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Long Short-Term Memory Spiking Networks and Their Applications
[article]
2020
arXiv
pre-print
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Analogous to the benefits presented by recurrent neural networks (RNNs) in learning time series models within DNNs, we develop SNNs based on long short-term memory (LSTM) networks. ...
The developed architecture and method for backpropagation within LSTM-based SNNs enable them to learn long-term dependencies with comparable results to conventional LSTMs. ...
We present a new framework for designing and training recurrent SNNs based on long short-term memory (LSTM) units. ...
arXiv:2007.04779v1
fatcat:edxjbezm6fabrlvct5p7czlvoa
Neuro-Inspired Computing with Resistive Switching Devices [Guest Editorial]
2018
IEEE Nanotechnology Magazine
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continuous event-based processes on the basis of correlation detection. they implement spiking a neural network equipped with short-and long-term plasticity to analyze real-world weather data following ...
Finally, timoleon Moraitis, Abu sebastian, and evangelos eleftheriou demonstrate, in "the role of short-term plasticity in neuromorphic learning," a combined short and long-term plasticity rule to cluster ...
continuous event-based processes on the basis of correlation detection. they implement spiking a neural network equipped with short-and long-term plasticity to analyze real-world weather data following ...
doi:10.1109/mnano.2018.2849799
fatcat:pppbdkwkn5aapik2aik4o57smq
Pathologic brain network activity: Memory impairment in epilepsy
2013
Neurology
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In this issue of Neurology ® , Kleen and colleagues 1 implicate pathologic hippocampal network activity during specific memory processes in the occurrence of errors in patients' performance on a short-term ...
Other paradigms will need to be used to assess what type of memory (i.e., verbal or spatial, short or long term) is primarily affected by IEDs occurring in specific brain regions involved in particular ...
In this issue of Neurology ® , Kleen and colleagues 1 implicate pathologic hippocampal network activity during specific memory processes in the occurrence of errors in patients' performance on a short-term ...
doi:10.1212/wnl.0b013e318297ef3c
pmid:23685930
pmcid:PMC4490898
fatcat:7vzm2xihz5hgdo7vlio56j2qxy
Plasticity in memristive devices for spiking neural networks
2015
Frontiers in Neuroscience
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term or short term plasticity. ...
term or short term plasticity. ...
(STM) and Long Term Memory (LTM). ...
doi:10.3389/fnins.2015.00051
pmid:25784849
pmcid:PMC4345885
fatcat:xvlfsjqnbjgvzhro7rigmhsdfm
A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns
2021
Frontiers in Neuroscience
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We demonstrate analytically the formation of long and short term memory in H-SNN and distinct response functions of memory pathways. ...
Within H-SNN, hierarchical spatial and temporal patterns are constructed with convolution connections and memory pathways containing spiking neurons with different dynamics. ...
For memory module, a combination of long-term and short-term neurons are used. Synapses in memory module are used only for perception thus not modified by STDP learning. ...
doi:10.3389/fnins.2020.615756
pmid:33519366
pmcid:PMC7841292
fatcat:xqfaivisn5bcrljnr6aunavy3y
Learning and Spatiotemporally Correlated Functions Mimicked in Oxide-Based Artificial Synaptic Transistors
[article]
2013
arXiv
pre-print
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Spike-timing dependent plasticity, short-term memory and long-term memory were successfully mimicked in such protonic/electronic hybrid artificial synapses. ...
And most importantly, spatiotemporally correlated logic functions are also mimicked in a simple artificial neural network without any intentional hard-wire connections due to the naturally proton-related ...
Short-term memory (STM) to long-term memory (LTM) transition was realized by tuning pre-synaptic spike voltage amplitude, and LTM was due to the proton-related interfacial electrochemical reaction. ...
arXiv:1304.7072v1
fatcat:74stmuyjbzexdlh5dwmsqhxb4y
Photo memtransistor based on CMOS flash memory technology on Graphene with neuromorphic applications
[article]
2021
arXiv
pre-print
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We show here how graphene can be implemented with conventional semiconductor flash memory technology in order to make programmable doping possible, simply by the application of short gate pulses. ...
Our approach may pave the way for integrating graphene in CMOS technology memory applications, and our device design could also be suitable for large scale neuromorphic computing structures. ...
Acknowledgments: We acknowledge funding from the Israeli Ministry of Science and Technology and The Air Force Office of Scientific Research. ...
arXiv:2005.06861v2
fatcat:w2t3t5rxzjedtmzqeubmfli42u
Bridging the semantic gap: Emulating biological neuronal behaviors with simple digital neurons
2013
2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA)
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By bridging the semantic gap for one such system we enable neuromorphic system developers, in general, to keep their hardware design simple and power-efficient and at the same time enjoy the benefits of ...
