Results 21 to 30 of about 68,095 (317)
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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Learning Universal Computations with Spikes. [PDF]
PLoS Computational Biology, 2016 Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the ...
Dominik Thalmeier +3 more
doaj +1 more source
Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays [PDF]
, 2012 Sheik S, Chicca E, Indiveri G. Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays. Presented at the International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia.Axonal delays are used in neural ...
Chicca, Elisabetta +2 more
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Targeting operational regimes of interest in recurrent neural networks.
PLoS Computational Biology, 2023 Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons.
Pierre Ekelmans +2 more
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BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
Frontiers in Neuroinformatics, 2018 The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms.
Hananel Hazan +6 more
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Spiking neural networks for computer vision [PDF]
Interface Focus, 2018 State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs. Biological vision systems use a quite different approach, where the eyes (cameras) sample the visual scene continuously ...
Michael Hopkins +3 more
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Models developed for spiking neural networks
MethodsX, 2023 Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these networks are inspired by the brain, they lack biological plausibility, and they have structural differences compared ...
Shahriar Rezghi Shirsavar +2 more
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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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Neural Sampling by Irregular Gating Inhibition of Spiking Neurons and Attractor Networks [PDF]
, 2016 A long tradition in theoretical neuroscience casts sensory processing in the brain as the process of inferring the maximally consistent interpretations of imperfect sensory input.
Indiveri, Giacomo, Muller, Lorenz K.
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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
core +2 more sources