![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
Freeman K-Set
Walter J. Freeman,Harry R. Erwin +1 more
TL;DR: Simulated impulse responses for open loop (interactions are suppressed by anesthesia), mutual excitation (KIe), and negative feedback (KII) and the rate constants have been shown to hold in closed-loop interactive states.
Book ChapterDOI
Scale-Free Cortical Planar Networks
TL;DR: Modeling brain dynamics requires us to define the behavioral context in which brains interact with the world, and to choose appropriate mathematics, here ordinary differential equations (ODE) and random graph theory (RGT).
Journal ArticleDOI
Definitions of state variables and state space for brain-computer interface Part 2. Extraction and classification of feature vectors
TL;DR: In this article, the central dynamics of the action-perception cycle has five steps: emergence from an existing macroscopic brain state of a pattern that predicts a future goal state; selection of a mesoscopic frame for action control; execution of a limb trajectory by microscopic spike activity; modification of microscopic cortical spike activity by sensory inputs; construction of mesoscopic perceptual patterns; and integration of a new macroscopy brain state.
Proceedings ArticleDOI
Application of Novel Chaotic Neural Networks to Mandarin Digital Speech Recognition
TL;DR: Experimental results show that the KIII set performs digital speech recognition efficiently, and is applied to digital classification of the sounds of Mandarin spoken digits.
Proceedings ArticleDOI
Recognition of hypoxia EEG with a preset confidence level based on EEG analysis
TL;DR: The experimental results of classifying normal and hypoxia EEGs show that the method can set confidence level in advance for every prediction to control the risk of error effectively.
References
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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.
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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.
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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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