![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
01 Jan 2006-International Journal of Bifurcation and Chaos (World Scientific Publishing Company)-Vol. 16, Iss: 8, pp 2425-2434
TL;DR: 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.
Summary (2 min read)
Jump to: [1. Introduction] – [2. K Set Model Description] – [3. Application on Handwriting Numeral Recognition] and [4. Discussion]
1. Introduction
- Artificial Neural Networks (ANN) form a class of models and methods inspired by the study of biological neural systems.
- Classical ANN are simplistic models in comparison with biological neural systems.
- Whereas deterministic chaos is stationary, noise-free, autonomous and low-dimensional, brain chaos is unstable with repeated state transitions, drenched in noise, high-dimensional, and engaged with the environment, therefore not autonomous in the sense of having no perturbation once initiated.
- This formation of local basins corresponds to the memory of different patterns; the recognition of a pattern follows when the system trajectory enters into a certain basin and converges to the attractor in that basin.
2. K Set Model Description
- The central olfactory neural system is composed of olfactory bulb (OB), anterior nucleus (AON) and prepyriform cortex (PC).
- Xj(t) represents the state variable of jth neural population, which is connected to the ith, while Wij indicates the connection strength between them.
- The KIII network describe the whole olfactory neural system, the populations of neurons, local synaptic connection, and long forward and distributed time-delayed feedback loops.
- Some numerical analysis of the KIII network, using the parameter set in reference [Chang & Freeman, 1998a], is shown in Figs. 2 and 3.
- It is also another indirect description of the basal chaotic attractor and the state transitions that take place when the stimulus begins and ends.
3. Application on Handwriting Numeral Recognition
- Pattern recognition is an important subject of artificial intelligence, also a primary field for the application of ANN.
- According to their specific requirements, the authors made some modifications in the Hebbian learning rule: (1) they designed two methods for increasing the connection strength which is described below; (2) they introduced a bias coefficient K to the learning process.
- The activity of the ith channel is represented by SDαi, which is the mean standard deviation of the output of the ith mitral node (Mi) over the period of the presentation of input patterns, as Eq. (3).
- The test data set contains 200 samples in 20 groups of handwritten numeric characters written by 20 different students.
- Results (Fig. 5) showed that as the noise level increased the correct classification rate of the KIII network increased to a plateau and then decreased.
4. Discussion
- The first is about the feature extraction in preprocessing.
- The new algorithm for increasing the connection weight makes KIII network able to memorize and classify more patterns than it used to.
- Also, it is more reasonable to believe that the connection weights, which represent the biological synaptic connections, change gradually in the learning process.
- It is demonstrated by electrophysiological experiment and computer simulation that the additive noise in KIII network could maintain the KII components at nonzero point attractor and could stabilize the chaotic attractor landscape formed by learning [Freeman, 1999].
- In the present research the KIII network is still implemented by digital computer, which differs fundamentally from the analog real olfactory neural system.
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Figures (8)
Fig. 4. Sample of the test data set. 
Fig. 3. (Continued) 
Fig. 5. Classification performance of KIII. ![Fig. 1. Topology of the KIII network. Adapted from [Chang & Freeman, 1998a].](/figures/fig-1-topology-of-the-kiii-network-adapted-from-chang-317bh99r.png)
Fig. 1. Topology of the KIII network. Adapted from [Chang & Freeman, 1998a]. 
Table 1. Classification result — using KIII. 
Table 2. Classification results — comparison. 
Fig. 3. (Continued) 
Fig. 2. Output of several nodes of KIII network with no stimulus.
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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.
Abstract: Artificial neural networks (ANNs) are generally considered as the most promising pattern recognition method to process the signals from a chemical sensor array of electronic noses, which makes the system more bionics. This paper presents 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. Thirty-two-dimensional feature vectors of a sensor array consisting of eight sensors, in which four features were extracted from the transient response of each TGS sensor, were input into the KIII network to investigate its generalization capability for concentration influence elimination and sensor drift counteraction. In comparison with the conventional back propagation trained neural network (BP-NN), experimental results show that the KIII network has a good performance in classification of these VOCs of different concentrations and even for the data obtained 1 month later than the training set. Its robust generalization capability is suitable for electronic nose applications to reduce the influence of concentration and sensor drift.
