{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:18Z","timestamp":1760245038459,"version":"3.37.3"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2015,11,17]],"date-time":"2015-11-17T00:00:00Z","timestamp":1447718400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004963","name":"Seventh Framework Programme (BE)","doi-asserted-by":"publisher","award":["604102"],"award-info":[{"award-number":["604102"]}],"id":[{"id":"10.13039\/501100004963","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004963","name":"Seventh Framework Programme (BE)","doi-asserted-by":"publisher","award":["604102"],"award-info":[{"award-number":["604102"]}],"id":[{"id":"10.13039\/501100004963","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005713","name":"Technische Universit\u00e4t M\u00fcnchen (DE)","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005713","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2016,8]]},"DOI":"10.1007\/s11063-015-9478-6","type":"journal-article","created":{"date-parts":[[2015,11,17]],"date-time":"2015-11-17T10:23:39Z","timestamp":1447755819000},"page":"103-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Computation by Time"],"prefix":"10.1007","volume":"44","author":[{"given":"Florian","family":"Walter","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian","family":"R\u00f6hrbein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alois","family":"Knoll","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,11,17]]},"reference":[{"issue":"5","key":"9478_CR1","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/JPROC.2014.2313565","volume":"102","author":"BV Benjamin","year":"2014","unstructured":"Benjamin BV, Peiran Gao, McQuinn E, Choudhary S, Chandrasekaran AR, Bussat JM, Alvarez-Icaza R, Arthur JV, Merolla PA, Boahen K (2014) Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc IEEE 102(5):699\u2013716","journal-title":"Proc IEEE"},{"issue":"1","key":"9478_CR2","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1146\/annurev.neuro.24.1.139","volume":"24","author":"G Bi","year":"2001","unstructured":"Bi G, Poo M (2001) Synaptic modification by correlated activity: Hebb\u2019s postulate revisited. Annu Rev Neurosci 24(1):139\u2013166","journal-title":"Annu Rev Neurosci"},{"issue":"2","key":"9478_CR3","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1023\/B:NACO.0000027755.02868.60","volume":"3","author":"SM Bohte","year":"2004","unstructured":"Bohte SM (2004) The evidence for neural information processing with precise spike-times: a survey: natural computing. Nat Comput 3(2):195\u2013206","journal-title":"Nat Comput"},{"issue":"1\u20134","key":"9478_CR4","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S0925-2312(01)00658-0","volume":"48","author":"SM Bohte","year":"2002","unstructured":"Bohte SM, Kok JN, La Poutr\u00e9 H (2002) Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1\u20134):17\u201337","journal-title":"Neurocomputing"},{"issue":"2","key":"9478_CR5","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1109\/72.991428","volume":"13","author":"SM Bohte","year":"2002","unstructured":"Bohte SM, La Poutre H, Kok JN (2002) Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. Neural Netw IEEE Trans 13(2):426\u2013435","journal-title":"Neural Netw IEEE Trans"},{"issue":"23","key":"9478_CR6","doi-asserted-by":"crossref","first-page":"9565","DOI":"10.1523\/JNEUROSCI.4098-12.2013","volume":"33","author":"J Brea","year":"2013","unstructured":"Brea J, Senn W, Pfister JP (2013) Matching recall and storage in sequence learning with spiking neural networks. J Neurosci 33(23):9565\u20139575","journal-title":"J Neurosci"},{"issue":"3","key":"9478_CR7","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1023\/A:1008925309027","volume":"8","author":"N Brunel","year":"2000","unstructured":"Brunel N (2000) Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8(3):183\u2013208","journal-title":"J Comput Neurosci"},{"key":"9478_CR8","unstructured":"Carnell A, Richardson D (2005) Linear algebra for time series of spikes. In: Proceedings of ESANN, pp 363\u2013368"},{"key":"9478_CR9","unstructured":"Carnevale N, Hines M (2015) NEURON for empirically-based simulations of neurons and networks of neurons: project homepage. http:\/\/www.neuron.yale.edu\/neuron\/"},{"key":"9478_CR10","doi-asserted-by":"crossref","unstructured":"Cyr A, Boukadoum M, Th\u00e9riault F (2014) Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired Robot\u2019s controller. Front Neurorobot 8(21)","DOI":"10.3389\/fnbot.2014.00021"},{"key":"9478_CR11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1590\/S0001-37652001000200006","volume":"73","author":"MC