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C Zetzsche

Publications and source records attributed to C Zetzsche.

10 recordsLinked to original sources
PubMed2004

Catching audiovisual mice: predicting the arrival time of auditory-visual motion signals.

We investigated the extent to which auditory and visual motion signals are combined when observers are asked to predict the location of a virtually moving target. In Condition 1, the unimodal and bimodal signals were noisy, but the target object was continuously visible and audible; in Condition 2, the virtually moving object was hidden (invisible and inaudible) for a short period prior to its arrival at the target location. Our main finding was that the facilitation due to simultaneous visual and auditory input is very different for the two conditions. When the target is continuously visible and audible (Condition 1), the bimodal performance is twice as good as the unimodal performances, thus suggesting a very effective integration mechanism. On the other hand, if the object is hidden for a short period (Condition 2) and the task therefore requires the extrapolation of motion speed over a temporal and spatial period, the facilitation due to both sensory inputs is almost absent, and the bimodal performance is limited by the visual performance.

Adolescent↗
PubMed2001

Nonlinear and extra-classical receptive field properties and the statistics of natural scenes.

The neural mechanisms of early vision can be explained in terms of an information-theoretic optimization of the neural processing with respect to the statistical properties of the natural environment. Recent applications of this approach have been successful in the prediction of the linear filtering properties of ganglion cells and simple cells, but the relations between the environmental statistics and cortical nonlinearities, like those of end-stopped or complex cells, are not yet fully understood. Here we present extensions of our previous investigations of the exploitation of higher-order statistics by nonlinear neurons. We use multivariate wavelet statistics to demonstrate that a strictly linear processing would inevitably leave substantial statistical dependencies between the outputs of the units. We then consider how the basic nonlinearities of cortical neurons--gain control and ON/OFF half-wave rectification--can exploit these higher-order statistical dependencies. We first show that gain control provides an adaptation to the polar separability of the multivariate probability density function (PDF), and, together with an output nonlinearity, enables an overcomplete sparse coding. We then consider how the remaining higher-order dependencies between different units can be exploited by a combination of basic ON/OFF point nonlinearities and subsequent weighted linear combinations. We consider two statistical optimization schemes for the computation of the optimal weights: principal component analysis (PCA) and independent component analysis (ICA). Since the intermediate nonlinearities transform some of the higher-order dependencies into second-order dependencies even the basic PCA approach is able to exploit part of the redundancies. ICA ignores this second-order structure, but can exploit higher-order dependencies. Both schemes yield a variety of nonlinear units which comprise the typical nonlinear processing properties, such as end-stopping, side-stopping, complex-cell properties and extra-classical receptive field properties, but the 'ideal' complex cells seem only to occur with PCA. Thus, a combination of ON/OFF nonlinearities with an integrated PCA-ICA strategy seems necessary to exploit the statistical properties of natural images.

Algorithms↗
PubMed2000

Object and scene analysis by saccadic eye-movements: an investigation with higher-order statistics.

Based on an information theoretical approach, we investigate feature selection processes in saccadic object and scene analysis. Saccadic eye movements of human observers are recorded for a variety of natural and artificial test images. These experimental data are used for a statistical evaluation of the fixated image regions. Analysis of second-order statistics indicates that regions with higher spatial variance have a higher probability to be fixated, but no significant differences beyond these variance effects could be found at the level of power spectra. By contrast, an investigation with higher-order statistics, as reflected in the bispectral density, yielded clear structural differences between the image regions selected by saccadic eye movements as opposed to regions selected by a random process. These results indicate that nonredundant, intrinsically two-dimensional image features like curved lines and edges, occlusions, isolated spots, etc. play an important role in the saccadic selection process which must be integrated with top-down knowledge to fully predict object and scene analysis by human observers.

Adult↗
PubMed1998

Intrinsic two-dimensional features as textons.

We suggest that intrinsic two-dimensional (i2D) features, computationally defined as the outputs of nonlinear operators that model the activity of end-stopped neurons, play a role in preattentive texture discrimination. We first show that for discriminable textures with identical power spectra the predictions of traditional models depend on the type of nonlinearity and fail for energy measures. We then argue that the concept of intrinsic dimensionality, and the existence of end-stopped neurons, can help us to understand the role of the nonlinearities. Furthermore, we show examples in which models without strong i2D selectivity fail to predict the correct ranking order of perceptual segregation. Our arguments regarding the importance of i2D features resemble the arguments of Julesz and co-workers regarding textons such as terminators and crossings. However, we provide a computational framework that identifies textons with the outputs of nonlinear operators that are selective to i2D features.

