Isotropic Dynamic Hierarchical Clustering

We face a need of discovering a pattern in locations of a great number of points in a high-dimensional space. Goal is to group the close points together. We are interested in a hierarchical structure, like a B-tree. B-Trees are hierarchical, balanced, and they can be constructed dynamically. B-Tree approach allows to determine the structure without any supervised learning or a priori knowlwdge. The space is Euclidean and isotropic. Unfortunately, there are no B-Tree implementations processing indices in a symmetrical and isotropical way. Some implementations are based on constructing compound asymmetrical indices from point coordinates; and the others split the nodes along the coordinate hyper-planes. We need to process tens of millions of points in a thousand-dimensional space. The application has to be scalable. Ideally, a cluster should be an ellipsoid, but it would require to store O(n2) ellipse axes. So, we are using multi-dimensional balls defined by the centers and radii. Calculation of statistical values like the mean and the average deviation, can be done in an incremental way. While adding a point to a tree, the statistical values for nodes recalculated in O(1) time. We support both, brute force O(2n) and greedy O(n2) split algorithms. Statistical and aggregated node information also allows to manipulate (to search, to delete) aggregated sets of closely located points. Hierarchical information retrieval. When searching, the user is provided with the highest appropriate nodes in the tree hierarchy, with the most important clusters emerging in the hierarchy automatically. Then, if interested, the user may navigate down the tree to more specific points. The system is implemented as a library of Java classes representing Points, Sets of points with aggregated statistical information, B-tree, and Nodes with a support of serialization and storage in a MySQL database.Comment: 6 pages with 3 example

Probabilistic modeling of eye movement data during conjunction search via feature-based attention

Where the eyes fixate during search is not random; rather, gaze reflects the combination of information about the target and the visual input. It is not clear, however, what information about a target is used to bias the underlying neuronal responses. We here engage subjects in a variety of simple conjunction search tasks while tracking their eye movements. We derive a generative model that reproduces these eye movements and calculate the conditional probabilities that observers fixate, given the target, on or near an item in the display sharing a specific feature with the target. We use these probabilities to infer which features were biased by top-down attention: Color seems to be the dominant stimulus dimension for guiding search, followed by object size, and lastly orientation. We use the number of fixations it took to find the target as a measure of task difficulty. We find that only a model that biases multiple feature dimensions in a hierarchical manner can account for the data. Contrary to common assumptions, memory plays almost no role in search performance. Our model can be fit to average data of multiple subjects or to individual subjects. Small variations of a few key parameters account well for the intersubject differences. The model is compatible with neurophysiological findings of V4 and frontal eye fields (FEF) neurons and predicts the gain modulation of these cells

Task-demands can immediately reverse the effects of sensory-driven saliency in complex visual stimuli

In natural vision both stimulus features and task-demands affect an observer's attention. However, the relationship between sensory-driven (“bottom-up”) and task-dependent (“top-down”) factors remains controversial: Can task-demands counteract strong sensory signals fully, quickly, and irrespective of bottom-up features? To measure attention under naturalistic conditions, we recorded eye-movements in human observers, while they viewed photographs of outdoor scenes. In the first experiment, smooth modulations of contrast biased the stimuli's sensory-driven saliency towards one side. In free-viewing, observers' eye-positions were immediately biased toward the high-contrast, i.e., high-saliency, side. However, this sensory-driven bias disappeared entirely when observers searched for a bull's-eye target embedded with equal probability to either side of the stimulus. When the target always occurred in the low-contrast side, observers' eye-positions were immediately biased towards this low-saliency side, i.e., the sensory-driven bias reversed. Hence, task-demands do not only override sensory-driven saliency but also actively countermand it. In a second experiment, a 5-Hz flicker replaced the contrast gradient. Whereas the bias was less persistent in free viewing, the overriding and reversal took longer to deploy. Hence, insufficient sensory-driven saliency cannot account for the bias reversal. In a third experiment, subjects searched for a spot of locally increased contrast (“oddity”) instead of the bull's-eye (“template”). In contrast to the other conditions, a slight sensory-driven free-viewing bias prevails in this condition. In a fourth experiment, we demonstrate that at known locations template targets are detected faster than oddity targets, suggesting that the former induce a stronger top-down drive when used as search targets. Taken together, task-demands can override sensory-driven saliency in complex visual stimuli almost immediately, and the extent of overriding depends on the search target and the overridden feature, but not on the latter's free-viewing saliency

