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Long Short-Term Memory Spiking Networks and Their Applications [article]

Ali Lotfi Rezaabad, Sriram Vishwanath
2020 arXiv   pre-print
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]

Duygu Kuzum
2018 IEEE Nanotechnology Magazine  
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

M. T. Kucewicz, G. A. Worrell, J. Gotman
2013 Neurology  
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

Sylvain Saïghi, Christian G. Mayr, Teresa Serrano-Gotarredona, Heidemarie Schmidt, Gwendal Lecerf, Jean Tomas, Julie Grollier, Sören Boyn, Adrien F. Vincent, Damien Querlioz, Selina La Barbera, Fabien Alibart (+4 others)
2015 Frontiers in Neuroscience  
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

Xueyuan She, Saurabh Dash, Daehyun Kim, Saibal Mukhopadhyay
2021 Frontiers in Neuroscience  
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]

Chang Jin Wan, Li Qiang Zhu, Yi Shi, Qing Wan
2013 arXiv   pre-print
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]

Christian Frydendahl, S.R.K. Chaitanya Indukuri, Meir Grajower, Noa Mazurski, Joseph Shappir, Uriel Levy
2021 arXiv   pre-print
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

A. Nere, A. Hashmi, M. Lipasti, G. Tononi
2013 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA)  
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

Gustavo Deco, Edmund T. Rolls
2005 Journal of Cognitive Neuroscience  
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

Ildikó Kemenes, Volko A. Straub, Eugeny S. Nikitin, Kevin Staras, Michael O'Shea, György Kemenes, Paul R. Benjamin
2006 Current Biology  
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

Shirin Dora, Nikola Kasabov
2021 Big Data and Cognitive Computing  
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

Giacomo Indiveri, Shih-Chii Liu
2015 Proceedings of the IEEE  
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

Nikola Kasabov
2012 2012 6th IEEE INTERNATIONAL CONFERENCE INTELLIGENT SYSTEMS  
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

Frédéric Henry, Emmanuel Daucé, Hédi Soula
2007 Neurocomputing  
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

Valerio Milo, Gerardo Malavena, Christian Monzio Compagnoni, Daniele Ielmini
2020 Materials  
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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