73 citations
Cites background from "A study on a bionic pattern classif..."
...E-mail address: guangli@zju.edu.cn (G. Li). o l n m s b [ 925-4005/$ – see front matter © 2007 Published by Elsevier B.V. oi:10.1016/j.snb.2007.02.058 t phase; Olfactory model; Sensor drift onitoring [4,5], food and beverage industry [6–8], medical iagnosis [9], public security [10], etc....
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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.
Abstract: Neocortical state variables are defined and evaluated at three levels: microscopic using multiple spike activity (MSA), mesoscopic using local field potentials (LFP) and electrocorticograms (ECoG), and macroscopic using electroencephalograms (EEG) and brain imaging Transactions between levels occur in all areas of cortex, upwardly by integration (abstraction, generalization) and downwardly by differentiation (speciation) The levels are joined by circular causality: microscopic activity upwardly creates mesoscopic order parameters, which downwardly constrain the microscopic activity that creates them Integration dominates in sensory cortices Microscopic activity evoked by receptor input in sensation induces emergence of mesoscopic activity in perception, followed by integration of perceptual activity into macroscopic activity in concept formation The reverse process dominates in motor cortices, where the macroscopic activity embodying the concepts supports predictions of future states as goals These macroscopic states are conceived to order mesoscopic activity in patterns that constitute plans for actions to achieve the goals These planning patterns are conceived to provide frames in which the microscopic activity evolves in trajectories that adapted to the immediate environmental conditions detected by new stimuli This circular sequence forms the action-perception cycle Its upward limb is understood through correlation of sensory cortical activity with behavior Now 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 Part 1 develops a hypothesis that describes qualitatively the neurodynamics that supports the action-perception cycle and derivative reflex arc Part 2 describes episodic, “cinematographic–spatial pattern formation and predicts some properties of the macroscopic and mesoscopic frames by which the embedded trajectories of the microscopic activity of cortical sensorimotor neurons might be organized and controlled
67 citations
Cites background from "A study on a bionic pattern classif..."
...These state variables then may also serve as variables in analytic equations that express the dynamics revealed by data-driven models in nonlinear differential equations (Freeman 1975/2004) forming K-sets (Kozma and Freeman 2001; Principe et al. 2001; Kozma et al. 2003; Li et al. 2006 ) and neuropercolation theory (Kozma et al. 2004)....
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...…serve as variables in analytic equations that express the dynamics revealed by data-driven models in nonlinear differential equations (Freeman 1975/2004) forming K-sets (Kozma and Freeman 2001; Principe et al. 2001; Kozma et al. 2003; Li et al. 2006) and neuropercolation theory (Kozma et al. 2004)....
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References
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01 Jan 1989
TL;DR: An attempt is made to understand the natural design principles that underlie the superior performance of biological olfactory systems in pattern recognition in mathematics, learning algorithms, and neuromorphic hardware.
Abstract: An attempt is made to understand the natural design principles that underlie the superior performance of biological olfactory systems in pattern recognition. The authors express these principles in mathematics, learning algorithms, and neuromorphic hardware. A diagram of the olfactory system and its mathematical model are presented to show how to implement the system by software and electronic hardware. Its capability for pattern classification is verified in an input-driven model olfactory bulb under an input correlation learning rule. >
19 citations
"A study on a bionic pattern classif..." refers methods in this paper
...The values of s = 5 and K = 0.4 are chosen based on the previous experiments of the application of KIII model [Kozma & Freeman, 2001; Principe et al., 2001; Yao & Freeman, 1989]....
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...…recognition, which simulated an aspect of the biological intelligence, as demonstrated by previous applications of the KIII network to recognition of one-dimensional sequences, industrial data and spatiotemporal EEG patterns [Kozma & Freeman, 2001; Principe et al., 2001; Yao & Freeman, 1989]....
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