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Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model
[article]
2014
arXiv
pre-print
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Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. ...
In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. ...
Through the self-optimization process our spiking neural network tends to go to an optimal attractor, which may correspond to a functionally efficacious CA in the sense of Varela. ...
arXiv:1409.0470v1
fatcat:vk4zucyl2vbx7bs7p5d6vitqie
Spiking neural networks trained via proxy
[article]
2021
arXiv
pre-print
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We propose a new learning algorithm to train spiking neural networks (SNN) using conventional artificial neural networks (ANN) as proxy. ...
The forward passes of the two networks are totally independent. ...
works: Bringing the power of gradient-based
optimization to spiking neural networks, IEEE [19] S. R. Kheradpisheh and T. ...
arXiv:2109.13208v2
fatcat:wlfdusom7nc5xomemafs244xum
RRAM based neuromorphic algorithms
[article]
2019
arXiv
pre-print
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This report mainly talks about the work on deep neural network to spiking neural network conversion and its significance. ...
This report basically gives an overview of the algorithms implemented on neuromorphic hardware with crossbar array of RRAM synapses. ...
Conversion of DNN to the spike-based domain: Spiking Deep Neural Network (SDNN) In a conventional CPU or GPU, it requires more time and energy to run a SDNN, whereas the power consumption and computational ...
arXiv:1903.02519v1
fatcat:kjb5c4e5yfhkjb7cft4nwgfqfi
Deep learning in spiking neural networks
2019
Neural Networks
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2018 pre-print arXiv:1804.08150v3 fatcat:gfgjgo4w5fbhzhbaw342vwccvyOther Versions
In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation. ...
Spiking neural networks (SNNs) are thus more biologically realistic than ANNs, and arguably the only viable option if one wants to understand how the brain computes. ...
In comparison to true biological networks, the network dynamics of artificial SNNs are highly simplified. ...
doi:10.1016/j.neunet.2018.12.002
fatcat:nfat4xwh5bdtfhauugyqpxhnzq
Training deep neural networks for binary communication with the Whetstone method
2019
Nature Machine Intelligence
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2018 pre-print arXiv:1810.11521v1 fatcat:kkuizhtf3fa7dh64tx36q3bjpy
To date, the majority of artificial neural networks have not operated using discrete spike-like communication. ...
We present a method for training deep spiking neural networks using an iterative modification of the backpropagation optimization algorithm. ...
Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. ...
doi:10.1038/s42256-018-0015-y
fatcat:pcoqu5ekibbcjj4q2mropjjlfe
Integration and co-design of memristive devices and algorithms for artificial intelligence
2020
iScience
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However, these similarities do not directly transfer to the success of efficient computation without device and algorithm co-designs and optimizations. ...
Such co-design and optimization have been the main focus of memristive neuromorphic engineering, which often abandons the "non-ideal" behaviors of memristive devices, although many of them resemble what ...
With limited conductance states, the conventional artificial neural network needs to be adapted. ...
doi:10.1016/j.isci.2020.101809
pmid:33305176
pmcid:PMC7718163
fatcat:bibhecux2nafzjexaklossadae
Event-based Signal Processing for Radioisotope Identification
[article]
2020
arXiv
pre-print
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This paper identifies the problem of unnecessary high power overhead of the conventional frame-based radioisotope identification process and proposes an event-based signal processing process to address ...
It also presents the design flow of the neuromorphic processor. ...
Due to their spatiotemporal nature and intermediate level of abstraction between biological plausibility and the Artificial Neural Networks (ANNs) of machine learning, Spiking Neural Networks (SNNs) are ...
arXiv:2007.05686v2
fatcat:eoavotv4tnadxp7hqsd3vadbku
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware
[article]
2016
arXiv
pre-print
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of artificial neurons with those of the spiking neurons. ...
Recurrent neural networks (RNN) are widely used in machine learning to solve a variety of sequence learning tasks. ...
Acknowledgments We thank the organizers and the participants of the Telluride Neuromorphic Cognition Engineering Workshop 2015, and especially the natural language processing group and Rodrigo Alvarez, ...
arXiv:1601.04187v1
fatcat:5jwrs5dhq5eghbzme7wb7wnoby
RetinaNet Object Detector based on Analog-to-Spiking Neural Network Conversion
[article]
2021
arXiv
pre-print
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The paper proposes a method to convert a deep learning object detector into an equivalent spiking neural network. ...
The aim is to provide a conversion framework that is not constrained to shallow network structures and classification problems as in state-of-the-art conversion libraries. ...
