A Theoretical Analysis of the Influence of Fixational Instability on the Development of Thalamocortical Connectivity

Under natural viewing conditions, the physiological inotability of visual fixation keeps the projection of the stimulus on the retina in constant motion. After eye opening, chronic exposure to a constantly moving retinal image might influence the experience-dependent refinement of cell response characteristics. The results of previous modeling studies have suggested a contribution of fixational instability in the Hebbian maturation of the receptive fields of V1 simple cells (Rucci, Edelman, & Wray, 2000; Rucci & Casile, 2004). This paper presents a mathematieal explanation of our previous computational results. Using quasi-linear models of LGN units and V1 simple cells, we derive analytical expressions for the second-order statistics of thalamocortical activity before and after eye opening. We show that in the presence of natural stimulation, fixational instability introduces a spatially uncorrelated signal in the retinal input, whieh strongly influences the structure of correlated activity in the model

Exploring and explaining properties of motion processing in biological brains using a neural network

Visual motion perception underpins behaviours ranging from navigation to depth perception and grasping. Our limited access to biological systems constrain our understanding of how motion is processed within the brain. Here we explore properties of motion perception in biological systems by training a neural network to estimate the velocity of image sequences. The network recapitulates key characteristics of motion processing in biological brains, and we use our access to its structure to explore and understand motion (mis)perception. We find that the network captures the biological response to reverse-phi motion in terms of direction. We further find that it overestimates and underestimates the speed of slow and fast reverse-phi motion, respectively, because of the correlation between reverse-phi motion and the spatiotemporal receptive fields tuned to motion in opposite directions. Second, we find that the distribution of spatiotemporal tuning properties in the V1 and MT layers of the network are similar to those observed in biological systems. We then show that compared to MT units tuned to fast speeds, those tuned to slow speeds primarily receive input from V1 units tuned to high spatial frequency and low temporal frequency. Next, we find that there is a positive correlation between the pattern-motion and speed selectivity of MT units. Finally, we show that the network captures human underestimation of low coherence motion stimuli, and that this is due to pooling of noise and signal motion. These findings provide biologically plausible explanations for well-known phenomena, and produce concrete predictions for future psychophysical and neurophysiological experiments

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

From receptive profiles to a metric model of V1

In this work we show how to construct connectivity kernels induced by the receptive profiles of simple cells of the primary visual cortex (V1). These kernels are directly defined by the shape of such profiles: this provides a metric model for the functional architecture of V1, whose global geometry is determined by the reciprocal interactions between local elements. Our construction adapts to any bank of filters chosen to represent a set of receptive profiles, since it does not require any structure on the parameterization of the family. The connectivity kernel that we define carries a geometrical structure consistent with the well-known properties of long-range horizontal connections in V1, and it is compatible with the perceptual rules synthesized by the concept of association field. These characteristics are still present when the kernel is constructed from a bank of filters arising from an unsupervised learning algorithm.Comment: 25 pages, 18 figures. Added acknowledgement

Spike to Spike Model and Applications: A biological plausible approach for the motion processing

We propose V1 and MT functional models for biological motion recognition. Our V1 model transforms a video stream into spike trains through local motion detectors. The spike trains are the inputs of a spiking MT network. Each entity in the MT network corresponds to a simplified model of an MT cell. From the spike trains of MT cells a motion map of velocity distribution is built representing a sequence. Biological plausibility of both models is discused in detail in the paper. In order to show the efficiency of these models, the motion maps here obtained are used in the biological motion recognition task. We ran the experiments using two databases Giese and Weizmann, containing two (march, walk) and ten (e.g., march, jump, run) different classes, respectively. The results revealed that the motion map here proposed could be used as a reliable motion representation

Time-causal and time-recursive spatio-temporal receptive fields

We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, based on a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain. Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale. Specifically, extensions are presented about (i) parameterizing the intermediate temporal scale levels, (ii) analysing the resulting temporal dynamics, (iii) transferring the theory to a discrete implementation, (iv) computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision. We show that by distributing the intermediate temporal scale levels according to a logarithmic distribution, we obtain much faster temporal response properties (shorter temporal delays) compared to a uniform distribution. Specifically, these kernels converge very rapidly to a limit kernel possessing true self-similar scale-invariant properties over temporal scales, thereby allowing for true scale invariance over variations in the temporal scale, although the underlying temporal scale-space representation is based on a discretized temporal scale parameter. We show how scale-normalized temporal derivatives can be defined for these time-causal scale-space kernels and how the composed theory can be used for computing basic types of scale-normalized spatio-temporal derivative expressions in a computationally efficient manner.Comment: 39 pages, 12 figures, 5 tables in Journal of Mathematical Imaging and Vision, published online Dec 201

Perception of motion direction in luminance-and contrast-defined reversed-phi motion sequences

Nonlinear processing can be used to recover the motion of contrast modulations of binary noise patterns. A nonlinear stage has also been proposed to explain the perception of forward motion in motion sequences which typically elicit reversed-phi. We examined perceived direction of motion for stimuli in which these reversed motion sequences were used to modulate the contrast of binary noise patterns. A percept of forward motion could be elicted by both luminance-defined and contrast-defined stimuli. The perceived direction of motion seen in the contrast-defined stimuli showed a profound carrier dependency. The replacement of a static carrier by a dynamic carrier can reverse the perceived direction of motion. Forward motion was never seen with dynamic carriers. For luminance- and contrast-defined patterns the reversed motion percept increasingly dominated, with increases in the spatial frequency and temporal frequency of the modulation. Differences in the patterns of responses to the two stimuli over spatial and temporal frequency were abolished by the addition of noise to the luminance-defined stimulus. These data suggest the possibility that a single mechanism may mediate the perception of luminance- and contrast-defined motion