Diamond","year":"2001","unstructured":"Diamond MC (2001) Response of the brain to enrichment. Anais da Academia Brasileira de Ci\u00eancias 73:211\u2013220","journal-title":"Anais da Academia Brasileira de Ci\u00eancias"},{"issue":"6","key":"9478_CR12","doi-asserted-by":"crossref","first-page":"3648","DOI":"10.1152\/jn.00364.2007","volume":"98","author":"MA Farries","year":"2007","unstructured":"Farries MA, Fairhall AL (2007) Reinforcement learning with modulated spike timing-dependent synaptic plasticity. J Neurophys 98(6):3648\u20133665","journal-title":"J Neurophys"},{"key":"9478_CR13","series-title":"From intelligent robotics to artificial life, lecture notes in computer science","first-page":"38","volume-title":"Evolutionary robotics","author":"D Floreano","year":"2001","unstructured":"Floreano D, Mattiussi C (2001) Evolution of spiking neural controllers for autonomous vision-based robots. In: Goos G, Hartmanis J, van Leeuwen J, Gomi T (eds) Evolutionary robotics, vol 2217., From intelligent robotics to artificial life, lecture notes in computer scienceSpringer, Berlin, pp 38\u201361"},{"key":"9478_CR14","doi-asserted-by":"crossref","unstructured":"Florian RV (2005) A reinforcement learning algorithm for spiking neural networks. In: Proceedings of the seventh international symposium on symbolic and numeric algorithms for scientific computing, SYNASC \u201905. IEEE Computer Society, Washington, DC, USA","DOI":"10.1109\/SYNASC.2005.13"},{"issue":"4","key":"9478_CR15","doi-asserted-by":"crossref","first-page":"e1003,024","DOI":"10.1371\/journal.pcbi.1003024","volume":"9","author":"N Fr\u00e9maux","year":"2013","unstructured":"Fr\u00e9maux N, Sprekeler H, Gerstner W (2013) Reinforcement learning using a continuous time actor-critic framework with spiking neurons. PLoS Comput Biol 9(4):e1003,024","journal-title":"PLoS Comput Biol"},{"key":"9478_CR16","doi-asserted-by":"crossref","unstructured":"Furber S, Brown A (2009) Biologically-inspired massively-parallel architectures - computing beyond a million processors. In: Application of concurrency to system design, 2009 (ACSD \u201909). Ninth international conference on, pp 3\u201312","DOI":"10.1109\/ACSD.2009.17"},{"issue":"12","key":"9478_CR17","doi-asserted-by":"crossref","first-page":"2454","DOI":"10.1109\/TC.2012.142","volume":"62","author":"SB Furber","year":"2013","unstructured":"Furber SB, Lester DR, Plana LA, Garside JD, Painkras E, Temple S, Brown AD (2013) Overview of the spinnaker system architecture. Comput IEEE Trans 62(12):2454\u20132467","journal-title":"Comput IEEE Trans"},{"issue":"6595","key":"9478_CR18","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1038\/383076a0","volume":"383","author":"W Gerstner","year":"1996","unstructured":"Gerstner W, Kempter R, van Hemmen JL, Wagner H (1996) A neuronal learning rule for sub-millisecond temporal coding. Nature 383(6595):76\u201378","journal-title":"Nature"},{"key":"9478_CR19","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511815706","volume-title":"Spiking neuron models: single neurons, populations, plasticity","author":"W Gerstner","year":"2002","unstructured":"Gerstner W, Kistler WM (2002) Spiking neuron models: single neurons, populations, plasticity. Cambridge University Press, Cambridge"},{"key":"9478_CR20","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1007\/978-94-007-3858-4_18","volume-title":"Computational systems neurobiology","author":"MO Gewaltig","year":"2012","unstructured":"Gewaltig MO, Morrison A, Plesser HE (2012) NEST by example: an introduction to the neural simulation tool NEST. In: Le Nov\u00e8re N (ed) Computational systems neurobiology. Springer, The Netherlands, pp 533\u2013558"},{"issue":"2","key":"9478_CR21","doi-asserted-by":"crossref","first-page":"192","DOI":"10.3389\/neuro.01.026.2009","volume":"3","author":"FM Goodman Dan","year":"2009","unstructured":"Goodman Dan FM, Brette R (2009) The brian simulator. Front Neurosci 3(2):192","journal-title":"Front Neurosci"},{"key":"9478_CR22","unstructured":"Gr\u00fcning A, Bohte SM (2014) Spiking neural networks: principles and challenges. In: ESANN 2014. 