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The capacity and attractor basins of associative memory models [chapter]

N. Davey, S. P. Hunt
<span title="">1999</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
The performance characteristics of five variants of the Hopfield network are examined. Two performance metrics are used: memory capacity, and a measure of the size of basins of attraction.  ...  We find that the posttraining adjustment of processor thresholds has, at best, little or no effect on performance, and at worst a significant negative effect.  ...  Measurement of Performance Five associative memory models were investigated: the standard Hopfield network, the Storkey network, the local learning network, the adjustable threshold network, and finally  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/bfb0098189">doi:10.1007/bfb0098189</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ld6rvlukpjgxfdz66c4gzfgo3i">fatcat:ld6rvlukpjgxfdz66c4gzfgo3i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160505185845/http://uhra.herts.ac.uk/bitstream/handle/2299/1564/901699.pdf?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/86/72/8672c672ba2d355db08f50da2551e78af99046c1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/bfb0098189"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

High capacity recurrent associative memories

N DAVEY, S HUNT, R ADAMS
<span title="">2004</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bby322qx6ndsje4ypr56c7nnly" style="color: black;">Neurocomputing</a> </i> &nbsp;
An experimental investigation of the performance of networks trained by these algorithms is presented, including measurements of capacity, training time and their ability to correct corrupted versions  ...  Various algorithms for constructing weight matrices for Hopfield-type associative memories are reviewed, including ones with much higher capacity than the basic model.  ...  Structure and function of memories based upon the Hopfield model The Hopfield network is probably the best known example of a neural network auto-associator; others include the simple linear associator  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0925-2312(04)00362-5">doi:10.1016/s0925-2312(04)00362-5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pu4hbfcso5an5fxn7lau2wksoa">fatcat:pu4hbfcso5an5fxn7lau2wksoa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170922004227/http://uhra.herts.ac.uk/bitstream/handle/2299/596/102105.pdf;jsessionid=F40EC2E8DC67BC7D99FB14876C340366?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/93/a1/93a141ce5f3e260df42a05dce0f9cf88ca294417.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0925-2312(04)00362-5"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

High capacity recurrent associative memories

N. Davey, S.P. Hunt, R.G. Adams
<span title="">2004</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bby322qx6ndsje4ypr56c7nnly" style="color: black;">Neurocomputing</a> </i> &nbsp;
An experimental investigation of the performance of networks trained by these algorithms is presented, including measurements of capacity, training time and their ability to correct corrupted versions  ...  Various algorithms for constructing weight matrices for Hopfield-type associative memories are reviewed, including ones with much higher capacity than the basic model.  ...  Structure and function of memories based upon the Hopfield model The Hopfield network is probably the best known example of a neural network auto-associator; others include the simple linear associator  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neucom.2004.02.007">doi:10.1016/j.neucom.2004.02.007</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o5octfwuizhabgnytlsxwijh2e">fatcat:o5octfwuizhabgnytlsxwijh2e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170922004227/http://uhra.herts.ac.uk/bitstream/handle/2299/596/102105.pdf;jsessionid=F40EC2E8DC67BC7D99FB14876C340366?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/93/a1/93a141ce5f3e260df42a05dce0f9cf88ca294417.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neucom.2004.02.007"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Using Variable Threshold to Increase Capacity in a Feedback Neural Network [article]

Praveen Kuruvada
<span title="2011-06-28">2011</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The article presents new results on the use of variable thresholds to increase the capacity of a feedback neural network. Non-binary networks are also considered in this analysis.  ...  The use of the variable threshold approach was simulated in Java. The simulation takes in the number of neurons and memories to be considered and generates random numbers for the memory vector.  ...  neurons in the network and the number of memories to be stored. 2) Find the T-Matrix associated with the memory vectors considered.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1103.5081v2">arXiv:1103.5081v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cce6mtoy7nd7tji3v277byeyq4">fatcat:cce6mtoy7nd7tji3v277byeyq4</a> </span>
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Scaling laws for the attractors of Hopfield networks

G. Weisbuch, F. Fogelman-Soulie
<span title="">1985</span> <i title="EDP Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6ll6qfhmzrbq7hkkhucrrwurai" style="color: black;">Journal de Physique Lettres</a> </i> &nbsp;
Abstract 2014 Networks of threshold automata are random dynamical systems with a large number of attractors, which J. Hopfield proposed to use as associative memories.  ...  We establish the scaling laws relating the maximum number of « useful » attractors and the radius of the attraction basin to the number of automata.  ...  We thank Peretto for communicating his results prior to publication and D'Humieres for many helpful comments. We used for the numerical simulations the computer facilities of GRECO 70 of CNRS.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/jphyslet:019850046014062300">doi:10.1051/jphyslet:019850046014062300</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hh7fba4w3nfxdkjc5eagwbjidq">fatcat:hh7fba4w3nfxdkjc5eagwbjidq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170924000755/https://hal.archives-ouvertes.fr/jpa-00232570/document" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a9/5c/a95c6e103befc15ed43293b2cd714b720f826b86.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/jphyslet:019850046014062300"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Neural associative memory storing gray-coded gray-scale images

