Neuromodulated Dopamine Plastic Networks for Heterogeneous Transfer Learning with Hebbian Principle

The plastic modifications in synaptic connectivity is primarily from changes triggered by neuromodulated dopamine signals. These activities are controlled by neuromodulation, which is itself under the control of the brain. The subjective brain’s self-modifying abilities play an essential role in learning and adaptation. The artificial neural networks with neuromodulated plasticity are used to implement transfer learning in the image classification domain. In particular, this has application in image detection, image segmentation, and transfer of learning parameters with significant results. This paper proposes a novel approach to enhance transfer learning accuracy in a heterogeneous source and target, using the neuromodulation of the Hebbian learning principle, called NDHTL (Neuromodulated Dopamine Hebbian Transfer Learning). Neuromodulation of plasticity offers a powerful new technique with applications in training neural networks implementing asymmetric backpropagation using Hebbian principles in transfer learning motivated CNNs (Convolutional neural networks). Biologically motivated concomitant learning, where connected brain cells activate positively, enhances the synaptic connection strength between the network neurons. Using the NDHTL algorithm, the percentage of change of the plasticity between the neurons of the CNN layer is directly managed by the dopamine signal’s value. The discriminative nature of transfer learning fits well with the technique. The learned model’s connection weights must adapt to unseen target datasets with the least cost and effort in transfer learning. Using distinctive learning principles such as dopamine Hebbian learning in transfer learning for asymmetric gradient weights update is a novel approach. The paper emphasizes the NDHTL algorithmic technique as synaptic plasticity controlled by dopamine signals in transfer learning to classify images using source-target datasets. The standard transfer learning using gradient backpropagation is a symmetric framework. Experimental results using CIFAR-10 and CIFAR-100 datasets show that the proposed NDHTL algorithm can enhance transfer learning efficiency compared to existing methods.

Download Full-text

Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells

10.2118/206537-ms ◽

2021 ◽

Author(s):

Ildar Radikovich Abdrakhmanov ◽

Evgenii Alekseevich Kanin ◽

Sergei Andreevich Boronin ◽

Evgeny Vladimirovich Burnaev ◽

Andrei Aleksandrovich Osiptsov

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Multivariate Time Series ◽

Operating Regime ◽

Oil Wells ◽

Well Testing ◽

Novel Approach ◽

Production History ◽

Single Well ◽

Bottomhole Pressure

Abstract We propose a novel approach to data-driven modeling of a transient production of oil wells. We apply the transformer-based neural networks trained on the multivariate time series composed of various parameters of oil wells measured during their exploitation. By tuning the machine learning models for a single well (ignoring the effect of neighboring wells) on the open-source field datasets, we demonstrate that transformer outperforms recurrent neural networks with LSTM/GRU cells in the forecasting of the bottomhole pressure dynamics. We apply the transfer learning procedure to the transformer-based surrogate model, which includes the initial training on the dataset from a certain well and additional tuning of the model's weights on the dataset from a target well. Transfer learning approach helps to improve the prediction capability of the model. Next, we generalize the single-well model based on the transformer architecture for multiple wells to simulate complex transient oilfield-level patterns. In other words, we create the global model which deals with the dataset, comprised of the production history from multiple wells, and allows for capturing the well interference resulting in more accurate prediction of the bottomhole pressure or flow rate evolutions for each well under consideration. The developed instruments for a single-well and oilfield-scale modelling can be used to optimize the production process by selecting the operating regime and submersible equipment to increase the hydrocarbon recovery. In addition, the models can be helpful to perform well-testing avoiding costly shut-in operations.

Download Full-text

Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle

Applied Sciences ◽

10.3390/app10165631 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5631 ◽

Cited By ~ 1

Author(s):

Arjun Magotra ◽

Juntae Kim

Keyword(s):

