Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
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TLDR
The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNN's typically require many fewer operations and are the better candidates to process spatio-temporal data.About:
This article is published in Neural Networks.The article was published on 2019-03-01 and is currently open access. It has received 756 citations till now. The article focuses on the topics: Spiking neural network & Artificial neural network.read more
Citations
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Journal ArticleDOI
Probability learned neural model for human behavior analysis based on language cognition
TL;DR: The lab-scale numerical results show that accurate prediction in human psychological behavior and its quality shows the proposed framework's stability.
Journal ArticleDOI
SpikeBASE: Spiking Neural Learning Algorithm With Backward Adaptation of Synaptic Efflux
Jake M Stauffer,Qingxue Zhang +1 more
TL;DR: The proposed SpikeBASE algorithm, through comprehensively coordinating the learning of synaptic strength, synaptic responses, and multi-scale temporal memory formation, has demonstrated its effectiveness on end-to-end SNN training.
Journal ArticleDOI
Implementation of Kalman Filtering with Spiking Neural Networks
Alejandro Juárez-Lora,Luis M. García-Sebastián,V. Ponce,E. Rubio-Espino,Herón Molina-Lozano,Juan Humberto Sossa Azuela +5 more
TL;DR: In this article , the authors explore how the values of a Kalman gain matrix can be estimated by using spiking neural networks through a combination of biologically plausible neuron models with spike-time-dependent plasticity learning algorithms.
Journal ArticleDOI
Hypothesis of Cyclic Structures of Pre- and Consciousness as a Transition in Neuron-like Graphs to a Special Type of Symmetry
TL;DR: The proposed statistical kinetic model for describing the pre- and consciousness structures based on the cognitive neural networks, which mimics the columnar structures of the neocortex, is studied and promising results are presented.
DissertationDOI
Experimenting with a Biologically Plausible Neural Network
TL;DR: This research presents research on an implementation of a biologically inspired Bayesian Confidence Propagation Neural Network (BCPNN), and examines the model’s capacity, noise recovery ability and crosscolumn connection influence, among other attributes.
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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