Results 51 to 60 of about 60,411 (286)
Spark: modular spiking neural networks
Frontiers in Artificial IntelligenceNowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy.
Mario Franco, Carlos Gershenson
doaj +1 more source
Improving Spiking Neural Network Performance with Auxiliary Learning
Machine Learning and Knowledge Extraction, 2023 The use of back propagation through the time learning rule enabled the supervised training of deep spiking neural networks to process temporal neuromorphic data. However, their performance is still below non-spiking neural networks. Previous work pointed
Paolo G. Cachi +2 more
doaj +1 more source
PlaNeural: Spiking Neural Networks that Plan [PDF]
Procedia Computer Science, 2016 PlaNeural is a spike-based neural network that has the ability to plan. The network is a spreading activation network implemented with Cell Assemblies; this combination has built a dynamic network of nodes that is able to interact with an environment and respond appropriately.
Ian Mitchell 0002 +2 more
openaire +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Super‐Refractory Status Epilepticus (SRSE) is a rare, life‐threatening neurological emergency with unclear etiology in many cases. Mitochondrial dysfunction, often due to disease‐causing genetic variants, is increasingly recognized as a cause, with each gene producing distinct pathophysiological mechanisms.
Pouria Mohammadi +2 more
wiley +1 more source
Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach [PDF]
, 2018 Speech recognition has become an important task to improve the human-machine interface. Taking into account the limitations of current automatic speech recognition systems, like non-real time cloud-based solutions or power demand, recent interest for
Davidson, Simón +6 more
core
, 2017 Elucidating principles that underlie computation in neural networks is currently a major research topic of interest in neuroscience. Transfer Entropy (TE) is increasingly used as a tool to bridge the gap between network structure, function, and behavior ...
de Ruyter F. +3 more
core +1 more source
Supervised Learning in Multilayer Spiking Neural Networks [PDF]
, 2012 The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it ...
André Grüning +10 more
core +2 more sources
Epilepsy‐Associated Variants of a Single SCN1A Codon Exhibit Divergent Functional Properties
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Pathogenic variants in SCN1A, which encodes the voltage‐gated sodium channel NaV1.1, are associated with multiple epilepsy syndromes exhibiting a range of clinical severity. SCN1A variants are reported in different syndromes, including Dravet syndrome, which is associated with loss‐of‐function, whereas neonatal/infantile‐onset ...
Lanie N. Liebovitz +3 more
wiley +1 more source
Sparse Computation in Adaptive Spiking Neural Networks
Frontiers in Neuroscience, 2019 Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and ...
Davide Zambrano +4 more
doaj +1 more source
Sound Recognition System Using Spiking and MLP Neural Networks [PDF]
, 2016 In this paper, we explore the capabilities of a sound classification system that combines a Neuromorphic Auditory System for feature extraction and an artificial neural network for classification.
Cerezuela Escudero, Elena +5 more
core