Effects of ion channel blocks on electrical activity of stochastic Hodgkin–Huxley neural network under electromagnetic induction

Abstract. Cloud to ground and cloud to cloud lightning flashes pose a threat to the aircraft body and the electronic systems inside the aircraft. In this paper we present a single unit, as opposed to a three unit, lightning locator mounted on the aircraft that uses the wave-shapes of electromagnetic fields radiated by lightning and electrical activity ahead of the aircraft to locate the distance range of lightning activity. A three element array antenna scans the area ahead of the aircraft to narrow down the area ahead where the lightning or threatening electrical activity is. Moreover, the unique shape of the electric fields depending on the distance from the lightning activity is used by a neural network to train and recognize the distance range of the lightning activity from the aircraft on which the lightning detector is mounted. The combined use of the three element array antenna and the neural network provides the required knowledge of lightning activity for the pilot to take evasive action.

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Mechanisms of Modulation of a Neural Network

Physiology ◽

10.1152/physiologyonline.2000.15.4.186 ◽

2000 ◽

Vol 15 (4) ◽

pp. 186-191 ◽

Cited By ~ 3

Author(s):

Abdeljabbar El Manira ◽

Peter Wallén

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Analysis ◽

Ion Channel ◽

Patterns Of Behavior ◽

And Control

Neural networks form the basis for the generation and control of various patterns of behavior. Such networks are subjected to modulatory systems that influence their operation and, thereby, the behavior. In the lamprey locomotor network, analysis on the ion channel, synaptic, and cellular levels has given new insights into the organization of such modulatory systems.

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Neural network based forward prediction of bladder pressure using pudendal nerve electrical activity

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2015.7319454 ◽

2015 ◽

Cited By ~ 3

Author(s):

A. Geramipour ◽

S. Makki ◽

A. Erfanian

Keyword(s):

Neural Network ◽

Electrical Activity ◽

Pudendal Nerve ◽

Bladder Pressure ◽

Forward Prediction

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Chaotic Resonance in Izhikevich Neural Network Motifs Under Electromagnetic Induction

10.21203/rs.3.rs-895256/v1 ◽

2021 ◽

Author(s):

Guowei Wang ◽

Lijian Yang ◽

Xuan Zhan ◽

Anbang Li ◽

Ya Jia

Keyword(s):

Neural Network ◽

Time Delay ◽

Electromagnetic Induction ◽

Current Intensity ◽

Signal Frequency ◽

Network Motifs ◽

Neuronal System ◽

Weak Signal ◽

Weak Signals ◽

The Impact

Abstract Chaotic resonance (CR) is the response of a nonlinear system to weak signals enhanced by internal or external chaotic activity (such as the signal derived from Lorenz system). In this paper, the triple-neuron feed-forward loop (FFL) Izhikevich neural network motifs with eight types are constructed as the nonlinear systems, and the effects of EMI on CR phenomenon in FFL neuronal network motifs are studied. It is found that both the single Izhikevich neural model under electromagnetic induction (EMI) and its network motifs exhibit CR phenomenon depending on the chaotic current intensity. There exists an optimal chaotic current intensity ensuring the best detection of weak signal in single Izhikevich neuron or its network motifs via CR. The EMI can enhance the ability of neuron to detect weak signals. For T1-FFL and T2-FFL motifs, the adjustment of EMI parameters makes T2-FFL show a more obvious CR phenomenon than that for T1-FFL motifs, which is different from the impact of system parameters (e.g., the weak signal frequency, the coupling strength, and the time delay) on CR. Another interesting phenomenon is that the variation of CR with time delay exhibits quasi periodic characteristics. Our results showed that CR effect is a robust phenomenon which is observed in both single Izhikevich neuron and network motifs, which might help one understand how to improve the ability of weak signal detection and propagation in neuronal system.

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Model Prediksi Interaksi Senyawa dan Protein untuk Drug Repositioning menggunakan Deep Semi-Supervised Learning

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020742236 ◽

2020 ◽

Vol 7 (4) ◽

pp. 727

Author(s):

Larasati Larasati ◽

Wisnu Ananta Kusuma ◽

Annisa Annisa

Keyword(s):

Neural Network ◽

Ion Channel ◽

Unsupervised Learning ◽

Supervised Learning ◽

Nuclear Receptor ◽

Deep Neural Network ◽

Drug Repositioning ◽

Imbalanced Data ◽

Unlabeled Data ◽

F Measure

Drug repositioning adalah penggunaan senyawa obat yang sudah lolos uji sebelumnya untuk mengatasi penyakit baru selain penyakit awal obat tersebut ditujukan. Drug repositioning dapat dilakukan dengan memprediksi interaksi senyawa obat dengan protein penyakit yang bereaksi positif. Salah satu tantangan dalam prediksi interaksi senyawa dan protein adalah masalah ketidakseimbangan data. Deep semi-supervised learning dapat menjadi alternatif untuk menangani model prediksi dengan data yang tidak seimbang. Proses pre-training berbasis unsupervised learning pada deep semi-supervised learning dapat merepresentasikan input dari unlabeled data (data mayoritas) dengan baik dan mengoptimasi inisialisasi bobot pada classifier. Penelitian ini mengimplementasikan Deep Belief Network (DBN) sebagai pre-training dan Deep Neural Network (DNN) sebagai classifier. Data yang digunakan pada penelitian ini adalah dataset ion channel, GPCR, dan nuclear receptor yang bersumber dari pangkalan data KEGG BRITE, BRENDA, SuperTarget, dan DrugBank. Hasil penelitian ini menunjukkan pada dataset tersebut, pre-training berupa ekstraksi fitur memberikan efek optimasi dilihat dari peningkatan performa model DNN pada akurasi (3-4.5%), AUC (4.5%), precision (5.9-6%), dan F-measure (3.8%). AbstractDrug repositioning is the reuse of an existing drug to treat a new disease other than its original medical indication. Drug repositioning can be done by predicting the interaction of drug compounds with disease proteins that react positively. One of the challenges in predicting the interaction of compounds and proteins is imbalanced data. Deep semi-supervised learning can be an alternative to handle prediction models with imbalanced data. The unsupervised learning based pre-training process in deep semi-supervised learning can represent input from unlabeled data (majority data) properly and optimize initialization of weights on the classifier. This study implements the Deep Belief Network (DBN) as a pre-training with Deep Neural Network (DNN) as a classifier. The data used in this study are ion channel, GPCR, and nuclear receptor dataset sourced from KEGG BRITE, BRENDA, SuperTarget, and DrugBank databases. The results of this study indicate that pre-training as feature extraction had an optimization effect. This can be seen from DNN performance improvement in accuracy (3-4.5%), AUC (4.5%), precision (5.9-6%), and F-measure (3.8%).

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