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Band-spectrum noise has been shown to suppress the visual perception of printed letters. The suppression exhibits a specific dependence on the spatial frequency of the noise, and the frequency domain of most effective inhibition has been related to the size of the letters. In this paper, we address two important questions that were left open by previous studies: (1) Is the observed effect specific to text, and which parameters determine the domain of most effective suppression? (2) What is the origin of the effect in terms of underlying neural processes? We conduct a series of psychophysical experiments that demonstrate that the frequency domain of most effective inhibition depends on the stroke width of the letter rather than on the letter size. These experiments also demonstrate that the effect is not specific to the recognition of letters but also applies to other objects and even to single bars. We attribute the observed effect to nonclassical receptive field (non-CRF) inhibition in visual area V1. This mechanism has previously been suggested as the possible origin of various other perceptual effects. We introduce computational models of two types of cell that incorporate non-CRF inhibition, which are based on Gabor energy filters extended by surround suppression of two kinds: isotropic and anisotropic. The computational models confirm previous qualitative explanations of perceptual effects, such as orientation contrast pop-out, reduced saliency of lines embedded in gratings, and reduced saliency of contours surrounded by textures. We apply the computational models to the images used in the psychophysical experiments. The computational results show a dependence of the inhibition effect on the spatial frequency of the noise that is similar to the suppression effect measured in the psychophysical experiments. The experimental results and their explanation give further support to the idea of a possible functional role of non-CRF inhibition in the separation of contour from texture information and the mediation of object contours to higher cortical areas.

Presented in this paper is a neural network model that can be used to investigate the possible self-organizing mechanisms occurring during the early ontogeny of spinal neural circuits in the vertebrate motor system. The neural circuit is composed of multiple types of neurons which correspond to motorneurons, Renshaw cells and a hypothetical class of interneurons. While the connectivity of this circuit is genetically predetermined, the efficacies of these connections - the synaptic strengths - evolve in accordance with activity-dependent mechanisms which are initiated by the intrinsic, autonomous activity present in the developing spinal cord. Using Oja's rule, a modified Hebbian learning scheme for adjusting the values of the connections, the network stably self-organizes developing, in the process, reciprocally activated motorneuron pools analogous to those which exist in vivo.

A statistical correlation technique (SCT) and two variants of a neural network are presented to solve the motion correspondence problem. Solutions of the motion correspondence problem aim to maintain the identities of individuated elements as they move. In a preprocessing stage, two snapshots of a moving scene are convoluted with two-dimensional Gabor functions, which yields orientations and spatial frequencies of the snapshots at every position. In this paper these properties are used to extract, respectively, the attributes orientation, size and position of line segments. The SCT uses cross-correlations to find the correct translation components, angle of rotation and scaling factor. These parameters are then used in combination with the positions of the line segments to calculate the centre of motion. When all of these parameters are known, the new positions of the line segments from the first snapshot can be calculated and compared to the features in the second snapshot. This yields the solution of the motion correspondence problem. Since the SCT is an indirect way of solving the problem, the principles of the technique are implemented in interactive activation and competition neural networks. With boundary problems and noise these networks perform better than the SCT. They also have the advantage that at every stage of the calculations the best candidates for corresponding pairs of line segments are known.

The nature of the stimulus-response relation for single auditory neurons is reflected in the properties of the Pre-Event Stimulus Ensemble: the ensemble of stimuli, preceding the occurrence of an action potential (neural event). This paper describes methods to analyse the spectro-temporal properties of this ensemble. These methods are based on the analytic signal representation of acoustic signals and functionals derived from it: the instantaneous amplitude and instantaneous frequency and the dynamic power spectrum. The procedures have been applied to a number of extra-cellular single unit recordings from the grassfrog, Rana temporatia L., recorded during presentation of an ensemble of tonal stimuli. The outcome of this analysis describes the spectro-temporal receptive field of the neuron under the present stimulus conditions. The procedure, based on the dynamic power spectrum is applicable to an arbitrary stimulus ensemble, thus allowing a comparison of the spectrotemporal receptive fields for different types of stimuli.