G. Costantini, D. Casali, R. Perfetti
<span title="">2003</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/22mhkeaq5zdqlmtti5oidj26fi" style="color: black;">IEEE Transactions on Neural Networks</a> </i> &nbsp;
Index Terms-Associative memories, brain-state-in-a-box (BSB) neural networks, gray-scale images, neural networks with finite precision weights.  ...  In this paper, we present a neural associative memory storing gray-scale images.  ...  INTRODUCTION T HE realization of binary associative memories by recurrent neural networks has been widely explored [1] - [3] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnn.2003.810596">doi:10.1109/tnn.2003.810596</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/18238051">pmid:18238051</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mdlny6saabhqlklvlk53uqnkg4">fatcat:mdlny6saabhqlklvlk53uqnkg4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200307000710/https://art.torvergata.it/retrieve/handle/2108/52599/423724/2003-Neural%20Associative%20Memory%20Storing%20Gray-Coded%20Gray-Scale%20Images.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fe/96/fe96862d5f97eb4ca441b533ab3bc9be2780c948.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnn.2003.810596"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Noise-Enhanced Associative Memories

Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney
<span title="2013-12-31">2013</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms allow reliable learning and recall of exponential numbers of patterns.  ...  Here we consider associative memories with noisy internal computations and analytically characterize performance.  ...  and further arguing for the viability of associative memory models for the (noisy) mammalian brain.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.10704">doi:10.5281/zenodo.10704</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/avi7b4xhyzdqzo5i4i7fkouv3u">fatcat:avi7b4xhyzdqzo5i4i7fkouv3u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160429180546/http://papers.nips.cc/paper/4875-noise-enhanced-associative-memories.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6a/1d/6a1d8a9994d1df33aaf8265e0dceca2608d65dd0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.10704"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Neuron-Less Neural-Like Networks with Exponential Association Capacity at Tabula Rasa [chapter]

Demian Battaglia
<span title="">2009</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Artificial neural networks have been used as models of associative memory but their storage capacity is severely limited.  ...  Here we present a radically new approach to the problem of classification based on the fact that networks associated to random hard constraint satisfaction problems display naturally an exponentially large  ...  The author is indebted to Riccardo Zecchina for inspiring this line of research.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-02264-7_20">doi:10.1007/978-3-642-02264-7_20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vsawvn7lg5hixcipsydyfaxgz4">fatcat:vsawvn7lg5hixcipsydyfaxgz4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170331173903/http://www.nld.ds.mpg.de/publications/inproceedingsreference.2009-11-10.3679461498/at_download/publication_pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1f/42/1f42bc5dd4970a467f2428e7694e68188a694372.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-02264-7_20"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Robust computation with rhythmic spike patterns [article]

E. Paxon Frady, Friedrich T. Sommer
<span title="2019-01-23">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Building on Hebbian neural associative memories, like Hopfield networks, we first propose threshold phasor associative memory (TPAM) networks.  ...  Here, we propose a novel type of attractor neural network in complex state space, and show how it can be leveraged to construct spiking neural networks with robust computational properties through a phase-to-timing  ...  The authors would like to thank Pentti Kanerva and members of the Redwood Center for valuable feedback.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.07718v1">arXiv:1901.07718v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2rpmq5r3cjat7nbkz3pcwb2oqe">fatcat:2rpmq5r3cjat7nbkz3pcwb2oqe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929170956/https://arxiv.org/pdf/1901.07718v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/60/20/6020c8281fb30145edc546b44160104e9e3fc9c9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.07718v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Realization of reinforcement learning using multi-winners KFM associative memory

Takahiro Ikeya, Yuko Osana
<span title="">2009</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hyrfp4q26bahtlwucwlaapnfwy" style="color: black;">2009 IEEE International Conference on Systems, Man and Cybernetics</a> </i> &nbsp;
In this paper, we propose a multi-winners Kohonen Feature Map (KFM) associative memory, and apply it to reinforcement learning.  ...  In the proposed model, the patterns are trained by the successive learning algorithm of the conventional KFM associative memory.  ...  MULTI-WINNERS KFM ASSOCIATIVE MEMORY Here, we explain the proposed Multi-Winners KFM (Kohonen Feature Map) Associative Memory (MW-KFMAM). A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icsmc.2009.5346624">doi:10.1109/icsmc.2009.5346624</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/smc/IkeyaO09.html">dblp:conf/smc/IkeyaO09</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/isl2ncty5zgljogf3g523l6psm">fatcat:isl2ncty5zgljogf3g523l6psm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170829025505/http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_SMC_2009/PDFs/508.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b4/0b/b40bd52b876e11d59f141eaf03c7eddad92181dc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icsmc.2009.5346624"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Analysis of the Effects of Noise on a Model for the Neural Mechanism of Short-Term Active Memory