22nd European symposium on artificial neural networks, computational intelligence and machine learning. Bruges, April 23\u201325, 2014. i6doc.com, Louvain-La-Neuve"},{"key":"9478_CR23","first-page":"134","volume":"25","author":"R G\u00fctig","year":"2014","unstructured":"G\u00fctig R (2014) To spike, or when to spike? Theor Comput Neurosci 25:134\u2013139","journal-title":"Theor Comput Neurosci"},{"issue":"3","key":"9478_CR24","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1038\/nn1643","volume":"9","author":"R G\u00fctig","year":"2006","unstructured":"G\u00fctig R, Sompolinsky H (2006) The tempotron: a neuron that learns spike timing-based decisions. Nat Neurosci 9(3):420\u2013428","journal-title":"Nat Neurosci"},{"key":"9478_CR25","volume-title":"The organization of behavior: a neuropsychological theory","author":"DO Hebb","year":"1949","unstructured":"Hebb DO (1949) The organization of behavior: a neuropsychological theory. Wiley, New York"},{"key":"9478_CR26","first-page":"493","volume-title":"Advances in neural information processing systems 8","author":"SE Hihi","year":"1996","unstructured":"Hihi SE, Bengio Y (1996) Hierarchical recurrent neural networks for long-term dependencies. In: Touretzky DS, Mozer MC, Hasselmo ME (eds) Advances in neural information processing systems 8. MIT Press, Cambridge, pp 493\u2013499"},{"issue":"4","key":"9478_CR27","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1113\/jphysiol.1952.sp004764","volume":"117","author":"AL Hodgkin","year":"1952","unstructured":"Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117(4):500\u2013544","journal-title":"J Physiol"},{"issue":"2","key":"9478_CR28","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","volume":"4","author":"K Hornik","year":"1991","unstructured":"Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251\u2013257","journal-title":"Neural Netw"},{"issue":"6","key":"9478_CR29","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1109\/TNN.2003.820440","volume":"14","author":"EM Izhikevich","year":"2003","unstructured":"Izhikevich EM (2003) Simple model of spiking neurons. Neural Netw IEEE Trans 14(6):1569\u20131572","journal-title":"Neural Netw IEEE Trans"},{"issue":"10","key":"9478_CR30","doi-asserted-by":"crossref","first-page":"2443","DOI":"10.1093\/cercor\/bhl152","volume":"17","author":"EM Izhikevich","year":"2007","unstructured":"Izhikevich EM (2007) Solving the distal reward problem through linkage of STDP and dopamine signaling. Cereb Cortex 17(10):2443\u20132452","journal-title":"Cereb Cortex"},{"issue":"5","key":"9478_CR31","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/MCSE.2010.112","volume":"12","author":"X Jin","year":"2010","unstructured":"Jin X, Lujan M, Plana LA, Davies S, Temple S, Furber SB (2010) Modeling spiking neural networks on spinnaker. Comput Sci Eng 12(5):91\u201397","journal-title":"Comput Sci Eng"},{"key":"9478_CR32","series-title":"Lecture notes in computer science","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/978-3-642-10677-4_48","volume-title":"Neural information processing","author":"X Jin","year":"2009","unstructured":"Jin X, Rast A, Galluppi F, Khan M, Furber S (2009) Implementing learning on the spinnaker universal neural chip multiprocessor. In: Leung C, Lee M, Chan J (eds) Neural information processing, vol 5863., Lecture notes in computer scienceSpringer, Berlin, pp 425\u2013432"},{"key":"9478_CR33","first-page":"1461","volume-title":"Principles of neural science","author":"ER Kandel","year":"2013","unstructured":"Kandel ER, Siegelbaum SA (2013) Cellular mechanisms of implicit memory storage and the biological basis of individuality. In: Kandel ER, Schwartz JH, Jessel TM, Siegelbaum SA, Hudspeth AJ (eds) Principles of neural science. McGraw-Hill, New York, pp 1461\u20131486"},{"key":"9478_CR34","first-page":"210","volume-title":"Principles of neural science","author":"ER Kandel","year":"2013","unstructured":"Kandel ER, Siegelbaum SA (2013) Synaptic integration in the central nervous system. In: Kandel ER, Schwartz JH, Jessel TM, Siegelbaum SA, Hudspeth AJ (eds) Principles of neural science. McGraw-Hill, New York, pp 210\u2013235"},{"key":"9478_CR35","first-page":"148","volume-title":"Principles of neural science","author":"J Koester","year":"2013","unstructured":"Koester J, Siegelbaum SA (2013) Propagated signaling: the action potential. In: Kandel ER, Schwartz JH, Jessel TM, Siegelbaum SA, Hudspeth AJ (eds) Principles of neural science. McGraw-Hill, New York, pp 148\u2013176"},{"issue":"3","key":"9478_CR36","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.4249\/scholarpedia.1365","volume":"3","author":"J Krichmar","year":"2008","unstructured":"Krichmar J (2008) Neurorobotics. Scholarpedia 3(3):1365","journal-title":"Scholarpedia"},{"key":"9478_CR37","unstructured":"Krichmar J (2015) CARLsim: GPU-accelerated spiking neural network simulator: project homepage. http:\/\/www.socsci.uci.edu\/~jkrichma\/CARLsim\/index.html"},{"issue":"7553","key":"9478_CR38","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"11","key":"9478_CR39","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1162\/0899766054796888","volume":"17","author":"R