{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T22:35:51Z","timestamp":1783463751769,"version":"3.55.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"34","license":[{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s00521-023-08552-7","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T09:02:17Z","timestamp":1683190937000},"page":"24543-24559","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["The flip-flop neuron: a memory efficient alternative for solving challenging sequence processing and decision-making problems"],"prefix":"10.1007","volume":"35","author":[{"given":"Sweta","family":"Kumari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vigneswaran","family":"Chandrasekaran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2453-1637","authenticated-orcid":false,"given":"V. Srinivasa","family":"Chakravarthy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"key":"8552_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1023\/A:1007634325138","volume":"40","author":"J Baxter","year":"2000","unstructured":"Baxter J, Tridgell A, Weaver L (2000) Learning to play chess using temporal differences. Mach Learn 40:243\u2013263","journal-title":"Mach Learn"},{"key":"8552_CR2","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1038\/s42256-019-0070-z","volume":"1","author":"F Agostinelli","year":"2019","unstructured":"Agostinelli F, McAleer S, Shmakov A, Baldi P (2019) Solving the Rubik\u2019s cube with deep reinforcement learning and search. Nat Mach Intell 1:356\u2013363","journal-title":"Nat Mach Intell"},{"key":"8552_CR3","unstructured":"Foo JL (2019) Smart security camera using machine learning. Ph.D. thesis, UTAR"},{"key":"8552_CR4","unstructured":"Wang L, Sng D (2015) Deep learning algorithms with applications to video analytics for a smart city: a survey. Preprint arXiv:1512.03131"},{"key":"8552_CR5","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1016\/j.jstrokecerebrovasdis.2019.02.004","volume":"28","author":"R Garg","year":"2019","unstructured":"Garg R, Oh E, Naidech A, Kording K, Prabhakaran S (2019) Automating ischemic stroke subtype classification using machine learning and natural language processing. J Stroke Cerebrovasc Dis 28:2045\u20132051","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"8552_CR6","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780","journal-title":"Neural Comput"},{"key":"8552_CR7","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111\u20133119"},{"key":"8552_CR8","doi-asserted-by":"crossref","unstructured":"Lu Y, Salem FM (2017) Simplified gating in long short-term memory (lSTM) recurrent neural networks. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS), IEEE, pp 1601\u20131604","DOI":"10.1109\/MWSCAS.2017.8053244"},{"key":"8552_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"F Zhu","year":"2019","unstructured":"Zhu F, Ye F, Fu Y, Liu Q, Shen B (2019) Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network. Sci Rep 9:1\u201311","journal-title":"Sci Rep"},{"key":"8552_CR10","doi-asserted-by":"crossref","unstructured":"Suh JS, et\u00a0al (2018) 24 GHz FMCW radar system for real-time hand gesture recognition using LSTM. In: 2018 Asia-Pacific microwave conference (APMC), IEEE, pp 860\u2013862","DOI":"10.23919\/APMC.2018.8617375"},{"key":"8552_CR11","unstructured":"Minaee S, Azimi E, Abdolrashidi A (2019) Deep-sentiment: sentiment analysis using ensemble of CNN and bi-LSTM models. Preprint arXiv:1904.04206"},{"key":"8552_CR12","unstructured":"Graves A (2013) Generating sequences with recurrent neural networks. Preprint arXiv:1308.0850"},{"key":"8552_CR13","unstructured":"Santhanam S (2020) Context based text-generation using lSTM networks. Preprint arXiv:2005.00048"},{"key":"8552_CR14","doi-asserted-by":"crossref","unstructured":"Chakraborty S, Banik J, Addhya S, Chatterjee D (2020) Study of dependency on number of lSTM units for character based text generation models. In: 2020 international conference on computer science, engineering and applications (ICCSEA), IEEE, pp 1\u20135","DOI":"10.1109\/ICCSEA49143.2020.9132839"},{"key":"8552_CR15","first-page":"44","volume":"10","author":"D Pawade","year":"2018","unstructured":"Pawade D, Sakhapara A, Jain M, Jain N, Gada K (2018) Story scrambler-automatic text generation using word level RNN-lSTM. Int J Inf Technol Comput Sci (IJITCS) 10:44\u201353","journal-title":"Int J Inf Technol Comput Sci (IJITCS)"},{"key":"8552_CR16","doi-asserted-by":"crossref","unstructured":"Abujar