An Analysis of Synaptic Transmission and its Plasticity by Glutamate Receptor Channel Kinetics Models and 2-Photon Laser Photolysis

Kojima, Hiroshi; Katsumata, Shiori

doi:10.1007/978-3-642-02490-0_11

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…An increased knowledge in the information processing of the biological neurons helped in explaining many additional parameters (like the gene and the protein expression) that needed to be taken into consideration for the neurons to spike [33][34][35]. The additional parameters included the different physical properties of the connections [32], the likelihood of the spikes being accepted at the synapse and the emitted neurotransmitters or the open-ion channels [36,37].…”

Section: Spiking Neural Networkmentioning

confidence: 99%

Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis

Ali¹

2018

Artificial Intelligence - Emerging Trends and Applications

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The subject of machine condition monitoring and fault diagnosis as a part of system maintenance has gained a lot of interest due to the potential benefits to be learned from reduced maintenance budgets, enhanced productivity and improved machine availability. Artificial intelligence (AI) is a successful method of machine condition monitoring and fault diagnosis since these techniques are used as tools for routine maintenance. This chapter attempts to summarize and review the recent research and developments in the field of signal analysis through artificial intelligence in machine condition monitoring and fault diagnosis. Intelligent systems such as artificial neural network (ANN), fuzzy logic system (FLS), genetic algorithms (GA) and support vector machine (SVM) have previously developed many different methods. However, the use of acoustic emission (AE) signal analysis and AI techniques for machine condition monitoring and fault diagnosis is still rare. In the future, the applications of AI in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature.

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Section: Spiking Neural Networkmentioning

confidence: 99%

Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis

Ali¹

2018

Artificial Intelligence - Emerging Trends and Applications

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“…The neuron spikes if the PSP is higher than a Threshold value, which is calculated using (5). It depends on the value of a parameter called Proportion Factor ( C ):…”

Section: The Proposed Extended Esnn (Eesnn) Model For Std Classifmentioning

confidence: 99%

“…However, more biological inspired methods have been introduced and have received tremendous attentions in regard to solving STP. In biological neurons, whether a neuron spikes or not at any given time may depend not only on input signals, but also on parameters such as gene and protein expression [5], the physical properties of connections [6], the probabilities of spikes being received at the synapses and the emitted neuro-transmitters or open ion channels. Many of these properties have been mathematically modeled and used to study biological neurons [7], but have not been fully utilized for the creation of more efficient ANN to solve complex STP.…”

Section: Introductionmentioning

confidence: 99%

An extended Evolving Spiking Neural Network model for spatio-temporal pattern classification

Hamed

Kasabov

Shamsuddin

et al. 2011

The 2011 International Joint Conference on Neural Networks

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This paper proposes a new model of an Evolving Spiking Neural Network (ESNN) for spatio-temporal data (STD) classification problems. The proposed ESNN model incorporates an additional layer for capturing both spatial and temporal components of the STD and then transforms them into high dimensional spiking patterns. These patterns are learned and classified in the evolving classification layer of the ESNN. A fast time-to-first-spike learning algorithm is used that enables the new model to be more suitable for learning from the STD streams in an adaptive and incremental manner. The proposed method is evaluated on a benchmark sign language video that is spatio-temporal in nature. The results show that the proposed method is able to capture important spatio-temporal information from the STD stream. This results in significantly higher classification accuracy than the traditional time-delay MLP neural network model. Future directions for the development of ESNN models for STD are discussed.

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“…In biological systems, neuronal spike generation is inherently stochastic [ 14 ]. This process is influenced not solely by signal transmission but also by factors such as ion channel states [ 15 ], protein synthesis [ 16 ], degradation, and specific physical connection characteristics [ 17 ]. Therefore, to align with biological principles, it is crucial to consider the uncertainty in neural signal transmission within neural network models.…”

Section: Introductionmentioning

confidence: 99%

A Bio-Inspired Probabilistic Neural Network Model for Noise-Resistant Collision Perception

Hong,

Sun,

Peng

et al. 2024

Biomimetics

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Bio-inspired models based on the lobula giant movement detector (LGMD) in the locust’s visual brain have received extensive attention and application for collision perception in various scenarios. These models offer advantages such as low power consumption and high computational efficiency in visual processing. However, current LGMD-based computational models, typically organized as four-layered neural networks, often encounter challenges related to noisy signals, particularly in complex dynamic environments. Biological studies have unveiled the intrinsic stochastic nature of synaptic transmission, which can aid neural computation in mitigating noise. In alignment with these biological findings, this paper introduces a probabilistic LGMD (Prob-LGMD) model that incorporates a probability into the synaptic connections between multiple layers, thereby capturing the uncertainty in signal transmission, interaction, and integration among neurons. Comparative testing of the proposed Prob-LGMD model and two conventional LGMD models was conducted using a range of visual stimuli, including indoor structured scenes and complex outdoor scenes, all subject to artificial noise. Additionally, the model’s performance was compared to standard engineering noise-filtering methods. The results clearly demonstrate that the proposed model outperforms all comparative methods, exhibiting a significant improvement in noise tolerance. This study showcases a straightforward yet effective approach to enhance collision perception in noisy environments.

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An Analysis of Synaptic Transmission and its Plasticity by Glutamate Receptor Channel Kinetics Models and 2-Photon Laser Photolysis

Cited by 5 publications

References 9 publications

Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis

Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis

An extended Evolving Spiking Neural Network model for spatio-temporal pattern classification

A Bio-Inspired Probabilistic Neural Network Model for Noise-Resistant Collision Perception

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