![Fig. 1. Topology of the KIII network. Adapted from [Chang & Freeman, 1998a].](/figures/fig-1-topology-of-the-kiii-network-adapted-from-chang-317bh99r.webp)
Q1. What have the authors contributed in "A study on a bionic pattern classifier based on olfactory neural system" ?
In this paper, the authors proposed a new application example of the KIII network for recognition of handwriting numerals.
A study on a bionic pattern classifier based on olfactory neural system
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A simulation of a biological olfactory neural system with a KIII set, which is a high-dimensional chaotic neural network designed to simulate the patterns of action potentials and EEG waveforms observed in electrophysiological experiments is presented.Abstract:
This paper presents a simulation of a biological olfactory neural system with a KIII set, which is a high-dimensional chaotic neural network. The KIII set differs from conventional artificial neural networks by use of chaotic attractors for memory locations that are accessed by, chaotic trajectories. It was designed to simulate the patterns of action potentials and EEG waveforms observed in electrophysiological experiments, and has proved its utility as a model for biological intelligence in pattern classification. An application to recognition of handwritten numerals is presented here, in which the classification performance of the KIII network under different noise levels was investigated.read more
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Journal ArticleDOI
Reply to comments received for “Dissipation of ‘dark energy’ by cortex in knowledge retrieval”
Book ChapterDOI
Engineering Applications of Olfactory Model from Pattern Recognition to Artificial Olfaction
TL;DR: The experiments to apply the novel neural network to recognition of handwriting numerals, classification of Mandarin spoken digits, recognition of human face and classi- fication of normal and hypoxia EEG have been carried out.
Proceedings ArticleDOI
A new olfactory receptor model based on chaos
Xing Wang,Jin Zhang,Rulong Wang +2 more
TL;DR: Experimental results show that the proposed olfactory receptor model can change between chaotic and non-chaotic states according to the input, which is also consonant with the real Olfactory neuron.
Book ChapterDOI
Neocortical ECoG Images Formed by Learning
TL;DR: Any algorithm that is proposed to explain vertebrate brain function is likely to be compatible with the olfactory code for perception, because all sensory and motor systems may have the same basic code in order to integrate information from multiple sources.
Book ChapterDOI
Three Types of State Transition Underlying Perception
TL;DR: This chapter sketches the neurodynamics of the prototypical olfactory sensory lobe of the brain; some ways in which its state changes are controlled by the forebrain in the mechanisms of attention and motivation; the development and evolution of its state space through bifurcations during learning; the role of chaos in creating new attractors in cerebral cortex; and explanation of the meanings of intentionality in the context of embodied cognition.
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Journal ArticleDOI
The organization of behavior.
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Book
Computer and Robot Vision
TL;DR: This two-volume set is an authoritative, comprehensive, modern work on computer vision that covers all of the different areas of vision with a balanced and unified approach.
Book
Neurodynamics: An Exploration in Mesoscopic Brain Dynamics
TL;DR: The dynamics of neural interaction and transmission, including spatial mapping of evoked brain potentials and EEGs to 27 define population state variables, and the use of behavioral correlates to optimize filters for gamma AM pattern classification are discussed.
Journal ArticleDOI
Chaotic resonance — methods and applications for robust classification of noisy and variable patterns
Robert Kozma,Walter J. Freeman +1 more
TL;DR: A theory of stochastic chaos is developed, in which aperiodic outputs with 1/f2 spectra are formed by the interaction of globally connected nodes that are individually governed by point attractors under perturbation by continuous white noise.
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