S, Masum AKM, Chowdhury S MH, Hasan M, Hossain SA (2019) Bengali text generation using bi-directional RNN. In: 2019 10th international conference on computing, communication and networking technologies (ICCCNT), IEEE, pp 1\u20135","DOI":"10.1109\/ICCCNT45670.2019.8944784"},{"key":"8552_CR17","doi-asserted-by":"crossref","unstructured":"Hosseini M, Maida AS, Hosseini M, Raju G (2019) Inception-inspired lSTM for next-frame video prediction. Preprint arXiv:1909.05622","DOI":"10.1609\/aaai.v34i10.7176"},{"key":"8552_CR18","unstructured":"Moskola\u00ef W, Abdou W, Dipanda A, Kolyang DT (2020) Application of lSTM architectures for next frame forecasting in sentinel-1 images time series. Preprint arXiv:2009.00841"},{"key":"8552_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105820","volume":"86","author":"C Dai","year":"2020","unstructured":"Dai C, Liu X, Lai J (2020) Human action recognition using two-stream attention based lSTM networks. Appl Soft Comput 86:105820","journal-title":"Appl Soft Comput"},{"key":"8552_CR20","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.neucom.2020.06.032","volume":"410","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Lv Z, Gan C, Zhu Q (2020) Human action recognition using convolutional lSTM and fully-connected lSTM with different attentions. Neurocomputing 410:304\u2013316","journal-title":"Neurocomputing"},{"key":"8552_CR21","doi-asserted-by":"crossref","unstructured":"Holla P, Chakravarthy S (2016) Decision making with long delays using networks of flip-flop neurons. In: 2016 international joint conference on neural networks (IJCNN), IEEE, pp 2767\u20132773","DOI":"10.1109\/IJCNN.2016.7727548"},{"key":"8552_CR22","doi-asserted-by":"publisher","first-page":"33610","DOI":"10.1109\/ACCESS.2019.2903586","volume":"7","author":"J-W Choi","year":"2019","unstructured":"Choi J-W, Ryu S-J, Kim J-H (2019) Short-range radar based real-time hand gesture recognition using lSTM encoder. IEEE Access 7:33610\u201333618","journal-title":"IEEE Access"},{"key":"8552_CR23","doi-asserted-by":"crossref","unstructured":"Wang J, Yu L-C, Lai KR, Zhang X (2016) Dimensional sentiment analysis using a regional CNN-lSTM model. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 2: Short Papers), pp 225\u2013230","DOI":"10.18653\/v1\/P16-2037"},{"key":"8552_CR24","unstructured":"Shkarupa Y, Mencis R, Sabatelli M (2016) Offline handwriting recognition using lSTM recurrent neural networks. In: The 28th Benelux conference on artificial intelligence"},{"key":"8552_CR25","doi-asserted-by":"crossref","unstructured":"Gao R et\u00a0al (2019) Distanced lSTM: time-distanced gates in long short-term memory models for lung cancer detection. In: International workshop on machine learning in medical imaging, Springer, pp 310\u2013318","DOI":"10.1007\/978-3-030-32692-0_36"},{"key":"8552_CR26","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/ACCESS.2017.2778011","volume":"6","author":"A Ullah","year":"2017","unstructured":"Ullah A, Ahmad J, Muhammad K, Sajjad M, Baik SW (2017) Action recognition in video sequences using deep bi-directional lSTM with CNN features. IEEE Access 6:1155\u20131166","journal-title":"IEEE Access"},{"key":"8552_CR27","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2016","unstructured":"Greff K, Srivastava RK, Koutn\u00edk J, Steunebrink BR, Schmidhuber J (2016) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28:2222\u20132232","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"8552_CR28","first-page":"15696","volume":"32","author":"N Maheswaranathan","year":"2019","unstructured":"Maheswaranathan N, Williams AH, Golub MD, Ganguli S, Sussillo D (2019) Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. Adv Neural Inf Process Syst 32:15696","journal-title":"Adv Neural Inf Process Syst"},{"key":"8552_CR29","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.4249\/scholarpedia.1977","volume":"2","author":"JJ Hopfield","year":"2007","unstructured":"Hopfield JJ (2007) Hopfield network. Scholarpedia 2:1977","journal-title":"Scholarpedia"},{"key":"8552_CR30","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1103\/PhysRevLett.57.2861","volume":"57","author":"H Sompolinsky","year":"1986","unstructured":"Sompolinsky H, Kanter I (1986) Temporal association in asymmetric neural networks. Phys Rev Lett 57:2861","journal-title":"Phys Rev Lett"},{"key":"8552_CR31","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1038\/nbt1004-1315","volume":"22","author":"SR