Furthermore, we demonstrate that for the LLIF primitives without built-in mechanisms for synaptic plasticity, two well-known neural learning rules-spike timing dependent plasticity and Hebbian learning-can ...
Acknowledgment The authors would like to thank Dharmendra Modha, Paul Merolla, Steve Esser, our anonymous reviewers, and our paper shepherd Mark Oskin for their helpful comments and review of this manuscript ...
doi:10.1109/hpca.2013.6522342
dblp:conf/hpca/NereHLT13
fatcat:nhyq2y2avvbjvey5zavphcin3i
Sequential Memory: A Putative Neural and Synaptic Dynamical Mechanism
2005
Journal of Cognitive Neuroscience
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We show that the short-term memory for a sequence of items can be implemented in an autoassociation neural network. Each item is one of the attractor states of the network. ...
We show with numerical simulations implementations of the mechanisms using (1) a sodium inactivation-based spike-frequency-adaptation mechanism, (2) a Ca 2+ -activated K + current, and (3) short-term synaptic ...
The memory for the order in which the items were presented is not implemented by long-term associative synaptic modification such as long-term potentiation, but instead by short-term nonassociative adaptation ...
doi:10.1162/0898929053124875
pmid:15811241
fatcat:7igwemb3fngu5id34pmnrtltpi
Role of Delayed Nonsynaptic Neuronal Plasticity in Long-Term Associative Memory
2006
Current Biology
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This is delayed with respect to early memory formation but concomitant with the establishment and duration of long-term memory. ...
It is now well established that persistent nonsynaptic neuronal plasticity occurs after learning and, like synaptic plasticity, it can be the substrate for long-term memory. ...
The depolarization is sufficient to increase the network response to the CS, emerges between 16 and 24 hr postconditioning, and persists as long as the long-term memory. ...
doi:10.1016/j.cub.2006.05.049
pmid:16824916
fatcat:aq6gp3kz65b2jeao73fegywv3q
Spiking Neural Networks for Computational Intelligence: An Overview
2021
Big Data and Cognitive Computing
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However, the same level of progress has not been observed in research on spiking neural networks (SNN), despite their capability to handle temporal data, energy-efficiency and low latency. ...
This review aims to provide an overview of the current real-world applications of SNNs and identifies steps to accelerate research involving SNNs in the future. ...
Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. ...
doi:10.3390/bdcc5040067
fatcat:5liaeyuytjejlpzprnclwteiuq
Memory and Information Processing in Neuromorphic Systems
2015
Proceedings of the IEEE
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In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. ...
A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. ...
Attractor Networks Mechanisms operating at the network level can also allow neural processing systems to form short-term memories, consolidate long-term ones, and carry out nonlinear processing functions ...
doi:10.1109/jproc.2015.2444094
fatcat:enmuv4qr6bdktlh7t3rfwfj27i
Evolving spiking neural networks for spatio-and spectro-temporal pattern recognition
2012
2012 6th IEEE INTERNATIONAL CONFERENCE INTELLIGENT SYSTEMS
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This paper provides a survey on the evolution of the evolving connectionist systems (ECOS) paradigm, from simple ECOS introduced in 1998 to evolving spiking neural networks (eSNN) and neurogenetic systems ...
Abstract This paper provides a survey on the evolution of the evolving connectionist systems (ECOS) paradigm, from simple ECOS introduced in 1998 to evolving spiking neural networks (eSNN) and neurogenetic ...
, represented as a change in the genetic code and the gene/ protein expression level as a result of the above short-term and long term memory changes and evolutionary processes. ...
doi:10.1109/is.2012.6335110
dblp:conf/is/Kasabov12
fatcat:5qa7yzkkjbdc7gy3grz32a4beu
Temporal pattern identification using spike-timing dependent plasticity
2007
Neurocomputing
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This approach is tested on a simple discrimination task which requires short-term memory : temporal pattern identication. ...
Our simulations take place in a recurrent network of spiking neurons with inhomogeneous synaptic weights. The network spontaneously displays a self-sustained activity. ...
that requires short term memory. ...
doi:10.1016/j.neucom.2006.10.082
fatcat:qgkyzk2zh5c7dir3llahhm565m
Memristive and CMOS Devices for Neuromorphic Computing
2020
Materials
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Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. ...
First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. ...
Acknowledgments This work has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 648635). ...
doi:10.3390/ma13010166
pmid:31906325
pmcid:PMC6981548
fatcat:mqi7putgvvc2ddlm7i2qqt6zh4
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