Neural Dynamics of Motion Processing and Speed Discrimination

A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-tuned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the Vl→7 MT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that has been used to simulate how global motion capture occurs, and how spatial attention is drawn to moving forms. It provides a computational foundation for an emerging neural theory of 3-D form and motion perception.Office of Naval Research (N00014-92-J-4015, N00014-91-J-4100, N00014-95-1-0657, N00014-95-1-0409, N00014-94-1-0597, N00014-95-1-0409); Air Force Office of Scientific Research (F49620-92-J-0499); National Science Foundation (IRI-90-00530

Neural Dynamics of Motion Perception: Direction Fields, Apertures, and Resonant Grouping

A neural network model of global motion segmentation by visual cortex is described. Called the Motion Boundary Contour System (BCS), the model clarifies how ambiguous local movements on a complex moving shape are actively reorganized into a coherent global motion signal. Unlike many previous researchers, we analyse how a coherent motion signal is imparted to all regions of a moving figure, not only to regions at which unambiguous motion signals exist. The model hereby suggests a solution to the global aperture problem. The Motion BCS describes how preprocessing of motion signals by a Motion Oriented Contrast Filter (MOC Filter) is joined to long-range cooperative grouping mechanisms in a Motion Cooperative-Competitive Loop (MOCC Loop) to control phenomena such as motion capture. The Motion BCS is computed in parallel with the Static BCS of Grossberg and Mingolla (1985a, 1985b, 1987). Homologous properties of the Motion BCS and the Static BCS, specialized to process movement directions and static orientations, respectively, support a unified explanation of many data about static form perception and motion form perception that have heretofore been unexplained or treated separately. Predictions about microscopic computational differences of the parallel cortical streams V1 --> MT and V1 --> V2 --> MT are made, notably the magnocellular thick stripe and parvocellular interstripe streams. It is shown how the Motion BCS can compute motion directions that may be synthesized from multiple orientations with opposite directions-of-contrast. Interactions of model simple cells, complex cells, hypercomplex cells, and bipole cells are described, with special emphasis given to new functional roles in direction disambiguation for endstopping at multiple processing stages and to the dynamic interplay of spatially short-range and long-range interactions.Air Force Office of Scientific Research (90-0175); Defense Advanced Research Projects Agency (90-0083); Office of Naval Research (N00014-91-J-4100

Evaluation and neurocomputational modelling of visual adaptation to optically induced distortions

Spatial geometrical distortions are major artefacts in vision aid optical spectacles. Progressive additional lenses (PALs) are among such spectacles incurring inherent distortions. Distortions alter perceived features of the natural environment and are one of the causes for visual discomforts, such as apparent motion perception and spatial disorientation, experienced by novice spectacle wearers. Thus, fast and efficient visual adaptation to the distortions is a necessity to increase the users’ comfort and consequently overcome the related problems, e.g. risk of fall in the elderly when using PALs. Inspired by this necessity, the work is targeted to investigate the visual mechanisms underlying adaptation to distortions, in particular in PALs. Psychophysical procedures are employed to probe the characteristics of the neural mechanisms underlying the adaptation process in natural viewing conditions. With psychophysical approaches, three main properties of distortion adaptation are revealed; its cortical origin, the reference frame in which it is achieved and its long-term temporal dynamics. In order to discern how the functional organization of neurons enables the visual system to carry out a robust distortion adaptation in a natural environment, biologically plausible recurrent neural network models are utilized. Prediction performance of model variants with different neural network complexity and temporal dynamics of operation were assessed. From the model simulations, major functional roles of recurrent bottom-up and top-down cortical interactions in neural response tuning and in mediating adaptation at different time scales were depicted. The outcomes would further contribute to suggest a solution for facilitating adaptation. The relevance of the research within these aforementioned studies is not restricted to PALs but extends to distortions in other daily used optical utilities, such as virtual reality (VR) displays. Optical distortions are also artefacts in artificial sensory systems, like lens distortions in cameras used in machine vision. Understanding the neural correlates of distortion adaptation in human vision will thereby elicit characteristic features of robust and flexible neural systems to be implemented in brain inspired artificial vision

Computational principles for an autonomous active vision system

Vision research has uncovered computational principles that generalize across species and brain area. However, these biological mechanisms are not frequently implemented in computer vision algorithms. In this thesis, models suitable for application in computer vision were developed to address the benefits of two biologically-inspired computational principles: multi-scale sampling and active, space-variant, vision. The first model investigated the role of multi-scale sampling in motion integration. It is known that receptive fields of different spatial and temporal scales exist in the visual cortex; however, models addressing how this basic principle is exploited by species are sparse and do not adequately explain the data. The developed model showed that the solution to a classical problem in motion integration, the aperture problem, can be reframed as an emergent property of multi-scale sampling facilitated by fast, parallel, bi-directional connections at different spatial resolutions. Humans and most other mammals actively move their eyes to sample a scene (active vision); moreover, the resolution of detail in this sampling process is not uniform across spatial locations (space-variant). It is known that these eye-movements are not simply guided by image saliency, but are also influenced by factors such as spatial attention, scene layout, and task-relevance. However, it is seldom questioned how previous eye movements shape how one learns and recognizes an object in a continuously-learning system. To explore this question, a model (CogEye) was developed that integrates active, space-variant sampling with eye-movement selection (the where visual stream), and object recognition (the what visual stream). The model hypothesizes that a signal from the recognition system helps the where stream select fixation locations that best disambiguate object identity between competing alternatives. The third study used eye-tracking coupled with an object disambiguation psychophysics experiment to validate the second model, CogEye. While humans outperformed the model in recognition accuracy, when the model used information from the recognition pathway to help select future fixations, it was more similar to human eye movement patterns than when the model relied on image saliency alone. Taken together these results show that computational principles in the mammalian visual system can be used to improve computer vision models

