Browsing by Subject "Neurons"
Now showing items 1-12 of 12
-
A Neural Field Theory for Loss of Consciousness: Synaptic Drive Dynamics, System Stability, Attractors, Partial Synchronization, and Hopf Bifurcations Characterizing the Anesthetic Cascade
( Elsevier Inc. , 2016 , Book chapter)With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the ... -
Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks
( Institute of Electrical and Electronics Engineers Inc. , 2021 , Article)Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine ... -
Exploiting heterogeneity in operational neural networks by synaptic plasticity
( Springer Science and Business Media Deutschland GmbH , 2021 , Article)The recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network ... -
Generalized Operational Classifiers for Material Identification
( Institute of Electrical and Electronics Engineers Inc. , 2020 , Conference Paper)Material is one of the intrinsic features of objects, and consequently material recognition plays an important role in image understanding. The same material may have various shapes and appearance, while keeping the same ... -
Heterogeneous Multilayer Generalized Operational Perceptron
( Institute of Electrical and Electronics Engineers Inc. , 2020 , Article)The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, generalized ... -
Operational neural networks
( Springer , 2020 , Article)Feed-forward, fully connected artificial neural networks or the so-called multi-layer perceptrons are well-known universal approximators. However, their learning performance varies significantly depending on the function ... -
Partial synchronization of biological neural networks and the anesthetic cascade
( Institute of Electrical and Electronics Engineers Inc. , 2015 , Conference Paper)With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the ... -
Progressive Operational Perceptrons with Memory
( Elsevier B.V. , 2020 , Article)Generalized Operational Perceptron (GOP) was proposed to generalize the linear neuron model used in the traditional Multilayer Perceptron (MLP) by mimicking the synaptic connections of biological neurons showing nonlinear ... -
Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks
( IEEE Computer Society , 2021 , Article)Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ... -
Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks
( Institute of Electrical and Electronics Engineers Inc. , 2022 , Article)Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) ... -
Self-organized operational neural networks for severe image restoration problems
( Elsevier Ltd , 2021 , Article)Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by learning from training examples of noisy-clean image pairs. It has become the go-to methodology for tackling image ... -
Self-organized Operational Neural Networks with Generative Neurons
( Elsevier Ltd , 2021 , Article)Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional Neural Networks (CNNs) such as network homogeneity with the sole linear neuron ...