{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T10:22:39Z","timestamp":1743157359394,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030910990"},{"type":"electronic","value":"9783030911003"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-91100-3_3","type":"book-chapter","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T00:03:57Z","timestamp":1638749037000},"page":"33-43","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Extended Category Learning with Spiking Nets and Spike Timing Dependent Plasticity"],"prefix":"10.1007","author":[{"given":"Christian","family":"Huyck","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos","family":"Samey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.cogsys.2010.08.003","volume":"12","author":"R Belavkin","year":"2010","unstructured":"Belavkin, R., Huyck, C.: Conflict resolution and learning probability matching in a neural cell-assembly architecture. Cogn. Syst. Res. 12, 93\u2013101 (2010)","journal-title":"Cogn. Syst. Res."},{"issue":"24","key":"3_CR2","doi-asserted-by":"publisher","first-page":"10464","DOI":"10.1523\/JNEUROSCI.18-24-10464.1998","volume":"18","author":"G Bi","year":"1998","unstructured":"Bi, G., Poo, M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18(24), 10464\u201310472 (1998)","journal-title":"J. Neurosci."},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"3637","DOI":"10.1152\/jn.00686.2005","volume":"94","author":"R Brette","year":"2005","unstructured":"Brette, R., Gerstner, W.: Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94, 3637\u20133642 (2005)","journal-title":"J. Neurophysiol."},{"key":"3_CR4","volume-title":"The Computational Brain","author":"P Churchland","year":"1999","unstructured":"Churchland, P., Sejnowski, T.: The Computational Brain. MIT Press, Cambridge (1999)"},{"issue":"S2","key":"3_CR5","doi-asserted-by":"publisher","first-page":"P2","DOI":"10.1186\/1471-2202-8-S2-P2","volume":"8","author":"A Davison","year":"2007","unstructured":"Davison, A., Yger, P., Kremkow, J., Perrinet, L., Muller, E.: PyNN: towards a universal neural simulator API in python. BMC Neurosci 8(S2), P2 (2007)","journal-title":"BMC Neurosci"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Diehl, P., Cook, M.: Efficient implementation of STDP rules on spinnaker neuromorphic hardware. In: International Joint Conference on Neural Networks (IJCNN), pp. 4288\u20134295 (2014)","DOI":"10.1109\/IJCNN.2014.6889876"},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3389\/fncom.2015.00099","volume":"9","author":"P Diehl","year":"2015","unstructured":"Diehl, P., Cook, M.: Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci 9, 99 (2015)","journal-title":"Front. Comput. Neurosci"},{"issue":"5","key":"3_CR8","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/JPROC.2014.2304638","volume":"102","author":"S Furber","year":"2014","unstructured":"Furber, S., Galluppi, F., Temple, S., Plana, L.A.: The spinnaker project. Proc. IEEE 102(5), 652\u2013665 (2014). https:\/\/doi.org\/10.1109\/JPROC.2014.2304638","journal-title":"Proc. IEEE"},{"issue":"4","key":"3_CR9","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.4249\/scholarpedia.1430","volume":"2","author":"M Gewaltig","year":"2007","unstructured":"Gewaltig, M., Diesmann, M.: NEST (NEural Simulation Tool). Scholarpedia 2(4), 1430 (2007)","journal-title":"Scholarpedia"},{"key":"3_CR10","unstructured":"Goldberg, Y., Levy, O.: Word2vec explained: deriving mikolov et al\u2019.s negative-sampling word-embedding method (2014). arXiv arXiv:1402.3722"},{"issue":"8","key":"3_CR11","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.neunet.2019.09.007","volume":"121","author":"Y Hao","year":"2020","unstructured":"Hao, Y., Huang, X., Dong, M., Xu, B.: A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule. Neural Netw. 121(8), 387 (2020)","journal-title":"Neural Netw."