Discrimination, Psychological↗
PubMed1997

Neural network models and the visual cortex: the missing link between orientation selectivity and the natural environment.

Orientation selectivity is a basic property of neurones in the visual cortex of higher vertebrates. Such neurones can be seen to act as 'feature detectors', which provide an efficient cortical representation of the outside world. More recently, the removal of correlations between the signals of cortical neurones has been suggested as suitable theoretical concept for explaining the development of receptive fields. Corresponding neural network simulations yielded oriented 'receptive field' structures resembling those observed by neurophysiologists. The findings suggest that the 'decorrelation approach' can provide a causal relationship between characteristics of the physical world and brain function. However, we were able to reveal a basic deficit of the decorrelation approach which we illustrate by the construction of two artificial 'worlds', a 'Gaussian' one and an 'orientation-only' one. We show that, according to the decorrelation approach, oriented environmental features would be neither necessary nor sufficient for the development of oriented receptive fields. Thus the link between environmental structure and cortical orientation selectivity still awaits a theoretical explanation.

Environment↗
PubMed1996

Amblyopic quasi-blindness for image structure.

Human amblyopes display reduced contrast sensitivities, suffer from perceptual distortion, and their letter acuities are worse than is predicted from grating visibility. We sought the origin of these dysfunctions by measuring normal and amblyopic sensitivities to various forms of well-defined image distortion, namely band-limited phase quantization, phase quantization with additional amplitude modulation, and grey-scale modification. Our results prove the existence of an amblyopic quasi-blindness to image structure, that cannot be explained in terms of contrast detection. We discuss these findings within the computational scheme of image decomposition into local amplitude and local phase values. they are consistent with the assumption of amblyopic eyes beings impaired in processing local phase but having the local amplitude (or "energy", possibly at reduced gain) at their disposal. Phrased in physiological terms, we propose a scheme of complex-cells-only vision in amblyopia. We also provide a demonstration of how amblyopic eyes may see the test stimuli and natural images by generating local amplitude and phase representations at limited phase resolution.

Amblyopia↗
PubMed1996

Generalization of form in visual pattern classification.

Human observers were trained to criterion in classifying compound Gabor signals with symmetry relationships, and were then tested with each of 18 blob-only versions of the learning set. Generalization to dark-only and light-only blob versions of the learning signals, as well as to dark-and-light blob versions was found to be excellent, thus implying virtually perfect generalization of the ability to classify mirror-image signals. The hypothesis that the learning signals are internally represented in terms of a 'blob code' with explicit labelling of contrast polarities was tested by predicting observed generalization behaviour in terms of various types of signal representations (pixelwise, Laplacian pyramid, curvature pyramid, ON/OFF, local maxima of Laplacian and curvature operators) and a minimum-distance rule. Most representations could explain generalization for dark-only and light-only blob patterns but not for the high-thresholded versions thereof. This led to the proposal of a structure-oriented blob-code. Whether such a code could be used in conjunction with simple classifiers or should be transformed into a propositional scheme of representation operated upon by a rule-based classification process remains an open question.

Adult↗
PubMed1995

A model of visual spatio-temporal memory: the icon revisited.

With a minimal set of assumptions resulting from considerations about the perception of temporal structure, we argue for the existence of a spatio-temporal memory established by the mapping of time into simultaneous physical properties. The important point of this model is the distinction between external, physical time and the internal representation of time. An immediate consequence of such a structure is the emergence of properties usually associated with the concept of iconic memory or informational persistence. Some of these properties may hence be regarded as epiphenomena produced by the testing of a spatio-temporal system with tachistoscopic spatial stimuli. The model can explain properties of the immediate memory span, the lack of effect of exposure duration on tachistoscopic report, the partial-report superiority, the decay of iconic memory, and effects of a backward mask. It does not only avoid the incompatibility problems of the frozen-image concept in dynamic vision, but also provides an adequate basis for the processing of time-varying scenes.

Attention↗
PubMedDate not supplied

Natural scene statistics and nonlinear neural interactions between frequency-selective mechanisms.

Linear filtering is a basic concept in neural models of early sensory information processing. In particular the visual system has been described to perform a wavelet-like multi-channel decomposition by a set of independent spatial-frequency selective filter mechanisms. Here we suggest that this principle of linear filtering deserves a critical re-evaluation. We propose that an optimal adaptation to natural scene statistics would require AND-like nonlinear interactions between the frequency-selective filter channels. We describe how this hypothesis can be tested by predicted violations of the principle of linearity that should be observable if cortical neurons would actually implement the proposed nonlinearities. We further explain why these effects might have been easily overlooked in earlier tests of the linearity of neurons in primary visual cortex.

Neurons↗