Intentional systems: Review of neurodynamics, modeling, and robotics implementation

Kozma, Róbert

doi:10.1016/j.plrev.2007.10.002

Cited by 20 publications

(16 citation statements)

References 82 publications

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“…Today, K sets are used in a wide range of applications, including classification (Chang, Freeman, & Burke, 1998;Freeman, Kozma, & Werbos, 2001), image recognition (Li, Lou, Wang, Li, & Freeman, 2006), time series prediction (Beliaev & Kozma, 2007), and robot navigation (Harter & Kozma, 2005;Voicu, Kozma, Wong, & Freeman, 2004). Recent developments include KIV sets for sensor fusion (Kozma & Tunstel, 2005;Kozma & Muthu, 2004), and autonomous control (Kozma, 2007a;Kozma et al, 2008). The hierarchical K model based approach is summarized in the 10 ''Building Blocks'' of neurodynamics (Freeman, 1975(Freeman, , 2001):…”

Section: Principles Of Neurodynamics -Hierarchy Of K Setsmentioning

confidence: 99%

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The KIV model of intentional dynamics and decision making

Kozma

Freeman

2009

Neural Networks

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a b s t r a c tHuman cognition performs granulation of the seemingly homogeneous temporal sequences of perceptual experiences into meaningful and comprehensible chunks of fuzzy concepts and behaviors. These knowledge granules are stored and consequently accessed during action selection and decisions. A dynamical approach is presented here to interpret experimental findings using K (Katchalsky) models. In the K model, meaningful knowledge is repetitiously created and processed in the form of sequences of oscillatory patterns of neural activity distributed across space and time. These patterns are not rigid but flexible and intermittent; soon after they arise through phase transitions, they dissipate. Computational implementations demonstrate the operation of the model based on the principles of intentional brain dynamics.

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Section: Principles Of Neurodynamics -Hierarchy Of K Setsmentioning

confidence: 99%

“…The interaction between entorhinal cortex, amygdala, hippocampal and cortical areas is studied in and Kozma and Muthu (2004). The role of entorhinal cortex in decision making under the influence of sensory, orientation, and motivational clues has been evaluated (Kozma, 2007a).…”

Section: Introductionmentioning

confidence: 99%

The KIV model of intentional dynamics and decision making

Kozma

Freeman

2009

Neural Networks

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“…They are mesoscopic models that represent an intermediate level between microscopic and macroscopic structures of the brain [24], [17]. The models were introduced by neuroscientist Walter J. Freeman in the 1970s [16] and gained prominence since the turn of the century [25], [26], [27], [28].…”

Section: Overview Of K-setsmentioning

confidence: 99%

“…KI typically operates far from thermodynamic equilibrium output. Neural interaction by stable mutual excitation (or mutual inhibition) is fundamental to understand brain dynamics [24].…”

Section: B Ki Setsmentioning

confidence: 99%

Face recognition through a chaotic neural network model

Carlos

Rosa

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Abstract-K-sets models are connectionist methods based on neuron populations, conceived through EEG analyses of the mammalian olfactory system. These models present a biologically more plausible structure and behavior than other neural networks models. K-sets have been used in many machine-learning problems, with potential application on pattern recognition while presenting novel chaotic concepts for signal processing. This paper presents the characteristics of the K-sets models and their application in a face recognition task. Our method was tested using Yale Face Database B and the results show that it outperforms popular recognition methods.

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“…The OB then connects to the second and third layers, the anterior olfactory nuclei (AON) and the prepiriform cortex (PC). AON and PC then have connections between themselves and back to the OB [13] (see Figure 3). As the output is dispersed in the PC layer of the KIII, a second algorithm is typically used to translate or classify this output.…”

Section: A K-set Hierarchymentioning

confidence: 99%

Motor imagery classification for brain-computer interfaces through a chaotic neural network

Piazentin

Rosa

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Abstract-In this paper, we propose to enhance the detection of control states in online brain-computer interfaces (BCI) with the use of the biologically inspired K-set neural network. This neural network was initially built to model brain waves of small sets of neurons in the brain and later showed a great capability of encoding complex and noisy data into oscillation patterns. We apply the K-set network to classification of motor imagery, a type of mental state very useful for BCI applications. Experimental results show that the network can work efficiently in this task and thus provide better control for BCI applications.

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Intentional systems: Review of neurodynamics, modeling, and robotics implementation

Cited by 20 publications

References 82 publications

The KIV model of intentional dynamics and decision making

The KIV model of intentional dynamics and decision making

Face recognition through a chaotic neural network model

Motor imagery classification for brain-computer interfaces through a chaotic neural network

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