},{"key":"3_CR12","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/0167-2789(90)90087-6","volume":"42","author":"S Harnad","year":"1990","unstructured":"Harnad, S.: The symbol grounding problem. Physica D 42, 335\u2013346 (1990)","journal-title":"Physica D"},{"key":"3_CR13","volume-title":"The Organization of Behavior: A Neuropsychological Theory","author":"D Hebb","year":"1949","unstructured":"Hebb, D.: The Organization of Behavior: A Neuropsychological Theory. Wiley, New York (1949)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Huyck, C.: Learning categories with spiking nets and spike timing dependent plasticity. In: International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 139\u2013144 (2020)","DOI":"10.1007\/978-3-030-63799-6_10"},{"issue":"4","key":"3_CR15","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/s11571-014-9282-4","volume":"8","author":"CR Huyck","year":"2014","unstructured":"Huyck, C.R., Mitchell, I.G.: Post and pre-compensatory Hebbian learning for categorisation. Cogn. Neurodyn. 8(4), 299\u2013311 (2014). https:\/\/doi.org\/10.1007\/s11571-014-9282-4","journal-title":"Cogn. Neurodyn."},{"key":"3_CR16","unstructured":"Kaggle: New article classification using LSTMS (2020). https:\/\/www.kaggle.com\/atechnohazard\/news-article-classification-using-lstms"},{"key":"3_CR17","unstructured":"Kaggle: News article classifier with different models (2020). https:\/\/www.kaggle.com\/amananandrai\/ag-news-classification-dataset?select=train.csv"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Kenter, T., Borisov, A., Rijke, M.D.: Siamese cbow: optimizing word embeddings for sentence representationst (2016). arXiv arXiv:1606.04640","DOI":"10.18653\/v1\/P16-1089"},{"issue":"4","key":"3_CR19","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1016\/j.neuron.2015.03.032","volume":"86","author":"J Lisman","year":"2015","unstructured":"Lisman, J.: The challenge of understanding the brain: where we stand in 2015. Neuron 86(4), 864\u2013882 (2015)","journal-title":"Neuron"},{"key":"3_CR20","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"W McCulloch","year":"1943","unstructured":"McCulloch, W., Pitts, W.: A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115\u2013133 (1943)","journal-title":"Bull. Math. Biophys."},{"key":"3_CR21","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/BF00275687","volume":"15","author":"E Oja","year":"1982","unstructured":"Oja, E.: A simplified neuron model as a principal component analyzer. J. Math. Biol. 15, 267\u2013273 (1982)","journal-title":"J. Math. Biol."},{"key":"3_CR22","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/TIT.1956.1056810","volume":"2","author":"N Rochester","year":"1956","unstructured":"Rochester, N., Holland, J., Haibt, L., Dudag, W.: Tests on a cell assembly theory of the action of the brain using a large digital computer. Trans. Inf. Theory IT 2, 80\u201393 (1956)","journal-title":"Trans. Inf. Theory IT"},{"key":"3_CR23","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/nature24270","volume":"550","author":"D Silver","year":"2017","unstructured":"Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550, 354\u201359 (2017)","journal-title":"Nature"},{"issue":"9","key":"3_CR24","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1038\/78829","volume":"3","author":"S Song","year":"2000","unstructured":"Song, S., Miller, K., Abbott, L.: Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(9), 919\u2013926 (2000)","journal-title":"Nat. Neurosci."},{"key":"3_CR25","unstructured":"Thrun, S., et al.: The monk\u2019s problems: a performance comparison of different learning algorithms. Technical Report, CMU-CS-91-197, Carnegie Mellon University, Pittsburgh, PA (1991)"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Vigneron, A., Martinet, J.: A critical survey of STDP in spiking neural networks for pattern recognition. In: 2020 International Joint Conference on on Neural Networks (IJCNN), pp. 1\u20139. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207239"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence XXXVIII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91100-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T00:04:50Z","timestamp":1638749090000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91100-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030910990","9783030911003"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91100-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SGAI-AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Innovative Techniques and Applications of Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"41","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sgai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/bcs-sgai.org\/ai2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}