Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456
Wichert et al., 2008 - Google Patents
[go: Go Back, main page]

Wichert et al., 2008 - Google Patents

Visual search light model for mental problem solving

Wichert et al., 2008

View PDF
Document ID
2600064755326475360
Author
Wichert A
Pereira J
Carreira P
Publication year
Publication venue
Neurocomputing

External Links

Snippet

This paper proposes a model of mental imagery which takes into account the role of internal attentional search light. We model the process of mental imagery problem solving by a long term memory which manipulates, with the aid of associations, the information of the visual …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • G06N3/082Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0454Architectures, e.g. interconnection topology using a combination of multiple neural nets
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/10Simulation on general purpose computers
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6251Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • G06K9/4604Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
    • G06K9/4609Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
    • G06K9/4619Biologically-inspired filters, e.g. receptive fields
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices

Similar Documents

Publication Publication Date Title
Jaafra et al. Reinforcement learning for neural architecture search: A review
You et al. Graph structure of neural networks
Parhi et al. Brain-inspired computing: Models and architectures
WO2021218517A1 (en) Method for acquiring neural network model, and image processing method and apparatus
Rao et al. Active predictive coding: A unifying neural model for active perception, compositional learning, and hierarchical planning
Wang Pattern recognition: Neural networks in perspective
Ji et al. Where-what network 1:“Where” and “What” assist each other through top-down connections
DE69124231T2 (en) ARTIFICIAL NEURONAL PLANT
Wang et al. D-lsm: Deep liquid state machine with unsupervised recurrent reservoir tuning
WO2002069137A1 (en) Dynamical brain model for use in data processing applications
CN111027457A (en) Gating-based Independent Recurrent Neural Network and Skeletal Action Recognition
Sun et al. A spiking neural network for extraction of features in colour opponent visual pathways and FPGA implementation
CN114638408A (en) Pedestrian trajectory prediction method based on spatiotemporal information
Liang et al. Facial expression recognition using LBP and CNN networks integrating attention mechanism
D’Amico et al. Self-attention as an attractor network: transient memories without backpropagation
Queisser et al. Emergence of content-agnostic information processing by a robot using active inference, visual attention, working memory, and planning
Wichert et al. Visual search light model for mental problem solving
Buscarino et al. Quaternion neural networks for multidimensional applications: An overview
Takada et al. Unsupervised learning of shape-invariant lie group transformer by embedding ordinary differential equation
Li et al. MEDA: Multi-output Encoder-Decoder for Spatial Attention in Convolutional Neural Networks
Zheng et al. Color image associative memory on a class of Cohen–Grossberg networks
Capece et al. Converting night-time images to day-time images through a deep learning approach
Reda Deep learning an overview
Greco et al. The acquisition of new categories through grounded symbols: An extended connectionist model
Popov et al. Recognition of Dynamic Targets using a Deep Convolutional Neural Network