Multi-Layer Perceptron with positive and negative pulse glial chain for solving two-spirals problem

Ikuta, Chihiro; Uwate, Yoko; Nishio, Yoshifumi

doi:10.1109/ijcnn.2012.6252725

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…Moreover, comparing to similar proposed models, our algorithm was tested with different number of hidden layers and neurons per layer, while C. Ikuta et al [10][11][12][13][14] did perform in only one topology.…”

Section: Discussionmentioning

confidence: 99%

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Self organizing neuro-glial network, SONG-Net

Landolsi

Marzouki

2014

2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)

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More convincing evidence has proven the existence of a bidirectional relationship between neurons and astrocytes. Assume now that astrocytes, a new type of glial cells previously considered as passive cells of support, constitute a system of nonsynaptic transmission plays a major role in modulating the activity of neurons. In this context, we proposed to model the effect of these cells to develop a new type of artificial neural network operating on new mechanisms to improve the information processing and reduce learning time, very expensive in traditional networks. The obtained results indicate that the implementation of bio-inspired functions such as of astrocytes, improve very considerably learning speed.The developed model achieves learning up to twelve times faster than traditional artificial neural networks.

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Section: Discussionmentioning

confidence: 99%

“…Finally, in [14], C. Ikuta et al proposed the MLP with positive and negative pulse glial chain which is inspired from features of the biological glia. They added the MLP to the positive and negative pulse glial chain.…”

Section: Related Workmentioning

confidence: 99%

Self organizing neuro-glial network, SONG-Net

Landolsi

Marzouki

2014

2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)

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“…Then, in [15][16][17][18][19][20][21] the authors propose a modification of their approach proposed in [13] and investigate the performance and the features of this approach. These modifications are inspired from glial cells observed properties.…”

Section: Related Workmentioning

confidence: 99%

Modeling Astrocyte-Neuron Interactions

Chikha

Marzouki

Ahmed

2015

Neural Information Processing

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Abstract. The involvement of astrocytes in information processing in the brain has recently been demonstrated. In this paper, we investigate, using computational models (SOM and MLP), one of the observed astrocyte-neuron interactions for information processing: the neural modulation represented by the observed calcium waves. We apply it to solve classification problems. The results of the performed tests confirmed that the proposed approach improved artificial neural network performance, especially learning time acceleration.

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“…As a special nerve cell in human brain, glia cell can transmit signals to neurons and other glia cells. Therefore, researchers paid attention to the characteristics of glia cells and applied it to the artificial neural networks [30, 31]. In the framework, raw multidomain features are obtained from multichannel EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework

Hao

Huang

Dong

et al. 2018

Computational Intelligence and Neuroscience

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Fusing multichannel neurophysiological signals to recognize human emotion states becomes increasingly attractive. The conventional methods ignore the complementarity between time domain characteristics, frequency domain characteristics, and time-frequency characteristics of electroencephalogram (EEG) signals and cannot fully capture the correlation information between different channels. In this paper, an integrated deep learning framework based on improved deep belief networks with glia chains (DBN-GCs) is proposed. In the framework, the member DBN-GCs are employed for extracting intermediate representations of EEG raw features from multiple domains separately, as well as mining interchannel correlation information by glia chains. Then, the higher level features describing time domain characteristics, frequency domain characteristics, and time-frequency characteristics are fused by a discriminative restricted Boltzmann machine (RBM) to implement emotion recognition task. Experiments conducted on the DEAP benchmarking dataset achieve averaged accuracy of 75.92% and 76.83% for arousal and valence states classification, respectively. The results show that the proposed framework outperforms most of the above deep classifiers. Thus, potential of the proposed framework is demonstrated.

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Multi-Layer Perceptron with positive and negative pulse glial chain for solving two-spirals problem

Cited by 12 publications

References 10 publications

Self organizing neuro-glial network, SONG-Net

Self organizing neuro-glial network, SONG-Net

Modeling Astrocyte-Neuron Interactions

Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework

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