Deadwood in logged-over Dipterocarp forests of Borneo

Deadwood is an important stock of carbon in logged-over Dipterocarp forests but still remains poorly studied. Here we present the study of deadwood in logged-over Dipterocarp forests using two common approaches: plot-based approach and line-intersect-based approach. We conducted our research in three sites which are forest logged in 2003, 2007, and 2010 within Hutansanggam Labanan Lestari (HLL) forest, a certified forest concessionaire in Indonesia. We established 1,500 m of transect line (broken down in 50 m section) for each site. As a reference, we established 47 10 m x 10 m subplot for three sites. All fallen deadwood with diameter > 10 cm were recorded. Our results shows that the mass of fallen deadwood resulted by line-intersect-based method was much higher in compare to plotbased method. The mass of fallen deadwood in plot-based study (44.563 ± 9.155 Mg/ha) was significantly different with the mass of fallen deadwood in line-intersect-based study (69.587 ± 8.079 Mg/ha). Furthermore, for the variability of deadwood, both methods show consistence results which is the variability in 2003 was lower than that in 2007 and 2010. Based on our data, in order to get coefficient of variation of 10%, we recommend the use of minimum 40 plots of 20 m x 20 m to estimate deadwood in logged-over Dipterocarp forests. (Texte intégral

Collective stability of networks of winner-take-all circuits

The neocortex has a remarkably uniform neuronal organization, suggesting that common principles of processing are employed throughout its extent. In particular, the patterns of connectivity observed in the superficial layers of the visual cortex are consistent with the recurrent excitation and inhibitory feedback required for cooperative-competitive circuits such as the soft winner-take-all (WTA). WTA circuits offer interesting computational properties such as selective amplification, signal restoration, and decision making. But, these properties depend on the signal gain derived from positive feedback, and so there is a critical trade-off between providing feedback strong enough to support the sophisticated computations, while maintaining overall circuit stability. We consider the question of how to reason about stability in very large distributed networks of such circuits. We approach this problem by approximating the regular cortical architecture as many interconnected cooperative-competitive modules. We demonstrate that by properly understanding the behavior of this small computational module, one can reason over the stability and convergence of very large networks composed of these modules. We obtain parameter ranges in which the WTA circuit operates in a high-gain regime, is stable, and can be aggregated arbitrarily to form large stable networks. We use nonlinear Contraction Theory to establish conditions for stability in the fully nonlinear case, and verify these solutions using numerical simulations. The derived bounds allow modes of operation in which the WTA network is multi-stable and exhibits state-dependent persistent activities. Our approach is sufficiently general to reason systematically about the stability of any network, biological or technological, composed of networks of small modules that express competition through shared inhibition.Comment: 7 Figure

Activity of human hippocampal and amygdala neurons during retrieval of declarative memories

Episodic memories allow us to remember not only that we have seen an item before but also where and when we have seen it (context). Sometimes, we can confidently report that we have seen something (familiarity) but cannot recollect where or when it was seen. Thus, the two components of episodic recall, familiarity and recollection, can be behaviorally dissociated. It is not clear, however, whether these two components of memory are represented separately by distinct brain structures or different populations of neurons in a single anatomical structure. Here, we report that the spiking activity of single neurons in the human hippocampus and amygdala [the medial temporal lobe (MTL)] contain information about both components of memory. We analyzed a class of neurons that changed its firing rate to the second presentation of a previously novel stimulus. We found that the neuronal activity evoked by the presentation of a familiar stimulus (during retrieval) distinguishes stimuli that will be successfully recollected from stimuli that will not be recollected. Importantly, the ability to predict whether a stimulus is familiar is not influenced by whether the stimulus will later be recollected. We thus conclude that human MTL neurons contain information about both components of memory. These data support a continuous strength of memory model of MTL function: the stronger the neuronal response, the better the memory