A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
Solving the Spike Feature Information Vanishing Problem in Spiking Deep Q Network with Potential Based Normalization
[article]
2022
arXiv
pre-print
Preserved Fulltext
In this work, we mathematically analyzed the problem of the disappearance of spiking signal features in SDQN and proposed a potential based layer normalization(pbLN) method to directly train spiking deep ...
deep Q Network (SDQN). ...
In this work, we propose a potential based layer normalization method to solve the spike activity vanishing problem in SDQN. ...
arXiv:2206.03654v1
fatcat:veym2jrzqbdilj7d4vehsjuidy
Going Deeper With Directly-Trained Larger Spiking Neural Networks
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
But due to the binary spike activity of the firing function and the problem of gradient vanishing or explosion, current methods are restricted to shallow architectures and thereby difficult in harnessing ...
Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal information and event-driven signal processing, which is very suited for energy-efficient implementation in neuromorphic ...
So, we normalize the pre-activations to N (0, V 2 th ).
Deep Spiking Residual Network ResNet is one of the most popular architectures o tackle with the problem of degradation when networks go deep. ...
arXiv:2011.05280v2
fatcat:kzerblzzf5eh3no7jmhdt2lyee
A New Spiking Convolutional Recurrent Neural Network (SCRNN) With Applications to Event-Based Hand Gesture Recognition
2020
Frontiers in Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Rather than standard ANN to SNN conversion techniques, the network utilizes a supervised Spike Layer Error Reassignment (SLAYER) training mechanism that allows the network to adapt to neuromorphic (event-based ...
In this paper, a novel spiking convolutional recurrent neural network (SCRNN) architecture that takes advantage of both convolution operation and recurrent connectivity to maintain the spatial and temporal ...
The use of SLAYER effectively prevents the common gradient vanishing and explosion problem associated with recurrent neural networks. ...
doi:10.3389/fnins.2020.590164
pmid:33324153
pmcid:PMC7722478
fatcat:cyav4pefyvhvjck2swmoie5fye
Linear Leaky-Integrate-and-Fire Neuron Model Based Spiking Neural Networks and Its Mapping Relationship to Deep Neural Networks
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Spiking neural networks (SNNs) are brain-inspired machine learning algorithms with merits such as biological plausibility and unsupervised learning capability. ...
It can serve as the theoretical basis for the potential combination of the respective merits of the two categories of neural networks. ...
We generally adjust the spiking threshold to solve this problem. But we know that the shape of the action potential is essentially fixed, and the spiking threshold of neurons does not change. ...
arXiv:2207.04889v1
fatcat:7onmmma27rcm7doyrprg7tyfya
An optimised deep spiking neural network architecture without gradients
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The proposed Optimized Deep Event-driven Spiking neural network Architecture (ODESA) can simultaneously learn hierarchical spatio-temporal features at multiple arbitrary time scales. ...
Through these tests, we demonstrate ODESA can optimally solve practical and highly challenging hierarchical spatio-temporal learning tasks with the minimum possible number of computing nodes. ...
Acknowledgement This research was supported by the Commonwealth of Australia through the NGTF Cyber Call 2019 and in collaboration with the Defence Science and Technology Group of the Department of Defence ...
arXiv:2109.12813v3
fatcat:lrufjssgwrf6dlvteiop55nrf4
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
By studying the equivalence between ANNs and SNNs in the discrete representation space, a primitive network conversion method is introduced that takes full advantage of spike count to approximate the activation ...
To compensate for the approximation errors arising from the primitive network conversion, we further introduce a layer-wise learning method with an adaptive training scheduler to fine-tune the network ...
While discretizing the feature tensors derived from the first network layer can effectively preserve the information by leveraging the redundancies in the high-dimensional feature representation [35] ...
arXiv:2007.01204v1
fatcat:6cgw3yvezfdjvkwxjrzcuq4gra
Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Here, we apply end-to-end learning with membrane potential-based backpropagation to a spiking convolutional auto-encoder with multiple trainable layers of leaky integrate-and-fire neurons. ...
However, training deep spiking neural networks, especially in an unsupervised manner, is challenging and the performance of a spiking model is significantly hindered by dead or bursting neurons. ...
