A neural network model of attention-modulated neurodynamics

In the field of brain research, attention as one of the main issues in cognitive neuroscience is an important mechanism to be studied. The complicated structure of the brain cannot process all the information it receives at any moment. Attention, in fact, is considered as a possible useful mechanism in which brain concentrates on the processing of important information which is required at any certain moment. The main goal of this study is decoding the location of visual attention from local field potential signals recorded from medial temporal (MT) area of a macaque monkey. To this end, feature extraction and feature selection are applied in both the time and the frequency domains. After applying feature extraction methods such as the short time Fourier transform, continuous wavelet transform (CWT), and wavelet energy (scalogram), feature selection methods are evaluated. Feature selection methods used here are T-test, Entropy, receiver operating characteristic, and Bhattacharyya. Subsequently, different classifiers are utilized in order to decode the location of visual attention. At last, the performances of the employed classifiers are compared. The results show that the maximum information about the visual attention in area MT exists in the low frequency features. Interestingly, low frequency features over all the time-axis and all of the frequency features at the initial time interval in the spectrogram domain contain the most valuable information related to the decoding of spatial attention. In the CWT and scalogram domains, this information exists in the low frequency features at the initial time interval. Furthermore, high performances are obtained for these features in both the time and the frequency domains. Among different employed classifiers, the best achieved performance which is about 84.5 % belongs to the K-nearest neighbor classifier combined with the T-test method for feature selection in the time domain. Additionally, the best achieved result (82.9 %) is related to the spectrogram with the least number of selected features as large as 200 features using the T-test method and SVM classifier in the time-frequency domain.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of local field potential signals in decoding of visual attention

Seif

Daliri

2015

“…Recently, the visual attention (Desimone and Duncan 1995;Gu and Liljenstrom 2007;Haab et al 2011) has been widely used in pattern recognition field and received good results in natural images. Visual attention is one of the most important parts of consciousness Koch 1998, 2003;Crick et al 2004), with the aid of which human vision can easily and rapidly focus on salient objects in his sight but machines cannot.…”

Section: Introductionmentioning

confidence: 99%

Airport detection in remote sensing images: a method based on saliency map

Wang

et al. 2012

The detection of airport attracts lots of attention and becomes a hot topic recently because of its applications and importance in military and civil aviation fields. However, the complicated background around airports brings much difficulty into the detection. This paper presents a new method for airport detection in remote sensing images.Distinct from other methods which analyze images pixel by pixel, we introduce visual attention mechanism into detection of airport and improve the efficiency of detection greatly. Firstly, Hough transform is used to judge whether an airport exists in an image. Then an improved graphbased visual saliency model is applied to compute the saliency map and extract regions of interest (ROIs). The airport target is finally detected according to the scaleinvariant feature transform features which are extracted from each ROI and classified by hierarchical discriminant regression tree. Experimental results show that the proposed method is faster and more accurate than existing methods, and has lower false alarm rate and better antinoise performance simultaneously.

“…Accordingly, a visual mechanism referred to as attention selection has evolved, which rapidly shifts across the scene under view and selects a small area for further cortical processing (Treisman and Gelade 1980;Koch and Ullman 1985;Desimone and Duncan 1995;Crick and Koch 1998;Bundesen and Habekost 2008;Gu and Liljenstrom 2007;Haab et al 2011). Typically, attention selection is either driven in bottom-up manner or controlled by top-down cues (Itti and Koch 2001;Treisman and Gelade 1980;Koch and Ullman 1985).…”

Section: Introductionmentioning

confidence: 99%

Bottom–up attention: pulsed PCA transform and pulsed cosine transform

Wang

Zhang

2011

In this paper we propose a computational model of bottom-up visual attention based on a pulsed principal component analysis (PCA) transform, which simply exploits the signs of the PCA coefficients to generate spatial and motional saliency. We further extend the pulsed PCA transform to a pulsed cosine transform that is not only data-independent but also very fast in computation. The proposed model has the following biological plausibilities. First, the PCA projection vectors in the model can be obtained by using the Hebbian rule in neural networks. Second, the outputs of the pulsed PCA transform, which are inherently binary, simulate the neuronal pulses in the human brain. Third, like many Fourier transform-based approaches, our model also accomplishes the cortical center-surround suppression in frequency domain. Experimental results on psychophysical patterns and natural images show that the proposed model is more effective in saliency detection and predict human eye fixations better than the state-of-the-art attention models.