Image Classification ◽

Transfer Learning ◽

Hebbian Learning ◽

Learning Rule ◽

Transfer Of Knowledge ◽

Learning Efficiency ◽

Simultaneous Activation ◽

Classification Of Images ◽

The Individual ◽

Heterogeneous Source

Transfer learning algorithms have been widely studied for machine learning in recent times. In particular, in image recognition and classification tasks, transfer learning has shown significant benefits, and is getting plenty of attention in the research community. While performing a transfer of knowledge among source and target tasks, homogeneous dataset is not always available, and heterogeneous dataset can be chosen in certain circumstances. In this article, we propose a way of improving transfer learning efficiency, in case of a heterogeneous source and target, by using the Hebbian learning principle, called Hebbian transfer learning (HTL). In computer vision, biologically motivated approaches such as Hebbian learning represent associative learning, where simultaneous activation of brain cells positively affect the increase in synaptic connection strength between the individual cells. The discriminative nature of learning for the search of features in the task of image classification fits well to the techniques, such as the Hebbian learning rule—neurons that fire together wire together. The deep learning models, such as convolutional neural networks (CNN), are widely used for image classification. In transfer learning, for such models, the connection weights of the learned model should adapt to new target dataset with minimum effort. The discriminative learning rule, such as Hebbian learning, can improve performance of learning by quickly adapting to discriminate between different classes defined by target task. We apply the Hebbian principle as synaptic plasticity in transfer learning for classification of images using a heterogeneous source-target dataset, and compare results with the standard transfer learning case. Experimental results using CIFAR-10 (Canadian Institute for Advanced Research) and CIFAR-100 datasets with various combinations show that the proposed HTL algorithm can improve the performance of transfer learning, especially in the case of a heterogeneous source and target dataset.

Download Full-text

The Concept of Metal-Insulator-Metal Nanostructures as Adaptive Neural Networks

U Porto Journal of Engineering ◽

10.24840/2183-6493_003.001_0001 ◽

2018 ◽

Vol 3 (1) ◽

pp. 1-10

Author(s):

Catarina Dias ◽

Luís M. Guerra ◽

Paulo Aguiar ◽

João Ventura

Keyword(s):

Neural Networks ◽

Computer Processing ◽

Metal Nanostructures ◽

Nonlinear Relationship ◽

Metal Insulator ◽

Von Neumann ◽

Metal Insulator Metal ◽

Learning And Adaptation ◽

The Brain ◽

Current History

Present computer processing capabilities are becoming a restriction to meet modern technological needs. Therefore, approaches beyond the von Neumann computational architecture are imperative and the brain operation and structure are truly attractive models. Memristors are characterized by a nonlinear relationship between current history and voltage and were shown to present properties resembling those of biological synapses. Here, the use of metal-insulator-metal-based memristive devices in neural networks capable of simulating the learning and adaptation features present in mammal brains is discussed.

Download Full-text

General differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain

PLoS Computational Biology ◽

10.1371/journal.pcbi.1006227 ◽

2018 ◽

Vol 14 (8) ◽

pp. e1006227 ◽

Cited By ~ 4

Author(s):

Stefano Zappacosta ◽

Francesco Mannella ◽

Marco Mirolli ◽

Gianluca Baldassarre

Keyword(s):

Neural Networks ◽

Hebbian Learning ◽

Temporal Relations ◽

The Brain

Download Full-text

Brain-Mediated Transfer Learning of Convolutional Neural Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5974 ◽

2020 ◽

Vol 34 (04) ◽

pp. 5281-5288 ◽

Cited By ~ 2

Author(s):

Satoshi Nishida ◽

Yusuke Nakano ◽

Antoine Blanc ◽

Naoya Maeda ◽

Masataka Kado ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Human Brain ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Individual Variability ◽

Generalization Ability ◽

Feature Representations ◽

And Behavior ◽

The Brain

The human brain can effectively learn a new task from a small number of samples, which indicates that the brain can transfer its prior knowledge to solve tasks in different domains. This function is analogous to transfer learning (TL) in the field of machine learning. TL uses a well-trained feature space in a specific task domain to improve performance in new tasks with insufficient training data. TL with rich feature representations, such as features of convolutional neural networks (CNNs), shows high generalization ability across different task domains. However, such TL is still insufficient in making machine learning attain generalization ability comparable to that of the human brain. To examine if the internal representation of the brain could be used to achieve more efficient TL, we introduce a method for TL mediated by human brains. Our method transforms feature representations of audiovisual inputs in CNNs into those in activation patterns of individual brains via their association learned ahead using measured brain responses. Then, to estimate labels reflecting human cognition and behavior induced by the audiovisual inputs, the transformed representations are used for TL. We demonstrate that our brain-mediated TL (BTL) shows higher performance in the label estimation than the standard TL. In addition, we illustrate that the estimations mediated by different brains vary from brain to brain, and the variability reflects the individual variability in perception. Thus, our BTL provides a framework to improve the generalization ability of machine-learning feature representations and enable machine learning to estimate human-like cognition and behavior, including individual variability.

Download Full-text