In this paper, we utilize the physiological mechanism of non-classical receptive field and design a hierarchical network model for image representation based on neurobiology. It is different from the contour detection...
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Graph theory is a very useful tool in the study of functional and anatomical network in the brain. It had been widely used in the functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) signals. ...
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ISBN:
(纸本)9781467314886
Graph theory is a very useful tool in the study of functional and anatomical network in the brain. It had been widely used in the functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) signals. Only very few studies analyzed the neuronal connections composed of individually recorded neurons. Particularly applying in the case of neuronal functional networks of animal behavior-dependent was rare. Scientists have found the small-world network properties in the functional network derived from fMRI and EEG signals. Whether there are existing small-world properties in the neuronal networks of multi-electrode recording? We use graph theory techniques to construct and analyze the neuronal networks. In the functional networks of a simultaneously recorded population of neurons in prefrontal cortex of the rat, in a Y-maze working memory task, we find that the neuronal connection density is highly relevant to rat behavior. We find there is a small-world effect in the neuronal functional network compared to a random graph with the same size and average connection density. We also find that small-world properties have a great relationship to correlation coefficient threshold selection. These findings indicate that neuronal functional networks of multi-electrode recordings are also small-world networks. Network connection topology and connection density are related to the working memory tasks in the rat.
The neural mechanism of memory has a very close relation with the problem of representation in Artificial Intelligence (AI). In this paper a computational model is proposed to simulate the network of neurons in brain ...
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The feedforward model proposed by Hubel and Wiesel partially explained orientation selectivity in simple cells. This classical hypothesis attributed orientation preference to idealized alignment of geniculate cell rec...
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ISBN:
(纸本)9781467314886
The feedforward model proposed by Hubel and Wiesel partially explained orientation selectivity in simple cells. This classical hypothesis attributed orientation preference to idealized alignment of geniculate cell receptive fields. Many scholars have been either revising this model or putting forward new theories to account for more related phenomenon such as contrast invariant tuning. None of the previous neural models is complete in implementation details or involves strict computational strategies. This paper mathematically studied a detailed but vital question which has long been neglected: the possibility of massive variable-sized, unaligned geniculate cell receptive fields producing the orientation selectivity of a simple cell. The response curve of each afferent neuron is fully utilized to obtain a local constraint and a group-decision making approach is then applied to solve the constraint satisfaction problem. Our new model does not achieve just consistent experimental results with physiological data, but consistent interpretations of several illusions with observers' perceptions. The current work, which supplemented the previous models with necessary computational details, is based on ensemble coding in essence. This underlying mechanism helps to understand how visual information is processed in from the retina to the cortex.
The neural mechanism of memory has a very close relation with the problem of representation in Artificial Intelligence (AI). In this paper a computational model is proposed to simulate the network of neurons in brain ...
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The neural mechanism of memory has a very close relation with the problem of representation in Artificial Intelligence (AI). In this paper a computational model is proposed to simulate the network of neurons in brain and how they process information. The model refers to morphological and electrophysiological characteristics of neural information processing, and is based on the assumption that neurons encode their firing sequence. The network structure, functions for neural encoding at different stages, the representation of stimuli in memory, and an algorithm to form a memory are presented. It also analyzes the stability and recall rate for learning and the capacity of memory. Because neural dynamic processes, one succeeding another, achieve a neuron-level and coherent form by which information is represented and processed, it may facilitate examination of various branches of AI, such as inference, problem solving, pattern recognition, natural language processing and learning. The processes of cognitive manipulation occurring in intelligent behavior have a consistent representation while all being modeled from the perspective of computational neuroscience. Thus, the dynamics of neurons make it possible to explain the inner mechanisms of different intelligent behaviors by a unified model of cognitive architecture at a micro-level.
According to Biology there is a large disinhibitory area outside the classical receptive field (CRF), which is called as non-classical receptive field (nCRF). Combining CRF with nCRF could increase the sparseness, rel...
