![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
Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics
TL;DR: The feasibility of interpreting neurophysiological data in the context of many-body physics is explored by using tools that physicists have devised to analyze comparable hierarchies in other fields of science using concepts of energy dissipation, the maintenance by cortex of multiple ground states corresponding to AM patterns, and the exclusive selection by spontaneous breakdown of symmetry of single states in sequential phase transitions.
Proceedings ArticleDOI
Neurodynamics of cognition and consciousness
Robert Kozma,Walter J. Freeman +1 more
TL;DR: A dynamical Theory-of-Mind (ToM) is presented to interpret experimental findings and it is proposed that meaningful knowledge is continuously created, processed, and dissipated in the form of sequences of oscillatory patterns of neural activity described through spatio-temporal phase transitions.
Journal ArticleDOI
A pattern recognition method for electronic noses based on an olfactory neural network
TL;DR: A chaotic neural network entitled KIII, which modeled olfactory systems, applied to an electronic nose to discriminate six typical volatile organic compounds (VOCs) in Chinese rice wines, has a good performance in classification of these VOCs of different concentrations.
Journal ArticleDOI
Definitions of state variables and state space for brain-computer interface : Part 1. Multiple hierarchical levels of brain function.
TL;DR: Brain-machine interfaces (BMI) offer a means to understand the downward sequence through correlation of behavior with motor cortical activity, beginning with macroscopic goal states and concluding with recording of microscopic MSA trajectories that operate neuroprostheses.
References
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Journal ArticleDOI
Mesoscopic neurodynamics: from neuron to brain.
TL;DR: The design of neurobiological experiments, analysis of the resulting data, and synthesis of explanatory models require an understanding of the hierarchical nature of brain organization, here conceived as single neurons and neural networks at the microscopic level.
Journal ArticleDOI
Biocomplexity: adaptive behavior in complex stochastic dynamical systems.
TL;DR: A new approach to chaos research is proposed that has the potential of characterizing biological complexity and the resulting theory of stochastic dynamical systems is a mathematical field at the interface of dynamical system theory and Stochastic differential equations.
Journal ArticleDOI
A proposed name for aperiodic brain activity: stochastic chaos
TL;DR: In this paper, a spectral analysis of short segments reveals peaks in the classical frequency ranges of the alpha (8-12 Hz), theta (3-7 Hz), beta (13-30 Hz) and gamma (30-100 Hz) bands of the EEG and MEG.
Journal ArticleDOI
Taming chaos: stabilization of aperiodic attractors by noise [olfactory system model]
TL;DR: The KIII model as mentioned in this paper is a model of the olfactory system that contains an array of 64 coupled oscillators simulating the Olfactory bulb (OB), with negative and positive feedback through low-pass filter lines from single oscillators.
Journal ArticleDOI
Parameter optimization in models of the olfactory neural system
Hung-Jen Chang,Walter J. Freeman +1 more
TL;DR: It is proposed that a set of reliable parameter optimization algorithms, including specification of performance criteria, should be an integral component of a realistic neural model.
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