Review of State-of-the-Art in Deep Learning Artificial Intelligence

Shakirov, Vladimir; Solovyeva, Ksenia P.; Dunin-Barkowski, Witali L.

doi:10.3103/s1060992x18020066

Cited by 19 publications

(5 citation statements)

References 8 publications

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“…This is a small but important step in ongoing research on the development of brain-inspired artificial intelligence. For instance, the performance and accuracy of neuromorphic computing implemented by spiking neural networks are still behind modern deeplearning networks in most learning tasks [64]. Along with the desire to understand how our brains work, the main reason for the intensified ongoing research efforts in designing brain-like hardware systems that implement neuronal and synaptic computations through spike-driven communication is that it can enable energy-efficient machine intelligence [65].…”

Section: Discussionmentioning

confidence: 99%

Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network

Tsybina

Kastalskiy

Krivonosov

et al. 2022

Neural Comput & Applic

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Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. In this paper, we propose a mathematical model of a spiking neural network (SNN) which simulates the way a fragment of information is maintained as a robust activity pattern for several seconds and the way it completely disappears if no other stimuli are fed to the system. Such short-term memory traces are preserved due to the activation of astrocytes accompanying the SNN. The astrocytes exhibit calcium transients at a time scale of seconds. These transients further modulate the efficiency of synaptic transmission and, hence, the firing rate of neighboring neurons at diverse timescales through gliotransmitter release. We demonstrate how such transients continuously encode frequencies of neuronal discharges and provide robust short-term storage of analogous information. This kind of short-term memory can store relevant information for seconds and then completely forget it to avoid overlapping with forthcoming patterns. The SNN is inter-connected with the astrocytic layer by local inter-cellular diffusive connections. The astrocytes are activated only when the neighboring neurons fire synchronously, e.g., when an information pattern is loaded. For illustration, we took grayscale photographs of people’s faces where the shades of gray correspond to the level of applied current which stimulates the neurons. The astrocyte feedback modulates (facilitates) synaptic transmission by varying the frequency of neuronal firing. We show how arbitrary patterns can be loaded, then stored for a certain interval of time, and retrieved if the appropriate clue pattern is applied to the input.

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Section: Discussionmentioning

confidence: 99%

Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network

Tsybina

Kastalskiy

Krivonosov

et al. 2022

Neural Comput & Applic

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“…This is a small but important step in ongoing research on the development of the braininspired artificial intelligence. Practically, the performance, for example, in terms, of the accuracy of neuromorphic computing implemented by spiking neuronal networks is still behind modern deep-learning networks in most learning tasks (Shakirov et al, 2018). The main reason for the intensified ongoing research efforts in designing brain-like hardware systems that implement neuronal and synaptic computations through spike-driven communication besides the understanding of brain mechanisms is that it can enable energy-efficient machine intelligence (Roy et al, 2019).…”

Section: Gap Junctionmentioning

confidence: 99%

Astrocytes mediate analogous memory in a multi-layer neuron-astrocytic network

Tsybina¹,

Kastalskiy²,

Krivonosov³

et al. 2021

Preprint

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Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. Here we propose a mathematical model of spiking neuron network (SNN) demonstrating how a piece of information can be maintained as a robust activity pattern for several seconds then completely disappear if no other stimuli come. Such short-term memory traces are preserved due to the activation of astrocytes accompanying the SNN. The astrocytes exhibit calcium transients at a time scale of seconds. These transients further modulate the efficiency of synaptic transmission and, hence, the firing rate of neighboring neurons at diverse timescales through gliotransmitter release. We show how such transients continuously encode frequencies of neuronal discharges and provide robust short-term storage of analogous information. This kind of short-term memory can keep operative information for seconds, then completely forget it to avoid overlapping with forthcoming patterns. The SNN is inter-connected with the astrocytic layer by local inter-cellular diffusive connections. The astrocytes are activated only when the neighboring neurons fire quite synchronously, e.g. when an information pattern is loaded. For illustration, we took greyscale photos of people's faces where the grey level encoded the level of applied current stimulating the neurons. The astrocyte feedback modulates (facilitates) synaptic transmission by varying the frequency of neuronal firing. We show how arbitrary patterns can be loaded, then stored for a certain interval of time, and retrieved if the appropriate clue pattern is applied to the input.

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“…Therefore, this research seeks a deep learning based approach in alliances with ML models to classify credit card transaction. Some of these learning models approach were elusively deliberated [29][30][31][32]. As this study proposes a Deep Convolutional Neural Network (DCNN) method as potential solution towards the mitigation of the inferences of credit card fraud against financial institutions.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural network based synthetic minority over sampling technique: a forfending model for fraudulent credit card transactions in financial institution

Salaudeen,

GABI,

Garba

et al. 2024

J. Nig. Soc. Phys. Sci.

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Fraudulent credit card transactions are committed by unauthorized individuals and organizations employing methods such as phishing and social engineering fraud tactics. Researchers propose several Machine Learning (ML) techniques to deter the challenges of credit card fraud. However, the ML approaches are endorsed with some challenges, which makes the detection of credit card fraud extremely difficult. This study proposes a Deep Convolutional Neural Network (DCNN) with Synthetic Minority Oversampling Techniques (SMOTE) as an ideal solution. Kaggle datasets with 284,807 records and 31 features were exploited. Implementation was performed on the Google Colab cloud-based platform, embedding a Jupyter notebook setting with Graphical Processing Units (GPUs). Two experiments were conducted; the first was probed to determine suitable models among baseline models: Logistic Regression (LR), Random Forest (RF), Isolation Forest, and a single Deep Learning (DL) model of Multiple Layer Perceptron (MLP). The baseline models yielded an overfitting accuracy score, with recall, specificity, precision, and F1-score all presenting 1.00% respectively. This outcome is not sufficient in establishing findings on imbalanced data distribution as it's biased. This led to the construction of a new ML model incorporating Light Gradient Boosting Machine (LGBM), with Artificial Neural Network (ANN) and the proposed DCNN+SMOTE for the second experimental phase alongside baseline models. Experimental results via simulation show the proposed DCNN+SMOTE yielded awesome superclass performance across the board, displaying 1.00% results respectively. Its Error Rate (ER) and Null Error Rate (NER) are 0.00% distinctly. Meanwhile, the False Positive Rate (FPR) yields a 0.001% result, lesser and better than the baseline models.

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Review of State-of-the-Art in Deep Learning Artificial Intelligence

Cited by 19 publications

References 8 publications

Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network

Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network

Astrocytes mediate analogous memory in a multi-layer neuron-astrocytic network

Deep convolutional neural network based synthetic minority over sampling technique: a forfending model for fraudulent credit card transactions in financial institution

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