: the fractal dimension of ideal Gaussian white noise is infinite, while the fractal dimension of a particular test signal is finite. This paper shows that the fractal dimension of a test signal is a key descriptor of its departure from ideality: the fractal dimension of the test signal bounds the number of terms that can reliably be identified in the orthogonal functional series of an unknown transducer.

Mathematics↗
PubMed1987

The dynamics of the cat retinal X cell centre.

1. The dynamics of the centre mechanism of individual cat X retinal ganglion cells is investigated. The visual stimuli consist of temporal contrast modulation of stationary patterns. In order to study the response of the centre mechanism, patterns were either sine gratings of high spatial frequency or small circular spots positioned over the receptive-field centre. 2. Responses to contrast reversal are approximately linear. However, as the modulation depth of the stimulus increases, responses become more transient. Ganglion cell responses show this phenomenon at moderate contrasts (e.g. 0.1), which do not elicit discharges that approach the maximum firing rate of the ganglion cell. 3. A sequence of dynamical models are constructed from responses elicited by sum-of-sinusoids modulation of the spatial pattern. The first model is strictly linear. It consists of a series of low-pass filters and a single high-pass filter. The linear model predicts the approximate shape of the step response, but does not account for the change in shape of the response as a function of modulation depth. 4. The second model, a quasi-linear model, allows the 'linear' dynamics to vary slowly with a neural measure of contrast. The main effect of high contrast is a shorter time constant in the high-pass filter. This model accounts qualitatively for the increased transience of the response, but fails to predict the magnitude of the effect at higher modulation depths. 5. In the third model, the transfer characteristics of the centre response adjust rapidly as contrast changes. This intrinsically non-linear model provides excellent agreement with observed response to steps and more complex modulation patterns. 6. The non-linearity necessitated by a voltage-to-spikes transduction is analysed quantitatively. In most ganglion cells, a simple truncation at 0 impulses/s (and no saturation) explains the changes in apparent gain and mean firing rate that occur as modulation depth is increased. A non-linear voltage-to-spike transduction per se cannot account for the observed effect of contrast on dynamics. 7. The parameters of the dynamical model are measured for a population of twenty-seven X ganglion cells (nineteen on-centre and eight off-centre). The low-pass stage and the strength of the high-pass stage are relatively uniform across the population. The over-all gain and the dynamics of the high-pass stage vary substantially across the population, but show no consistent dependence on the on-off distinction or on retinal location. Some implications of this variability for retinal function are discussed.

Action Potentials↗
PubMed1986

Maximum-entropy approximations of stochastic nonlinear transductions: an extension of the Wiener theory.

We consider the description of a nonlinear stochastic transduction in terms of its input/output distribution. We construct a sequence of approximating maximum-entropy estimates from a finite set of input/output observations. This procedure extends the Wiener theory to the analysis of nonlinear stochastic transducers and to the analysis of transducers with multiple outputs but an inaccessible input.

Animals↗
PubMed1986

Isolation of components due to intracortical processing in the visual evoked potential.

A class of stochastic visual textures are used to analyze the components of the visual evoked potential (VEP). This procedure exploits the differential sensitivity of populations of visual neurons to aspects of contrast and pattern. A simple transformation of VEP responses elicited by these stimuli separates components that reflect complex aspects of visual processing from those that reflect elementary aspects. Simultaneous recordings of the VEP and cellular activity in the cat lateral geniculate nucleus are obtained. Responses to traditional VEP stimuli contain a mixture of intracortically generated and precortically generated components. A theoretical and experimental analysis demonstrates that the present approach cleanly separates intracortical generators of the VEP from precortical generators.

Animals↗
PubMed1985

Complex visual textures as a tool for studying the VEP.

A method of separating components of the visual evoked potential by using complex visual textures is described. Interchange of visual textures with identical power specta and third-order autocorrelations elicits a response which may be analyzed into symmetric and asymmetric components. It is shown that the asymmetric component depends on complex attributes of form. Mechanisms that generate this component must possess nonlinear interactions among multiple areas of the visual pattern. These interactions are likely to be more complex than rectification following spatial summation. It is concluded that the asymmetric component reflects intracortical, rather than precortical, processing.

Adult↗
PubMed1985

The human visual evoked potential: analysis of components due to elementary and complex aspects of form.

The visual evoked potential (VEP) elicited by alternation between isodipole visual textures may be separated into an asymmetric and a symmetric part. The asymmetric part reflects processing of complex attributes of form. The spatial and temporal dependence of this response is used to evaluate models for the generation of this response. The symmetric part appears to reflect processing of local luminance and contrast changes. The relation of these components to the VEP elicited by contrast reversal, contrast modulation, and the windmill-dartboard stimulus is discussed.

Adult↗
PubMed1981

How the contrast gain control modifies the frequency responses of cat retinal ganglion cells.

