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
Explainable neural networks that simulate reasoning
Paul Blazek,Milo M. Lin +1 more
TL;DR: In this article, the authors show how neural circuits can directly encode cognitive processes via simple neurobiological principles, and demonstrate how neural systems can encode cognitive functions, and use the proposed model to train robust, scalable deep neural networks that are explainable and capable of symbolic reasoning and domain generalization.
Journal ArticleDOI
Integration of Leaky-Integrate-and-Fire Neurons in Standard Machine Learning Architectures to Generate Hybrid Networks: A Surrogate Gradient Approach.
Richard C. Gerum,Achim Schilling +1 more
TL;DR: In this article, a surrogate gradient approach is proposed to train the LIF units via backpropagation, which can be used to run the neurons in different operating modes, such as simple signal integrators or coincidence detectors.
Posted ContentDOI
Spiking recurrent neural networks represent task-relevant neural sequences in rule-dependent computation
TL;DR: In this article, an excitatory-inhibitory spiking recurrent neural network (SRNN) was developed to perform a rule-dependent two-alternative forced choice (2AFC) task.
Journal ArticleDOI
Perception Understanding Action: Adding Understanding to the Perception Action Cycle With Spiking Segmentation.
TL;DR: A novel Neuromorphic Perception Understanding Action (PUA) system is presented, that aims to combine the feature extraction benefits of CNNs with low latency processing of SCNNs, and can deliver robust results of over 96 and 81% for accuracy and Intersection over Union, ensuring such a system can be successfully used within object recognition, classification and tracking problem.
Journal ArticleDOI
Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration
TL;DR: It is argued that simply copying and pasting the weights of ANN to SNN inevitably results in activation mismatch, especially for ANNs that are trained with batch normalization (BN) layers, and a set of layer-wise parameter calibration algorithms are proposed, which adjusts the parameters to minimize the activation mismatch.
References
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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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