ACKNOWLEDGMENTS The research leading to this paper was conducted within the ShippingLab research program [13] sponsored by the Danish Innovation Fund, The Danish Maritime Fund, Orients Fund and the Lauritzen ...
arXiv:2106.05624v2
fatcat:usghcmb2ibf3hbby2tfryfdouu
State-of-the-art deep learning has a carbon emission problem. Can neuromorphic engineering help?
2020
Dialogues in Clinical Neuroscience & Mental Health
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While a method to train neural networks directly on neuromorphic devices has yet to be discovered it has already been demonstrated that executing trained neural networks on neuromorphic platforms comes ...
Deep learning has attracted a lot of attention from both academic, as well as, industrial parties mainly due to its success when working large datasets and its ability to improve performance by scaling ...
Artificial neurons when combined together with other artificial neurons, form Artificial Neural Networks (ANNs), while the various ways that artificial neurons can be combined together give rise to the ...
doi:10.26386/obrela.v3i3.166
doaj:5adeafdcbdee4dcc8e4c8e4013f45188
fatcat:h3sjpkrupbcqhpjuxkzshqkxca
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
2015
2015 International Joint Conference on Neural Networks (IJCNN)
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However, this has come at the cost of performance losses due to the conversion from analog neural networks (ANNs) without a notion of time, to sparsely firing, event-driven SNNs. ...
We present a set of optimization techniques to minimize performance loss in the conversion process for ConvNets and fully connected deep networks. ...
Training of spiking deep networks typically does not use spike-based learning rules, but instead starts from a conventional ANN, fully trained with backpropagation, followed by a conversion of the rate-based ...
doi:10.1109/ijcnn.2015.7280696
dblp:conf/ijcnn/DiehlNB0LP15
fatcat:qdomfyjbkvb6nkkytlzfb3thrq
Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware
2016
2016 IEEE International Conference on Rebooting Computing (ICRC)
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Acknowledgments We thank the organizers and the participants of the Telluride Neuromorphic Cognition Engineering Workshop 2015, and especially the natural language processing group and Rodrigo Alvarez, ...
Those networks are pre-trained on a conventional computer and then converted to spiking neural networks (SNN) [6] . ...
Some of the issues that arise during the conversion of Elman recurrent networks to spiking neural networks have been addressed by the conversion of convolutional neural networks and fully-connected networks ...
doi:10.1109/icrc.2016.7738691
dblp:conf/icrc/DiehlZCPN16
fatcat:abq5rlfc5ff6lcjjqe3dqk4oey
Deep Learning With Spiking Neurons: Opportunities and Challenges
2018
Frontiers in Neuroscience
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A wide range of training methods for SNNs is presented, ranging from the conversion of conventional deep networks into SNNs, constrained training before conversion, spiking variants of backpropagation, ...
Neuromorphic hardware platforms have great potential to enable deep spiking networks in real-world applications. ...
ACKNOWLEDGMENTS We would like to thank David Stöckel, Volker Fischer, and Andre Guntoro for critical reading and helpful discussions. ...
doi:10.3389/fnins.2018.00774
pmid:30410432
pmcid:PMC6209684
fatcat:flcvj3c4tvfibhn2du3y6t3jvq
Training a digital model of a deep spiking neural network using backpropagation
2020
E3S Web of Conferences
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The classification accuracy on test data for spiking neural network with 3 hidden layers is equal to 98.14%. ...
Deep spiking neural networks are one of the promising eventbased sensor signal processing concepts. ...
There are three main approaches to train deep SNNs: conversion of a trained conventional deep neural network to SNN [5] ; unsupervised learning based on local learning rules such as STDP [6] ; direct ...
doi:10.1051/e3sconf/202022401026
fatcat:5tm4h55iunbxblhnupsbi7gbjq
Layer-wise synapse optimization for implementing neural networks on general neuromorphic architectures
2017
2017 IEEE Symposium Series on Computational Intelligence (SSCI)
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2018 pre-print arXiv:1802.06920v1 fatcat:yqnswgp3ovcdziihqrkiot7hfq
Deep artificial neural networks (ANNs) can represent a wide range of complex functions. ...
Conventional ANNs must be converted into equivalent Spiking Neural Networks (SNNs) in order to be deployed on neuromorphic chips. This paper presents a way to perform this translation. ...
ACKNOWLEDGMENT The authors would like to thank the U.S. ...
doi:10.1109/ssci.2017.8285202
dblp:conf/ssci/MernGK17
fatcat:qd4plzx25jfzzea7i5mowctmli
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