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According to Biology there is a large disinhibitory area outside the classical receptive field (CRF), which is called as non-classical receptive field (nCRF). Combining CRF with nCRF could increase the sparseness, reliability and precision of the neuronal responses. This paper is aimed at the realization of the neural circuit and the dynamic adjustment mechanism of the receptive field (RF) with respect to nCRF. On the basis of anatomical and electrophysiological evidence, we constructed a neural computational model, which can represent natural images faithfully, simply and rapidly. And the representation can significantly improve the subsequent operation efficiency such as segmentation or integration. This study is of particular significance in the development of efficient image processing algorithms based on neurobiological mechanisms. The RF mechanism of ganglion cell (GC) is the result of a long term of evolution and optimization of self-adaptability and high representation efficiency. So its performance evaluation in natural image processing is worthy of further study.
The traditional three-dimensional object recognition method based on hypothesise and test need to solve the coordinate transformation matrix from scene to model through a group of non-linear equations. Therefore, it h...
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The traditional three-dimensional object recognition method based on hypothesise and test need to solve the coordinate transformation matrix from scene to model through a group of non-linear equations. Therefore, it has a very high complexity. This paper presents a man-made object recognition method based on the geometry feature of line segments characteristics, and disperses the overall coordinate transformation calculation in every local plane homography calculation, reduces the complexity of the solution. First we pre-match the feature points using geometric invariants, then assume and solve the plane homography matrix between scenes to model. After that we match the line segments on the homography plane, and by this we verify the assumption. Experiments proved that this method can rapidly and accurately identify man-made objects which contain coplanar line segment features.
This paper presents a monocular approach of partially recovering three-dimensional information from rectangles and circles with proper prior knowledge. Taking image-formation process as projective transform, we first ...
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This paper presents a monocular approach of partially recovering three-dimensional information from rectangles and circles with proper prior knowledge. Taking image-formation process as projective transform, we first work out the focal length of an image with a common perspective rectangle and further obtain normal vectors of the supporting planes of rectangles and circles and relative depths of each point on the planes with respect to camera coordinate system. The absolute depths and other information about lengths and distances are also acquirable with reasonable estimation or measurement of side lengths and radius. The experimental results suggest good feasibility and acceptable precision of our approach.
It has been proved that acquired training is important to the development of stereopsis experience. Month-old babies already have the initial experience of invariance recognition of 3D objects. There is a slight lack ...
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It has been proved that acquired training is important to the development of stereopsis experience. Month-old babies already have the initial experience of invariance recognition of 3D objects. There is a slight lack of precision in the interpretation of biological vision. However, the small cost and the fast speed in calculation meet the requirements of invariance recognition, the rich visual experience in which play an important role. But what is the experience, how to acquire and how to use, these problems have never been satisfactorily resolved. In this paper we simulate the learning of visual experience in children, and solve a view angle estimated problem by using self-organizing network, which make the hidden experience clarified. Compared to the Classic camera calibration, which a large number of parameters need to be estimated, this method needs only one image and does not aim to 3D reconstruction. By avoiding the complex calibration and registration process, an amount of computation has been reduced. Visual experiences are all obtained from the most ordinary examples, and the characterization based on the geometric feature. Therefore, this method has strong expansibility and good generalization ability.
In this paper a new method for matching contours called CTFDP is presented. It is invariant to affine transformations and can provide robust and accurate estimation of point correspondence between closed curves. This ...
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In this paper a new method for matching contours called CTFDP is presented. It is invariant to affine transformations and can provide robust and accurate estimation of point correspondence between closed curves. This has all been achieved by exploiting the dynamic programming techniques in a coarse-to-fine framework. By normalizing the shape into a standard point distribution, the new method can compare different shapes despite the shearing and scaling effect of affine transformation. Using the coarse-to-fine dynamic programming technique, the shapes are aligned to each other by iteratively seeking for correspondences and estimating relative transformations so as to prune the start points in the dynamic programming stage in turn. Experiments on artificial and real images have validated the robustness and accuracy of the presented method.
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