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arXiv 2023年

作者： Wei, Hui Tang, Fu-Yu The Laboratory of Cognitive Algorithms and Modeling Shanghai Key Laboratory of Data Science Department of Computer Science Fudan University Shanghai201203 China

Artificial objects usually have very stable shape features, which are stable, persistent properties in geometry. They can provide evidence for object recognition. Shape features are more stable and more distinguishing than appearance features, color features, grayscale features, or gradient features. The difficulty with object recognition based on shape features is that objects may differ in color, lighting, size, position, pose, and background interference, and it is not currently possible to predict all possible conditions. The variety of objects and conditions renders object recognition based on geometric features very challenging. This paper provides a method based on shape templates, which involves the selection, collection, and combination discrimination of geometric evidence of the edge segments of images, to find out the target object accurately from background, and it is able to identify the semantic attributes of each line segment of the target object. In essence, the method involves solving a global optimal combinatorial optimization problem. Although the complexity of the global optimal combinatorial optimization problem seems to be very high, there is no need to define the complex feature vector and no need for any expensive training process. It has very good generalization ability and environmental adaptability, and more solid basis for cognitive psychology than other methods. The process of collecting geometric evidence, which is simple and universal, shows considerable prospects for practical use. The experimental results prove that the method has great advantages in response to changes in the environment, invariant recognition, pinpointing the geometry of objects, search efficiency, and efficient calculation. This attempt contributes to understanding of some types of universal processing during the process of object recognition. © 2023, CC BY-NC-SA.

关键词： Object recognition

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A possible neural circuit for decision making and its learning process 1

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8th International Conference on Brain Inspired cognitive Systems, BICS 2016

作者： Wei, Hui Bu, Yijie Dai, Dawei Laboratory of Cognitive Modeling and Algorithms Shanghai Key Laboratory of Data Science Department of Computer Science Fudan University Shanghai China

ISBN: (数字)9783319496856

ISBN: (纸本)9783319496849

To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision making and responding according to changes in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits, and the encoding and decoding mechanisms from stimuli to responses, are important goals in neuroscience. A biologically plausible decision circuit consisting of computational neuron and synapse models and its learning mechanism are designed in this paper. The learning mechanism is based on two parts: first, effect of the punishment from the environment on the temporal correlations of neuron firings;second, spike timing dependent plasticity (STDP) of synapse. The decision circuit was used successfully to simulate the behavior of Drosophila exhibited in real experiments. In this paper, we place focus on the connections and interactions among excitatory and inhibitory neurons and try to give an explanation at a micro level (i.e. neurons and neural circuit) of how the observable decision making behavior is acquired and achieved. © Springer International Publishing AG 2016.

关键词： Decision making

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Contour representation and shape matching based on mechanism of visual cortex

Contour representation and shape matching based on mechanism...

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International Joint Conference on Neural Networks (IJCNN)

作者： Hui Wei Zheng Dong Laboratory of Cognitive Modeling and Algorithms Fudan University Shanghai China

ISBN: (纸本)9781509006212

We present a novel model to represent and match contour lines of closed shapes. This model is based on the mechanism of visual cortex. It extracts orientation features from input images with simple computation units that imitate simple cells in the visual cortex. The contour lines are accurately located by searching adjacent activated simple units. These activated simple units are concatenated in a chain to code the contour lines of closed shapes. In order to match between shapes, we propose a measure based on Fréchet distance and use dynamic programming to calculate the distance between different chains of simple units. The model is evaluated on the MPEG7 shape data set. We also demonstrate that this model can explain the shape selectivity of visual area V4.

关键词： Shape Visualization Brain modeling Computational modeling Feature extraction Data models Dynamic programming

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A hierarchical model for structure learning based on the physiological characteristics of neurons

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中国高等学校学术文摘·计算机科学 2007年第3期1卷 361-372页

作者： WEI Hui Department of Computer Science Laboratory of Algorithms for Cognitive ModelingLaboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory *** characteristics of typical ANN models are fixed connections,with evolved weights,globalized representations,and globalized optimizations,all based on a mathematical *** makes those models to be deficient in robustness,efficiency of learning,capacity,anti-jamming between training sets,and correlativity of samples,*** this paper,we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing.A hierarchical neural network was designed and realized to implement structure learning and representations based on connected *** basic characteristics of this model are localized and random connections,field limitations of neuron fan-in and fan-out,dynamic behavior of neurons,and samples represented through different sub-circuits of neurons specialized into different response *** the end of this paper,some important aspects of error correction,capacity,learning efficiency,and soundness of structural representation are analyzed *** paper has demonstrated the feasibility and advantages of structure learning and *** model can serve as a fundamental element of cognitive systems such as perception and associative ***-words structure learning,representation,associative memory,computational neuroscience

关键词： physiological characteristics based learning structure hierarchical neural network Artificial Neural Networks memory different error correction paper perception model connected cognitive problems typical dynamic aspects end ANN

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