Characterization of Generalizability of Spike Timing Dependent Plasticity Trained Spiking Neural Networks

Chakraborty, Biswadeep; Mukhopadhyay, Saibal

doi:10.3389/fnins.2021.695357

Cited by 9 publications

(6 citation statements)

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“…This principle highlights the intricate dance between time and neural activity, showcasing the dynamics of our neural circuits. This biologically plausible learning rule is a timing-dependent specialization of Hebbian learning (13) [68]. STDP sheds light on the intricate interplay between timing and synaptic modification.…”

Section: Spike Timing Dependent Plasticitymentioning

confidence: 99%

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Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka,

Szczepanski,

Pregowska

2024

Electronics

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Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed.

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Section: Spike Timing Dependent Plasticitymentioning

confidence: 99%

“…where a(W) is a scaling function that determines the weight dependence, while τ denotes the time constant for depression [66][67][68][69]. STDP's significance is underpinned by its numerous advantages.…”

Section: Spike Timing Dependent Plasticitymentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka,

Szczepanski,

Pregowska

2024

Electronics

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“…Unsupervised learning models like STDP have shown great generalization, and trainability properties (Chakraborty and Mukhopadhyay, 2021 ). Previous works have used STDP for training the recurrent spiking networks (Gilson et al, 2010 ).…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous recurrent spiking neural network for spatio-temporal classification

Chakraborty

Mukhopadhyay

2023

Front. Neurosci.

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Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagation show classification accuracy comparable to deep networks, the performance of unsupervised learning-based SNNs remains much lower. This paper presents a heterogeneous recurrent spiking neural network (HRSNN) with unsupervised learning for spatio-temporal classification of video activity recognition tasks on RGB (KTH, UCF11, UCF101) and event-based datasets (DVS128 Gesture). We observed an accuracy of 94.32% for the KTH dataset, 79.58% and 77.53% for the UCF11 and UCF101 datasets, respectively, and an accuracy of 96.54% on the event-based DVS Gesture dataset using the novel unsupervised HRSNN model. The key novelty of the HRSNN is that the recurrent layer in HRSNN consists of heterogeneous neurons with varying firing/relaxation dynamics, and they are trained via heterogeneous spike-time-dependent-plasticity (STDP) with varying learning dynamics for each synapse. We show that this novel combination of heterogeneity in architecture and learning method outperforms current homogeneous spiking neural networks. We further show that HRSNN can achieve similar performance to state-of-the-art backpropagation trained supervised SNN, but with less computation (fewer neurons and sparse connection) and less training data.

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“…presynaptic and postsynaptic neurons emit a high rate) and depression (LTD, i.e. presynaptic neurons emit a high rate) in the time window 𝒕 𝒑𝒓𝒆 − 𝒕 𝒑𝒐𝒔𝒕 [ ) for 𝒕 𝒑𝒓𝒆 − 𝒕 𝒑𝒐𝒔𝒕 > 𝟎 (17) where 𝒂(𝑾) is a scaling function that determines the weight dependence, while 𝝉 denotes the time constant for depression [61][62][63]. STDP's significance is underpinned by its numerous advantages.…”

Section: Spike Timing Dependent Plasticitymentioning

confidence: 99%

“…where 𝒂(𝑾) is a scaling function that determines the weight dependence, while 𝝉 denotes the time constant for depression [61][62][63]. STDP's significance is underpinned by its numerous advantages.…”

Section: Spike Timing Dependent Plasticitymentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Rudnicka,

Szczepanski,

Pregowska

2023

Preprint

View full text Add to dashboard Cite

Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

show abstract

Characterization of Generalizability of Spike Timing Dependent Plasticity Trained Spiking Neural Networks

Cited by 9 publications

References 50 publications

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Heterogeneous recurrent spiking neural network for spatio-temporal classification

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

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