However, deep network structures are desirable to enable hierarchical information abstraction and integration. ...
arXiv:2109.11045v1
fatcat:tacibbyzerfohb3mmgnvznsod4
HF-SNN: High-frequency Spiking Neural Network
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
However, SNN with binary input and output will lose much information and slightly inferior to deep neural networks (DNN). We consider how to make the most of information to protect input. ...
INDEX TERMS Spiking neural network, high-frequency, deep learning I. INTRODUCTION ...
Due to the non-continue function and the deep network, it is getting easier to meet the vanishing gradient problem. It is difficult and costly to train SNN from scratch. ...
doi:10.1109/access.2021.3068159
fatcat:butt7yujnbhszcz647yg4pt2oq
Training Deep Spiking Neural Networks Using Backpropagation
2016
Frontiers in Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. ...
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. ...
Similarly, the generation of spikes in neuron i acts on its own membrane potential via the term a i , which is due to the reset in Equation (3) (normalized by V th ). ...
doi:10.3389/fnins.2016.00508
pmid:27877107
pmcid:PMC5099523
fatcat:uv2hg62gfbgqpmiy6n7i2mjkwm
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
2022
Electronics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
To improve accuracy while reducing the energy consumption, we propose a novel spiking neural network (SNN)-based respiratory classification solution, which can be implemented on event-driven neuromorphic ...
We propose a five-stage design pipeline involving data collection and labeling, feature scaling, deep learning model selection with hyperparameter tuning, model training and validation, and model testing ...
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics11050682
fatcat:jdztc4lbebbbzo6rmyhsq2i754
Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning
2022
Frontiers in Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the major challenges of current machine learning techniques, including the high energy consumption induced by deep neural ...
In this paper, we propose a novel spike-based framework with the entropy theory, namely, heterogeneous ensemble-based spike-driven few-shot online learning (HESFOL). ...
ACKNOWLEDGMENTS All authors would like to thank the editor and reviewer for their comments on this manuscript. ...
doi:10.3389/fnins.2022.850932
pmid:35615277
pmcid:PMC9124799
fatcat:walcddjcgjaydmuakhleclh32m
Toward Efficient Processing and Learning With Spikes: New Approaches for Multispike Learning
2020
IEEE Transactions on Cybernetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2005.00723v1 fatcat:ymdntymlprakrkmr54cha7rhpq
Spikes are the currency in central nervous systems for information transmission and processing. ...
A simplified spiking neuron model is first introduced with the effects of both synaptic input and firing output on the membrane potential being modeled with an impulse function. ...
describing the influence of spikes on membrane potential. κ(t) is causal and thus vanishes for t < 0. ...
doi:10.1109/tcyb.2020.2984888
pmid:32356771
fatcat:m2fu25jsirerbk742ch22algwi
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Experimental studies support the notion of spike-based neuronal information processing in the brain, with neural circuits exhibiting a wide range of temporally-based coding strategies to rapidly and efficiently ...
Motivated by this, we propose a new supervised learning method that can train multilayer spiking neural networks to solve classification problems based on a rapid, first-to-spike decoding strategy. ...
Acknowledgments This research has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project ...
arXiv:2008.06937v1
fatcat:slw4uyi6xfgjhjyzzzqgxbcdae
Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR with spiking neuros: DFRs are a new type of recurrent neural networks (RNNs) that are able to capture the ...
The introduced deep spiking DFR model is used to capture the temporal correlation of the spectrum occupancy time series and predict the idle/busy subcarriers in future time slots for potential spectrum ...
This work was supported by the U.S. National Science Foundation under grants ECCS 1731672, ECCS-1802710, ECCS-1811497, CNS-1811720, and CCF-1937487. ...
doi:10.1609/aaai.v34i02.5484
fatcat:p2js5knwvvhl5nu4c7r6ylomfm
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2017 pre-print arXiv:1710.02913v1 fatcat:zv6wulsdjbeird6vh7o4dyhlmu
For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. ...
In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential ...
Several types of DNNs exist such as deep convolutional networks, deep RNNs, deep belief networks, deep feedforward networks, deep SNNs, deep Q-learning, deep ESN, deep residual network, and long-short ...
arXiv:1710.02913v2
fatcat:kljn2evlwba4fha4lpwxjpv4yu
« Previous
Showing results 1 — 15 out of 2,038 results