J. Devin McAuley, Joseph Stampfli
<span title="">1994</span> <i title="MIT Press - Journals"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rckx6fqoszfvva5c53bqivu5am" style="color: black;">Neural Computation</a> </i> &nbsp;
neural network model, trained on a simple memory task, matched the temporal activity patterns of memory-associated neurons in monkeys performing delayed saccade or delayed match-to-sample tasks.  ...  Zipser (1991) showed that the activity of hidden units in a fully recur- rent neural network, trained on a very simple memory task, matched the qualitative temporal activity patterns of some of these memory-associated  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1162/neco.1994.6.4.668">doi:10.1162/neco.1994.6.4.668</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sbelj3mzgbd7zgtkzflpupm764">fatcat:sbelj3mzgbd7zgtkzflpupm764</a> </span>
<a target="_blank" rel="noopener" href="https://archive.org/details/sim_neural-computation_1994-07_6_4/page/668" title="read fulltext microfilm" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Archive [Microfilm] <div class="menu fulltext-thumbnail"> <img src="https://archive.org/serve/sim_neural-computation_1994-07_6_4/__ia_thumb.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1162/neco.1994.6.4.668"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> mitpressjournals.org </button> </a>

A High-Performance Computing Implementation of Iterative Random Forest for the Creation of Predictive Expression Networks

Ashley Cliff, Jonathon Romero, David Kainer, Angelica Walker, Anna Furches, Daniel Jacobson
<span title="2019-12-02">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/githwx23ynaxdoftesby3jya3y" style="color: black;">Genes</a> </i> &nbsp;
In this paper, we present a high-performance computing (HPC)-capable implementation of Iterative Random Forest (iRF).  ...  We compare the new implementation of iRF with the previous R version and analyze its time to completion on two of the world's fastest supercomputers, Summit and Titan.  ...  The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/genes10120996">doi:10.3390/genes10120996</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31810264">pmid:31810264</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hjbl2gfd5zfaxhtyj4hva6sg3a">fatcat:hjbl2gfd5zfaxhtyj4hva6sg3a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191208181743/https://res.mdpi.com/d_attachment/genes/genes-10-00996/article_deploy/genes-10-00996-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/46/1c/461c1b7573dd242cb5c1a5f2b2af966461f1a00b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/genes10120996"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

An Experimental Assessment of the Performance of Several Associative Memory Models [chapter]

S. P. Turvey, S. P. Hunt, N. Davey, R. J. Frank
<span title="">2001</span> <i title="Springer Vienna"> Artificial Neural Nets and Genetic Algorithms </i> &nbsp;
The performance characteristics of four different associative memory models are examined.  ...  The models differ in the training algorithm employed, although all four employ algorithms that are iterative, and use local information.  ...  Introduction The dynamics and performance of the Hopfield associative memory model have been thoroughly investigated and are well understood.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-7091-6230-9_16">doi:10.1007/978-3-7091-6230-9_16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/guplgn5kibhbxdvu63nvyh7wim">fatcat:guplgn5kibhbxdvu63nvyh7wim</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20030421204631/http://homepages.feis.herts.ac.uk:80/~nngroup/pubs/papers/turvey-icannga2001.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/38/1a/381a9a066443944ea078e9cf9423ec7e018f76e8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-7091-6230-9_16"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Two coding strategies for bidirectional associative memory

Y.-F. Wang, J.B. Cruz, J.H. Mulligan
<span title="">1990</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/22mhkeaq5zdqlmtti5oidj26fi" style="color: black;">IEEE Transactions on Neural Networks</a> </i> &nbsp;
Iterative retrieval has been proposed in a large number of different models to improve performance in auto-association tasks.  ...  Stability, capacity and statistical dynamics of second-order bidirectional associative memory. IEEE Trans. Syst. Man Cybern., 25(10), 1414-1424].  ...  Palomero-Gallagher and T. Wennekers for their comments and suggestions which helped in improving the manuscript.  ... 
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Iterative free-energy optimization for recurrent neural networks (INFERNO)

Alexandre Pitti, Philippe Gaussier, Mathias Quoy, Wael El-Deredy
<span title="2017-03-10">2017</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network.  ...  Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations.  ...  Acknowledgments We thank Souheil Hanoune and Karl Friston as well as the reviewers for interesting comments and fruitful feedback on anterior versions of the paper.  ... 
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