52,866 research outputs found
The left intraparietal sulcus modulates the selection of low salient stimuli
Neuropsychological and functional imaging studies have suggested a general right hemisphere advantage for processing global visual information and a left hemisphere advantage for processing local information. In contrast, a recent transcranial magnetic stimulation study [Mevorach, C., Humphreys, G. W., & Shalev, L. Opposite biases in salience-based selection for the left and right posterior parietal cortex. Nature Neuroscience, 9, 740-742, 2006b] demonstrated that functional lateralization of selection in the parietal cortices on the basis of the relative salience of stimuli might provide an alternative explanation for previous results. In the present study, we applied a whole-brain analysis of the functional magnetic resonance signal when participants responded to either the local or the global levels of hierarchical figures. The task (respond to local or global) was crossed with the saliency of the target level (local salient, global salient) to provide, for the first time, a direct contrast between brain activation related to the stimulus level and that related to relative saliency. We found evidence for lateralization of salience-based selection but not for selection based on the level of processing. Activation along the left intraparietal sulcus (IPS) was found when a low saliency stimulus had to be selected irrespective of its level. A control task showed that this was not simply an effect of task difficulty. The data suggest a specific role for regions along the left IPS in salience-based selection, supporting the argument that previous reports of lateralized responses to local and global stimuli were contaminated by effects of saliency
Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model
A fundamental challenge in machine learning today is to build a model that
can learn from few examples. Here, we describe a reservoir based spiking neural
model for learning to recognize actions with a limited number of labeled
videos. First, we propose a novel encoding, inspired by how microsaccades
influence visual perception, to extract spike information from raw video data
while preserving the temporal correlation across different frames. Using this
encoding, we show that the reservoir generalizes its rich dynamical activity
toward signature action/movements enabling it to learn from few training
examples. We evaluate our approach on the UCF-101 dataset. Our experiments
demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5
accuracy, respectively, on the 101-class data while requiring just 8 video
examples per class for training. Our results establish a new benchmark for
action recognition from limited video examples for spiking neural models while
yielding competetive accuracy with respect to state-of-the-art non-spiking
neural models.Comment: 13 figures (includes supplementary information
A spatial scan statistic for zero-inflated Poisson process
The scan statistic is widely used in spatial cluster detection applications
of inhomogeneous Poisson processes. However, real data may present substantial
departure from the underlying Poisson process. One of the possible departures
has to do with zero excess. Some studies point out that when applied to data
with excess zeros, the spatial scan statistic may produce biased inferences. In
this work, we develop a closed-form scan statistic for cluster detection of
spatial zero-inflated count data. We apply our methodology to simulated and
real data. Our simulations revealed that the Scan-Poisson statistic steadily
deteriorates as the number of zeros increases, producing biased inferences. On
the other hand, our proposed Scan-ZIP and Scan-ZIP+EM statistics are, most of
the time, either superior or comparable to the Scan-Poisson statistic
Change-point model on nonhomogeneous Poisson processes with application in copy number profiling by next-generation DNA sequencing
We propose a flexible change-point model for inhomogeneous Poisson Processes,
which arise naturally from next-generation DNA sequencing, and derive score and
generalized likelihood statistics for shifts in intensity functions. We
construct a modified Bayesian information criterion (mBIC) to guide model
selection, and point-wise approximate Bayesian confidence intervals for
assessing the confidence in the segmentation. The model is applied to DNA Copy
Number profiling with sequencing data and evaluated on simulated spike-in and
real data sets.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS517 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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