Results 31 to 40 of about 60,411 (286)
An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing [PDF]
, 2020 The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the
Belatreche, Ammar +7 more
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Counting to Ten with Two Fingers: Compressed Counting with Spiking Neurons [PDF]
, 2019 We consider the task of measuring time with probabilistic threshold gates implemented by bio-inspired spiking neurons. In the model of spiking neural networks, network evolves in discrete rounds, where in each round, neurons fire in pulses in response to
Hitron, Yael, Parter, Merav
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Biomimetics, 2023 One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS).
William R. P. Nourse +3 more
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Modeling spiking neural networks
Theoretical Computer Science, 2008 AbstractA notation for the functional specification of a wide range of neural networks consisting of temporal or non-temporal neurons, is proposed. The notation is primarily a mathematical framework, but it can also be illustrated graphically and can be extended into a language in order to be automated. Its basic building blocks are processing entities,
Ioannis D. Zaharakis, Achilles D. Kameas
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Neural Architecture Search for Spiking Neural Networks
, 2022 Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence ...
Youngeun Kim +4 more
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A biomimetic neural encoder for spiking neural network [PDF]
Nature Communications, 2021 AbstractSpiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven information processing capabilities.
Shiva Subbulakshmi Radhakrishnan +4 more
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Training Spiking Neural Networks for Reinforcement Learning Tasks With Temporal Coding Method
Frontiers in Neuroscience, 2022 Recent years witness an increasing demand for using spiking neural networks (SNNs) to implement artificial intelligent systems. There is a demand of combining SNNs with reinforcement learning architectures to find an effective training method.
Guanlin Wu +3 more
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Deep learning in spiking neural networks [PDF]
Neural Networks, 2019 In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation.
Kheradpisheh, Saeed Reza +5 more
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Designing the Dynamics of Spiking Neural Networks [PDF]
Physical Review Letters, 2006 4 pages, 3 ...
Memmesheimer, R., Timme, M.
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Brain-Inspired Computing: Models and Architectures
IEEE Open Journal of Circuits and Systems, 2020 With an exponential increase in the amount of data collected per day, the fields of artificial intelligence and machine learning continue to progress at a rapid pace with respect to algorithms, models, applications, and hardware.
Keshab K. Parhi, Nanda K. Unnikrishnan
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