The Role of Bottom-Up and Top-Down Cortical Interactions in Adaptation to Natural Scene Statistics

Adaptation is a mechanism by which cortical neurons adjust their responses according to recently viewed stimuli. Visual information is processed in a circuit formed by feedforward (FF) and feedback (FB) synaptic connections of neurons in different cortical layers. Here, the functional role of FF-FB streams and their synaptic dynamics in adaptation to natural stimuli is assessed in psychophysics and neural model. We propose a cortical model which predicts psychophysically observed motion adaptation aftereffects (MAE) after exposure to geometrically distorted natural image sequences. The model comprises direction selective neurons in V1 and MT connected by recurrent FF and FB dynamic synapses. Psychophysically plausible model MAEs were obtained from synaptic changes within neurons tuned to salient direction signals of the broadband natural input. It is conceived that, motion disambiguation by FF-FB interactions is critical to encode this salient information. Moreover, only FF-FB dynamic synapses operating at distinct rates predicted psychophysical MAEs at different adaptation time-scales which could not be accounted for by single rate dynamic synapses in either of the streams. Recurrent FF-FB pathways thereby play a role during adaptation in a natural environment, specifically in inducing multilevel cortical plasticity to salient information and in mediating adaptation at different time-scales

Temporal Dynamics of Decision-Making during Motion Perception in the Visual Cortex

How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.National Science Foundation (SBE-0354378, IIS-02-05271); Office of Naval Research (N00014-01-1-0624); National Institutes of Health (R01-DC-02852

A cortical model of object perception based on Bayesian networks and belief propagation.

Evidence suggests that high-level feedback plays an important role in visual perception by shaping the response in lower cortical levels (Sillito et al. 2006, Angelucci and Bullier 2003, Bullier 2001, Harrison et al. 2007). A notable example of this is reflected by the retinotopic activation of V1 and V2 neurons in response to illusory contours, such as Kanizsa figures, which has been reported in numerous studies (Maertens et al. 2008, Seghier and Vuilleumier 2006, Halgren et al. 2003, Lee 2003, Lee and Nguyen 2001). The illusory contour activity emerges first in lateral occipital cortex (LOC), then in V2 and finally in V1, strongly suggesting that the response is driven by feedback connections. Generative models and Bayesian belief propagation have been suggested to provide a theoretical framework that can account for feedback connectivity, explain psychophysical and physiological results, and map well onto the hierarchical distributed cortical connectivity (Friston and Kiebel 2009, Dayan et al. 1995, Knill and Richards 1996, Geisler and Kersten 2002, Yuille and Kersten 2006, Deneve 2008a, George and Hawkins 2009, Lee and Mumford 2003, Rao 2006, Litvak and Ullman 2009, Steimer et al. 2009). The present study explores the role of feedback in object perception, taking as a starting point the HMAX model, a biologically inspired hierarchical model of object recognition (Riesenhuber and Poggio 1999, Serre et al. 2007b), and extending it to include feedback connectivity. A Bayesian network that captures the structure and properties of the HMAX model is developed, replacing the classical deterministic view with a probabilistic interpretation. The proposed model approximates the selectivity and invariance operations of the HMAX model using the belief propagation algorithm. Hence, the model not only achieves successful feedforward recognition invariant to position and size, but is also able to reproduce modulatory effects of higher-level feedback, such as illusory contour completion, attention and mental imagery. Overall, the model provides a biophysiologically plausible interpretation, based on state-of-theart probabilistic approaches and supported by current experimental evidence, of the interaction between top-down global feedback and bottom-up local evidence in the context of hierarchical object perception

Abstracts from CIP 2007: Segundo Congreso Ibérico de Percepción

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Experimental Evidence for Top-Down Attentional Selection in the Selective Tuning Model of Visual Attention

To overcome limited processing capacity, our visual system facilitates information that relates to the task at hand while inhibiting irrelevant information via selective attention. Among various attention models and theories, the Selective Tuning model of visual attention (ST) is a computation model of visual processing that is based on biological mechanisms. This model emphasizes the role of top-down feedback processing in visual perception and has predicted its unique consequences, such as an attentional surround suppression in which the attentional focus is accompanied by an inhibitory surround. The previous studies have experimentally validated STs predictions, indicating that the components in ST do reflect actual visual processing in the brain. Nevertheless, many aspects of ST still need to be elaborated and several predictions and assumptions remain untested. The series of works in this dissertation investigate different aspects of top-down feedback processing in visual perception that ST has proposed to corroborate this model and to broaden our understanding of visual attention. The first study examined whether top-down feedback processing is necessary for an attention-demanding, fine-grained visual localization (Chapter 2). The subsequent two studies focused on the properties of different types of the attentional surround suppression, the end-result of top-down feedback processing. The second study suggested the interplay between the location-based and feature-based surround suppression and tested the potential factors that could manipulate the spatial extent of the location-based suppressive surround (Chapter 3). The last study demonstrated feature-based surround suppression in motion processing and its neurophysiological mechanism (Chapter 4). Collectively, this work reinforces functional significance of top-down, attention-mediated feedback for visual processing and supports the validity of ST as well