1. A model is proposed for the effect of contrast on the first-order frequency responses of cat retinal ganglion cells. The model consists of several cascaded low pass filters ('leaky integrators') followed by a single stage of negative feed-back. 2. Values of time constants and gain of the components in this model were chosen to approximate (with least-squared deviation) experimentally measured first-order frequency responses. In the experiments used for the analysis, the visual stimulus was a sine grating modulated by a sum of sinusoids. 3. For both X cells and Y cells, the over-all gain and the time constants of the cascade of low pass filters were insensitive to contrast. 4. In all cells, the gain-bandwidth product of the negative feed-back loop was markedly increased with increasing contrast. 5. The effect of stimulation in the periphery of the receptive fields on the first-order frequency response to a centrally placed spot was identical to the effect of increasing contrast in the grating experiments. In all cases, the gain-bandwidth product of the negative feed-back loop was the only model parameter affected by peripheral stimulation. 6. A similar effect of non-linear summation was investigated for two bars located in the receptive field periphery. 7. This analysis of the contrast gain control mechanism is compared with other models of retinal function.

Action Potentials↗
PubMed1980

The effect of contrast on the non-linear response of the Y cell.

1. Second-order frequency responses were obtained from cat retinal ganglion cells of the Y type. The cells were stimulated by a spatial sine grating whose contrast was modulated in time by a sum of eight sinusoids. 2. Second-order frequency responses obtained at higher contrasts have a peak amplitude at higher input temporal frequency, and phase shifts, compared to their low-contrast counterparts. 3. This change in shape of the second-order frequency response is a departure from the prediction of the linear/static non-linear/linear sandwich model of the non-linear pathway in the cat retina. The departure is analysed by means of the hypothesis that the two filters of the sandwich model are parametric in contrast. 4. Most of the change in shape of the second-order frequency response with contrast is accounted for in terms of the sandwich model by changes in the transfer characteristics of the filter preceding the static non-linearity. 5. The effect of contrast on the second-order responses of Y cells is qualitatively similar in several ways to the effect of contrast on first-order responses. This suggests that the contrast gain control mechanism acts early in the retina, before linear and non-linear pathways have diverged.

Action Potentials↗
PubMed1979

Nonlinear systems analysis: comparison of white noise and sum of sinusoids in a biological system.

The Gaussian white noise and the sum-of-sinusoids methods of systems analysis provide equivalent descriptions of nearly linear and strongly nonlinear transductions in the cat retina. Smoothness in the frequency domain is a common characteristic of biological transductions. This permits a substantial improvement in the signal-to-noise ratio by using the sum-of-sinusoids method, as is demonstrated for the transductions of the cat retina.

Animals↗
PubMed1979

Receptive field mechanisms of cat X and Y retinal ganglion cells.

We investigated receptive field properties of cat retinal ganglion cells with visual stimuli which were sinusoidal spatial gratings amplitude modulated in time by a sum of sinusoids. Neural responses were analyzed into the Fourier components at the input frequencies and the components at sum and difference frequencies. The first-order frequency response of X cells had a marked spatial phase and spatial frequency dependence which could be explained in terms of linear interactions between center and surround mechanisms in the receptive field. The second-order frequency response of X cells was much smaller than the first-order frequency response at all spatial frequencies. The spatial phase and spatial frequency dependence of the first-order frequency response in Y cells in some ways resembled that of X cells. However, the Y first-order response declined to zero at a much lower spatial frequency than in X cells. Furthermore, the second-order frequency response was larger in Y cells; the second-order frequency components became the dominant part of the response for patterns of high spatial frequency. This implies that the receptive field center and surround mechanisms are physiologically quite different in Y cells from those in X cells, and that the Y cells also receive excitatory drive from an additional nonlinear receptive field mechanism.

Action Potentials↗
PubMed1979

The nonlinear pathway of Y ganglion cells in the cat retina.

Retinal ganglion cells of the Y type in the cat retina produce two different types of response: linear and nonlinear. The nonlinear responses are generated by a separate and independent nonlinear pathway. The functional connectivity in this pathway is analyzed here by comparing the observed second-order frequency responses of Y cells with predictions of a "sandwich model" in which a static nonlinear stage is sandwiched between two linear filters. The model agrees well with the qualitative and quantitative features of the second-order responses. The prefilter in the model may well be the bipolar cells and the nonlinearity and postfilter in the model are probably associated with amacrine cells.

Action Potentials↗
PubMed1979

Nonlinear spatial summation and the contrast gain control of cat retinal ganglion cells.

