Goal Directed Visual Search Based on Color Cues: Co-operative Effectes of Top-Down & Bottom-Up Visual Attention

Focus of Attention plays an important role in perception of the visual environment. Certain objects stand out in the scene irrespective of observers\u27 goals. This form of attention capture, in which stimulus feature saliency captures our attention, is of a bottom-up nature. Often prior knowledge about objects and scenes can influence our attention. This form of attention capture, which is influenced by higher level knowledge about the objects, is called top-down attention. Top-down attention acts as a feedback mechanism for the feed-forward bottom-up attention. Visual search is a result of a combined effort of the top-down (cognitive cue) system and bottom-up (low level feature saliency) system. In my thesis I investigate the process of goal directed visual search based on color cue, which is a process of searching for objects of a certain color. The computational model generates saliency maps that predict the locations of interest during a visual search. Comparison between the model-generated saliency maps and the results of psychophysical human eye -tracking experiments was conducted. The analysis provides a measure of how well the human eye movements correspond with the predicted locations of the saliency maps. Eye tracking equipment in the Visual Perceptual Laboratory in the Center for Imaging Science was used to conduct the experiments

Retinal oscillations carry visual information to cortex

Thalamic relay cells fire action potentials that transmit information from retina to cortex. The amount of information that spike trains encode is usually estimated from the precision of spike timing with respect to the stimulus. Sensory input, however, is only one factor that influences neural activity. For example, intrinsic dynamics, such as oscillations of networks of neurons, also modulate firing pattern. Here, we asked if retinal oscillations might help to convey information to neurons downstream. Specifically, we made whole-cell recordings from relay cells to reveal retinal inputs (EPSPs) and thalamic outputs (spikes) and analyzed these events with information theory. Our results show that thalamic spike trains operate as two multiplexed channels. One channel, which occupies a low frequency band (<30 Hz), is encoded by average firing rate with respect to the stimulus and carries information about local changes in the image over time. The other operates in the gamma frequency band (40-80 Hz) and is encoded by spike time relative to the retinal oscillations. Because these oscillations involve extensive areas of the retina, it is likely that the second channel transmits information about global features of the visual scene. At times, the second channel conveyed even more information than the first.Comment: 21 pages, 10 figures, submitted to Frontiers in Systems Neuroscienc

Neurons in the thalamic reticular nucleus are selective for diverse and complex visual features

All visual signals the cortex receives are influenced by the perigeniculate sector (PGN) of the thalamic reticular nucleus, which receives input from relay cells in the lateral geniculate and provides feedback inhibition in return. Relay cells have been studied in quantitative depth; they behave in a roughly linear fashion and have receptive fields with a stereotyped center-surround structure. We know far less about reticular neurons. Qualitative studies indicate they simply pool ascending input to generate non-selective gain control. Yet the perigeniculate is complicated; local cells are densely interconnected and fire lengthy bursts. Thus, we employed quantitative methods to explore the perigeniculate using relay cells as controls. By adapting methods of spike-triggered averaging and covariance analysis for bursts, we identified both first and second order features that build reticular receptive fields. The shapes of these spatiotemporal subunits varied widely; no stereotyped pattern emerged. Companion experiments showed that the shape of the first but not second order features could be explained by the overlap of On and Off inputs to a given cell. Moreover, we assessed the predictive power of the receptive field and how much information each component subunit conveyed. Linear-non-linear (LN) models including multiple subunits performed better than those made with just one; further each subunit encoded different visual information. Model performance for reticular cells was always lesser than for relay cells, however, indicating that reticular cells process inputs non-linearly. All told, our results suggest that the perigeniculate encodes diverse visual features to selectively modulate activity transmitted downstream.This work is supported by National Institutes of Health EY09593 (Judith A. Hirsch), National Science Foundation IIS-0713657 (Friedrich T. Sommer), and the Redwood Center for Theoretical Neuroscience (Friedrich T. Sommer)

Detection of inconsistent regions in video streams

Humans have a general understanding about their environment. We possess a sense of distinction between what is consistent and inconsistent about the environment based on our prior experience. Any aspect of the scene that does not fit into this definition of normalcy tends to be classified as a novel event. An example of this is a casual observer standing over a bridge on a freeway, tracking vehicle traffic, where the vehicles traveling at or around the same speed limit are generally ignored and a vehicle traveling at a much higher (or lower) speed is subject to one’s immediate attention. In this paper, we extend a computational learning based framework for detecting inconsistent regions in a video sequence detection. The framework extracts low-level features from scenes, based on the focus of attention theory and combines unsupervised learning with habituation theory for learning these features. The paper presents results from our experiments on natural video streams for identifying inconsistency in velocity of moving objects and also the static changes in the scene