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Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities
Italian National Conference on Sensors, 2023 Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more interest nowadays. They more closely resemble actual neural networks in the brain than their second-generation counterparts, artificial neural networks (ANNs). SNNs have
Pawel Pietrzak +3 more
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IEEE Access, 2022 In recent years, the use of artificial neural network applications to perform object classification and event prediction has increased, mainly from research about deep learning techniques running on hardware such as GPU and FPGA.
Francisco De Assis Pereira Januario +1 more
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Fractional diffusion theory of balanced heterogeneous neural networks
Physical Review Research, 2021 Interactions of large numbers of spiking neurons give rise to complex neural dynamics with fluctuations occurring at multiple scales. Understanding the dynamical mechanisms underlying such complex neural dynamics is a long-standing topic of interest in ...
Asem Wardak, Pulin Gong
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Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons
Sensors, 2023 A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems.
Geunbo Yang +7 more
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Function approximation with uncertainty propagation in a VLSI spiking neural network [PDF]
, 2012 The brain combines and integrates multiple cues to take coherent, context-dependent action using distributed, event-based computational primitives. Computational models that use these principles in software simulations of recurrently coupled spiking ...
Sonnleithner, D. +20 more
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Energy-Efficient Spiking Segmenter for Frame and Event-Based Images
Biomimetics, 2023 Semantic segmentation predicts dense pixel-wise semantic labels, which is crucial for autonomous environment perception systems. For applications on mobile devices, current research focuses on energy-efficient segmenters for both frame and event-based ...
Hong Zhang, Xiongfei Fan, Yu Zhang
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Spiking Neural Network Integrated Circuits: A Review of Trends and Future Directions [PDF]
IEEE Custom Integrated Circuits Conference, 2022 The rapid growth of deep learning, spurred by its successes in various fields ranging from face recognition [1] to game playing [2], has also triggered a growing interest in the design of specialized hardware accelerators to support these algorithms ...
Arindam Basu +3 more
semanticscholar +1 more source
A biologically inspired spiking model of visual processing for image feature detection [PDF]
, 2015 To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose.
Kerr, D +3 more
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SpikeGoogle: Spiking Neural Networks with GoogLeNet‐like inception module
CAAI Transactions on Intelligence Technology, 2022 Spiking Neural Network is known as the third‐generation artificial neural network whose development has great potential. With the help of Spike Layer Error Reassignment in Time for error back‐propagation, this work presents a new network called ...
Xuan Wang +6 more
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Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties
Frontiers in Applied Mathematics and Statistics, 2022 Over the past few decades, neuroscience studies have elucidated the structural/anatomical network characteristics in the brain and their associations with functional networks and the dynamics of neural activity.
Sou Nobukawa, Sou Nobukawa
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