Habituation in the kiii olfactory model with chemical sensor arrays

Gutierrez‐Osuna, Ricardo; Gutiérrez-Gálvez, A.

doi:10.1109/tnn.2003.820438

Cited by 28 publications

(25 citation statements)

References 15 publications

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“…Based on the neurophsiological findings, Freeman [31], [32], [20] has developed a model of the chaotic dynamics observed in the cortical olfactory system, called the K-sets. K-sets have been used successfully for dynamic memory designs and for robust classification and pattern recognition [21], [33], [24], [34].…”

Section: A Connectionist Models Of Spatio-temporal Neural Dynamicsmentioning

confidence: 99%

Chaotic Neurodynamics for Autonomous Agents

Harter

Kozma

2005

IEEE Trans. Neural Netw.

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Abstract-Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are becoming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limitcycle approximation allow. Here we present a discretized model inspired by Freeman's K-set mesoscopic level population model. We show that this version is capable of replicating the important principles of aperiodic/chaotic neurodynamics while being fast enough for use in real-time autonomous agent applications. This simplification of the K model provides many advantages not only in terms of efficiency but in simplicity and its ability to be analyzed in terms of its dynamical properties. We study the discrete version using a multi-layer, highly recurrent model of the neural architecture of perceptual brain areas. We use this architecture to develop example action selection mechanisms in an autonomous agent.

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Section: A Connectionist Models Of Spatio-temporal Neural Dynamicsmentioning

confidence: 99%

Chaotic Neurodynamics for Autonomous Agents

Harter

Kozma

2005

IEEE Trans. Neural Netw.

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“…The K-sets have been used for dynamic memory designs and for robust classification and pattern recognition (Gutierrez-Osuna and Gutierrez-Galvez 2003;Harter and Kozma 2005). It is deserved further investigation that the K-sets are used for the classification and pattern recognition of the CPG which corresponds to different locomotion (Dong et al 2011).…”

Section: Discussionmentioning

confidence: 99%

Effects on hypothalamus when CPG is fed back to basal ganglia based on KIV model

Tian

et al. 2014

Cogn Neurodyn

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The KIV model approximates the operation of the basic vertebrate forebrain together with the basal ganglia and motor systems. In KIV model, the hypothalamus and the basal ganglia which are two important parts in the midline forebrain are closely associated with the locomotion. The CPG model with time delay is established in this paper and the stability of this CPG model is discussed. The CPG output is treated as the proprioception and fed back to the basal ganglia. We focus on the effects on the hypothalamus and the basal ganglia when the time delay parameter a d , the CPG amplitude parameter e and the CPG frequency parameter T r are changed. Through analysis, we find that there exists optimum value of the parameters a d or T r which can make the synchronization of the hypothalamus optimum when the CPG is added into the basal ganglia. The results could have important implications for biological processes which are about interaction between the neural network and the CPG.

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“…23 In Ref. 9 we have proposed a model for odor suppression in gas sensor arrays through local habituation of KIII channels. In that earlier model, a statistical preprocessing stage was used to orthogonalize odor patterns, thus facilitating the segmentation process.…”

Section: Background Suppression Through Contrast Enhancement and Habimentioning

confidence: 99%

“…Habituation parameters B and t are borrowed from Ref. 9. Figure 6 shows the response of the KIII model in a simple habituation scenario using the synthetic patterns in Figure 3a.…”

Section: Background Suppression Through Contrast Enhancement and Habimentioning

confidence: 99%

“…Otto has applied the KIII to process data from FTIR and chemical sensors [6][7][8] using habituation and hebbian learning. Our own prior work on the KIII model 9 has investigated the use of local habituation to depress the activity of channels that receive sustained input, allowing the model to segment odors into their individual components. In that case, however, the KIII relied on a statistical preprocessing stage to orthogonalize sensor patterns.…”

Section: Introductionmentioning

confidence: 99%

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Contrast enhancement and background suppression of chemosensor array patterns with the KIII model

Gutiérrez-Gálvez

Gutierrez‐Osuna

2006

Int. J. Intell. Syst.

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Inspired by the ability of the olfactory bulb to enhance the contrast between odor representations, we propose a new hebbian learning rule that is able to increase the separability of odor patterns from gas sensor arrays. The proposed learning rule employs a hebbian term to build associations within odors and an anti-hebbian term to reduce correlated activity across odors. In addition to increasing the separability of patterns, the new learning rule can also achieve odor background suppression when combined with a habituation term. These two functions are demonstrated on Freeman's KIII, a neurodynamics model of the olfactory system. The system is first characterized on synthetic data, and also validated on experimental data from an array of chemical sensors exposed to organic solvents.

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Habituation in the kiii olfactory model with chemical sensor arrays

Cited by 28 publications

References 15 publications

Chaotic Neurodynamics for Autonomous Agents

Chaotic Neurodynamics for Autonomous Agents

Effects on hypothalamus when CPG is fed back to basal ganglia based on KIV model

Contrast enhancement and background suppression of chemosensor array patterns with the KIII model

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