Neural System Identification with Spike-triggered Non-negative Matrix Factorization

Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina as a relatively simple neuronal circuit. A retinal ganglion cell receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required that can decipher these components in a systematic manner. Recently a method termed spike-triggered non-negative matrix factorization (STNMF) has been proposed for this purpose. In this study, we extend the scope of the STNMF method. By using the retinal ganglion cell as a model system, we show that STNMF can detect various computational properties of upstream bipolar cells, including spatial receptive field, temporal filter, and transfer nonlinearity. In addition, we recover synaptic connection strengths from the weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of a ganglion cell into a few subsets of spikes where each subset is contributed by one presynaptic bipolar cell. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.Comment: updated versio

Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution

Bayesian contextual classification of noise-contaminated multi-variate images

A Bayesian contextual classification scheme is presented in connection with modified M-estimates and a discrete Markov random field model. Due to information noise caused by the spatial distribution of training samples and system noise caused by the sensor, modified M-estimates and a preprocessing method of restoring degraded images are implemented to yield spectral models insensitive to the noise sources. The spatial dependency of adjacent class labels is characterized based on local transition probabilities in order to use contextual information. The classification performance which relies on the signal-to-noise ratio is enhanced by removing system noise, and restored images are incorporated in the suggested contextual decision rule. The experimental results show that the suggested scheme outperforms conventional non-contextual classifiers as well as contextual classifiers which are based on the least squares estimates or other spatial interaction models

An Exploratory Study on the Measuring of Privacy Policies

With the development of Internet technology, individuals have become increasingly more dependent on the network. Personal information is collected by many firms when people surf online, but information collected is often misused by collectors, which may seriously violated the privacy of consumer. For this reason, a lot of government agencies and international organizations put great emphases on the development of privacy protection regulations. Companies from different parts of the globe have begun to issue privacy policies or statements on their websites, some detailed some simple. It is thus a vital task to evaluate these privacy policies and identify the degree of privacy protection. On the basis of previous studies, this article explores the dimensions of Internet privacy policy from the perspectives of personal information protection. Five dimensions of privacy policies including notice, choice, access, security and enforcement are identified and scale items for each dimensions are also proposed