Eddy","year":"2004","unstructured":"Eddy SR (2004) What is a hidden Markov model? Nat Biotechnol 22:1315\u20131316","journal-title":"Nat Biotechnol"},{"key":"8552_CR32","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. Preprint arXiv:1412.3555"},{"key":"8552_CR33","unstructured":"Graves A, Wayne G, Danihelka I (2014) Neural turing machines. Preprint arXiv:1410.5401"},{"key":"8552_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-68169-x","volume":"10","author":"SC Quax","year":"2020","unstructured":"Quax SC, D\u2019Asaro M, van Gerven MA (2020) Adaptive time scales in recurrent neural networks. Sci Rep 10:1\u201314","journal-title":"Sci Rep"},{"key":"8552_CR35","unstructured":"Shewalkar AN (2018) Comparison of RNN, lSTM and GRU on speech recognition data"},{"key":"8552_CR36","unstructured":"Wang YE, Wei G-Y, Brooks D (2019) Benchmarking TPU, GPU, and CPU platforms for deep learning. Preprint arXiv:1907.10701"},{"key":"8552_CR37","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/s11107-017-0754-4","volume":"35","author":"GK Bharti","year":"2018","unstructured":"Bharti GK, Rakshit JK (2018) Design of all-optical JK, SR and t flip-flops using micro-ring resonator-based optical switch. Photon Netw Commun 35:381\u2013391","journal-title":"Photon Netw Commun"},{"key":"8552_CR38","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511805172","volume-title":"Digital integrated circuit design: from VLSI architectures to CMOS fabrication","author":"H Kaeslin","year":"2008","unstructured":"Kaeslin H (2008) Digital integrated circuit design: from VLSI architectures to CMOS fabrication. Cambridge University Press"},{"key":"8552_CR39","doi-asserted-by":"crossref","unstructured":"Aggarwal M, Barsainya R, Rawat TK (2015) FPGA implementation of Hilbert transformer based on lattice wave digital filters. In: 2015 4th international conference on reliability, Infocom technologies and optimization (ICRITO) (Trends and Future Directions), IEEE, pp 1\u20135","DOI":"10.1109\/ICRITO.2015.7359331"},{"key":"8552_CR40","doi-asserted-by":"crossref","unstructured":"Wang T, Zhang B, Arshad MJ, Arrathoon R (1990) Complex optoelectronic combinatorial logic systems: a fiber based distributed array image processor. In: Digital optical computing II, vol 1215, International Society for Optics and Photonics, pp 78\u201392","DOI":"10.1117\/12.18052"},{"key":"8552_CR41","unstructured":"Maas A et\u00a0al (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies, pp 142\u2013150"},{"key":"8552_CR42","doi-asserted-by":"crossref","unstructured":"Liwicki M, Bunke H (2005) IAM-OnDB-an on-line English sentence database acquired from handwritten text on a whiteboard. In: Eighth international conference on document analysis and recognition (ICDAR\u201905), IEEE, pp 956\u2013961","DOI":"10.1109\/ICDAR.2005.132"},{"key":"8552_CR43","first-page":"651","volume":"2018","author":"S Bodapati","year":"2020","unstructured":"Bodapati S, Reddy S, Katta S (2020) Realistic handwriting generation using recurrent neural networks and long short-term networks. ICCII 2018:651","journal-title":"ICCII"},{"key":"8552_CR44","unstructured":"Liddy ED (2001) Natural language processing"},{"key":"8552_CR45","doi-asserted-by":"crossref","unstructured":"Peng B, Yao K (2015) Recurrent neural networks with external memory for language understanding. Preprint arXiv:1506.00195","DOI":"10.1007\/978-3-319-25207-0_3"},{"key":"8552_CR46","unstructured":"Chung J, Gulcehre C, Cho, K, Bengio, Y (2015) Gated feedback recurrent neural networks. In: International conference on machine learning, PMLR, pp 2067\u20132075"},{"key":"8552_CR47","doi-asserted-by":"crossref","unstructured":"Zheng S et\u00a0al (2015) Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE international conference on computer vision, pp 1529\u20131537","DOI":"10.1109\/ICCV.2015.179"},{"key":"8552_CR48","unstructured":"Sung W, Park J (2018) Single stream parallelization of recurrent neural networks for low power and fast inference. Preprint arXiv:1803.11389"},{"key":"8552_CR49","unstructured":"Bordes A, Usunier N, Chopra S, Weston J (2015) Large-scale simple question answering with memory networks. Preprint arXiv:1506.02075"},{"key":"8552_CR50","doi-asserted-by":"crossref","unstructured":"Collier M, Beel J (2018) Implementing neural turing machines. In: International conference on artificial neural networks, Springer, pp 94\u2013104","DOI":"10.1007\/978-3-030-01424-7_10"},{"key":"8552_CR51","unstructured":"Rosca