1. We studied how responses to visual stimuli at spatially separated locations were combined by cat retinal ganglion cells. 2. The temporal signal which modulated the stimuli was a sum of sinusoids. Fourier analysis of the ganglion cell impulse train yielded first order responses at the modulation frequencies, and second order responses at sums and differences of the input frequencies. 3. Spatial stimuli were spots in the centre and periphery of the cell's receptive field. Four conditions of stimulation were used: centre alone, periphery alone, centre and periphery in phase, centre and periphery out of phase. 4. The effective first order response of the centre was defined as the response due to centre stimulation in the presence of periphery stimulation, but independent of the relative phases of the two regions. Likewise, the effective first order response of the periphery was defined as the response due to periphery in the presence of centre stimulation, but independent of the relative phases of the two regions. These effective responses may be calculated by addition and subtraction of the measured responses to the combined stimuli. 5. There was a consistent difference between the first order frequency kernal of the effective centre and the first order kernel of the centre alone. The amplitudes of the effective centre responses were diminished at low frequencies of modulation compared to the isolated centre responses. Also, the phase of the effective centre's response to high frequencies was advanced. Such non-linear interaction occurred in all ganglion cells, X or Y, but the effects were larger in Y cells. 6. In addition to spatially uniform stimuli in the periphery, spatial grating patterns were also used. These peripheral gratings affected the first order kernal of the centre even though the peripheral gratings produced no first order responses by themselves. 7. The temporal properties of the non-linear interaction of centre and periphery were probed by modulation in the periphery with single sinusoids. The most effective temporal frequencies for producing non-linear summation were: (a) 4-15 Hz when all the visual stimuli were spatially uniform, (b) 2-8 Hz when spatial grating patterns were used in the periphery. 8. The characteristics of non-linear spatial summation observed in these experiments are explained by the properties of the contrast gain control mechanism which we have previously postulated.

Action Potentials↗
PubMed1978

Visual discrimination of textures with identical third-order statistics.

We found a new class of two-dimensional random textures with identical third-order statistics that can be effortlessly discriminated. Discrimination is based on local "granularity" differences between these iso-trigon texture pairs. This is the more surprising since it is commonly assumed that texture granularity (grain) is determined by the power spectrum which, in turn, can be obtained from the second-order statistics. Because textures with identical third-order statistics must have identical second-order statistics (i.e., identical power spectra), visible texture granularity is not controlled by power spectra, and not even by third-order statistics.

Discrimination, Psychological↗
PubMed1978

The effect of contrast on the transfer properties of cat retinal ganglion cells.

1. Variation in stimulus contrast produces a marked effect on the dynamics of the cat retina. This contrast effect was investigated by measurement of the responses of X and Y ganglion cells. The stimuli were sine gratings or rectangular spots modulated by a temporal signal which was a sum of sinusoids. Fourier analysis of the neural response to such a stimulus allowed us to calculate first order and second order frequency kernels. 2. The first order frequency kernel of both X and Y ganglion cells became more sharply tuned at higher contrasts. The peak amplitude also shifted to higher temporal frequency at higher contrasts. Responses to low frequencies of modulation (less than 1 Hz) grew less than proportionally with contrast. However, response amplitudes at higher modulation frequencies (greater than 4 Hz) scaled approximately proportionally with contrast. Also, there was a marked phase advance in these latter components as contrast increased. 3. The contrast effect was significantly larger for Y cells than for X cells. 4. The first order frequency kernel was measured with single sine waves as well as with the sum of sinusoids as a modulation signal. The transfer function measured in this way was much less affected by increases in contrast. This implied that stimulus energy at one temporal frequency could affect the response amplitude and phase shift at another temporal frequency. 5. Direct proof was found that modulation at one frequency modifies the response at other frequencies. This was demonstrated by perturbation experiments in which the modulation stimulus was the sum of one strong perturbing sinusoid and seven weak test sinusoids. 6. The shape of the graph of the amplitude of the first order frequency kernel vs. temporal frequency did not depend on the amplitudes of the first order components, but rather on local retinal contrast. This was shown in an experiment with a sine grating placed at different positions in the visual field. The shape of the first order kernel did not vary with spatial phase, while the magnitudes of the first order responses varied greatly with spatial phase. 7. Models for the contrast gain control mechanism are considered in the Discussion.

Action Potentials↗
PubMed1977

Nonlinear analysis of cat retinal ganglion cells in the frequency domain.

We have analyzed the responses of cat retinal ganglion cells to luminosity gratings that are modulated in time by a sum of sinusoids. A judicious choice of the component temporal frequencies permits a separation of the linear and second-order nonlinear components. Y cell responses show harmonic generation and intermodulation distortion over a wide frequency range. These nonlinear components predominate over the linear components for certain types of spatial stimuli. Nonlinear components in X cells are greatly diminished in comparison. The character of the nonlinear responses provides strong constraints on prospective models for the nonlinear pathway of the Y cell.

Action Potentials↗
PubMedDate not supplied

Visual evoked potentials in dyslexics and normals: failure to find a difference in transient or steady-state responses.

We measured transient and steady-state checkerboard contrast-reversal visual evoked potentials (VEPs) in ten dyslexics, five patient controls, and 11 normals over a range of contrasts and luminances. Latency, amplitude, and phase measurements failed to distinguish the responses of dyslexics from those of normals or patient controls. Decreases in luminance or contrast resulted in an increased latency of the transient VEP in all groups, but these changes also did not distinguish the responses of dyslexics from those of the controls. Response variability was similar in dyslexics and normals, but was increased in subjects with attention deficit-hyperactivity disorder (ADHD). Performance on standardized psychometric testing did differentiate the dyslexics from controls, but did not correlate with VEP responses.

Adolescent↗