M, Lakshminarayanan B, Warde-Farley D, Mohamed S (2017) Variational approaches for auto-encoding generative adversarial networks. Preprint arXiv:1706.04987"},{"key":"8552_CR52","doi-asserted-by":"crossref","unstructured":"Narusawa A, Shimoda W, Yanai K (2018) Font style transfer using neural style transfer and unsupervised cross-domain transfer. In: Asian conference on computer vision, Springer, pp 100\u2013109","DOI":"10.1007\/978-3-030-21074-8_9"},{"key":"8552_CR53","doi-asserted-by":"publisher","first-page":"2621","DOI":"10.1016\/j.ins.2008.02.009","volume":"178","author":"S Suresh","year":"2008","unstructured":"Suresh S, Sundararajan N, Saratchandran P (2008) Risk-sensitive loss functions for sparse multi-category classification problems. Inf Sci 178:2621\u20132638","journal-title":"Inf Sci"},{"key":"8552_CR54","unstructured":"Creswell A, Arulkumaran, K, Bharath AA (2017) On denoising autoencoders trained to minimise binary cross-entropy. Preprint arXiv:1708.08487"},{"key":"8552_CR55","doi-asserted-by":"crossref","unstructured":"Liu J, Luo, J, Shah, M (2009) Recognizing realistic actions from videos \u201cin the wild\u201d. In: 2009 IEEE conference on computer vision and pattern recognition, IEEE, pp 1996\u20132003","DOI":"10.1109\/CVPR.2009.5206744"},{"key":"8552_CR56","doi-asserted-by":"crossref","unstructured":"Grupp A et\u00a0al (2019) Benchmarking deep learning infrastructures by means of tensorflow and containers. In: International conference on high performance computing, Springer, pp 478\u2013489","DOI":"10.1007\/978-3-030-34356-9_36"},{"key":"8552_CR57","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1093\/jigpal\/jzp046","volume":"18","author":"P Simen","year":"2010","unstructured":"Simen P, Polk T (2010) A symbolic\/subsymbolic interface protocol for cognitive modeling. Logic J IGPL 18:705\u2013761","journal-title":"Logic J IGPL"},{"key":"8552_CR58","doi-asserted-by":"crossref","unstructured":"Hayworth KJ, Marblestone AH (2018) How thalamic relays might orchestrate supervised deep training and symbolic computation in the brain. bioRxiv 304980","DOI":"10.1101\/304980"},{"key":"8552_CR59","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.conb.2004.03.003","volume":"14","author":"D Passingham","year":"2004","unstructured":"Passingham D, Sakai K (2004) The prefrontal cortex and working memory: physiology and brain imaging. Curr Opin Neurobiol 14:163\u2013168","journal-title":"Curr Opin Neurobiol"},{"key":"8552_CR60","doi-asserted-by":"publisher","first-page":"13338","DOI":"10.1523\/JNEUROSCI.3408-06.2006","volume":"26","author":"M Hampson","year":"2006","unstructured":"Hampson M, Driesen NR, Skudlarski P, Gore JC, Constable RT (2006) Brain connectivity related to working memory performance. J Neurosci 26:13338\u201313343","journal-title":"J Neurosci"},{"key":"8552_CR61","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004592","volume":"11","author":"J Fonollosa","year":"2015","unstructured":"Fonollosa J, Neftci E, Rabinovich M (2015) Learning of chunking sequences in cognition and behavior. PLoS Comput Biol 11:e1004592","journal-title":"PLoS Comput Biol"},{"key":"8552_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep27755","volume":"6","author":"RM Cichy","year":"2016","unstructured":"Cichy RM, Khosla A, Pantazis D, Torralba A, Oliva A (2016) Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci Rep 6:1\u201313","journal-title":"Sci Rep"},{"key":"8552_CR63","doi-asserted-by":"crossref","unstructured":"Haque A, Alahi A, Fei-Fei L (2016) Recurrent attention models for depth-based person identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1229\u20131238","DOI":"10.1109\/CVPR.2016.138"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08552-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08552-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08552-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T09:05:36Z","timestamp":1699002336000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08552-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,4]]},"references-count":63,"journal-issue":{"issue":"34","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["8552"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08552-7","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.11.16.468605","asserted-by":"object"}]},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,4]]},"assertion":[{"value":"28 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}