Legenstein","year":"2005","unstructured":"Legenstein R, Naeger C, Maass W (2005) What can a neuron learn with spike-timing-dependent plasticity? Neural Comput 17(11):2337\u20132382","journal-title":"Neural Comput"},{"issue":"10","key":"9478_CR40","doi-asserted-by":"crossref","first-page":"e1000,180","DOI":"10.1371\/journal.pcbi.1000180","volume":"4","author":"R Legenstein","year":"2008","unstructured":"Legenstein R, Pecevski D, Maass W (2008) A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLoS Comput Biol 4(10):e1000,180","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"9478_CR41","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","volume":"3","author":"M Luko\u0161evi\u010dius","year":"2009","unstructured":"Luko\u0161evi\u010dius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev 3(3):127\u2013149","journal-title":"Comput Sci Rev"},{"issue":"9","key":"9478_CR42","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","volume":"10","author":"W Maass","year":"1997","unstructured":"Maass W (1997) Networks of spiking neurons: the third generation of neural network models. Neural Netw 10(9):1659\u20131671","journal-title":"Neural Netw"},{"key":"9478_CR43","unstructured":"Maass W, Jaeger H, Steil J, Dominey PF, Schrauwen B (2015) Web portal for reservoir computing. http:\/\/organic.elis.ugent.be\/"},{"issue":"11","key":"9478_CR44","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1162\/089976602760407955","volume":"14","author":"W Maass","year":"2002","unstructured":"Maass W, Natschl\u00e4ger T, Markram H (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14(11):2531\u20132560","journal-title":"Neural Comput"},{"issue":"5297","key":"9478_CR45","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1126\/science.275.5297.213","volume":"275","author":"H Markram","year":"1997","unstructured":"Markram H, L\u00fcbke J, Frotscher M, Sakmann B (1997) Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275(5297):213\u2013215","journal-title":"Science"},{"issue":"2","key":"9478_CR46","doi-asserted-by":"crossref","first-page":"e31","DOI":"10.1371\/journal.pcbi.0030031","volume":"3","author":"T Masquelier","year":"2007","unstructured":"Masquelier T, Thorpe SJ (2007) Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput Biol 3(2):e31","journal-title":"PLoS Comput Biol"},{"issue":"4","key":"9478_CR47","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115\u2013133","journal-title":"Bull Math Biophys"},{"issue":"4","key":"9478_CR48","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1016\/j.neuron.2014.03.026","volume":"82","author":"RM Memmesheimer","year":"2014","unstructured":"Memmesheimer RM, Rubin R, \u00d6lveczky BP, Sompolinsky H (2014) Learning precisely timed spikes. Neuron 82(4):925\u2013938","journal-title":"Neuron"},{"issue":"6","key":"9478_CR49","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s00422-008-0233-1","volume":"98","author":"A Morrison","year":"2008","unstructured":"Morrison A, Diesmann M, Gerstner W (2008) Phenomenological models of synaptic plasticity based on spike timing. Biol Cybern 98(6):459\u2013478","journal-title":"Biol Cybern"},{"issue":"3","key":"9478_CR50","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1088\/0954-898X_9_3_003","volume":"9","author":"T Natschl\u00e4ger","year":"1998","unstructured":"Natschl\u00e4ger T, Ruf B (1998) Spatial and temporal pattern analysis via spiking neurons. Network 9(3):319\u2013332","journal-title":"Network"},{"key":"9478_CR51","unstructured":"NEST Initiative (2015) NEST: project homepage. http:\/\/www.nest-initiative.org\/"},{"issue":"06","key":"9478_CR52","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1142\/S0129065710002577","volume":"20","author":"C Nichols","year":"2010","unstructured":"Nichols C, McDaid LJ, Siddique NH (2010) Case study on a self-organizing spiking neural network for robot navigation. Int J Neural Syst 20(06):501\u2013508","journal-title":"Int J Neural Syst"},{"key":"9478_CR53","doi-asserted-by":"crossref","unstructured":"Norton D, Ventura D (2006) Preparing more effective liquid state machines using hebbian learning. In: Neural networks, 2006. IJCNN \u201906. International joint conference on, pp 4243\u20134248","DOI":"10.1109\/IJCNN.2006.246996"},{"issue":"38","key":"9478_CR54","doi-asserted-by":"crossref","first-page":"9673","DOI":"10.1523\/JNEUROSCI.1425-06.2006","volume":"26","author":"JP Pfister","year":"2006","unstructured":"Pfister JP (2006) Triplets of spikes in a model of spike timing-dependent plasticity. J Neurosci 26(38):9673\u20139682","journal-title":"J Neurosci"},{"key":"9478_CR55","doi-asserted-by":"crossref","first-page":"98","DOI":"10.3389\/fncom.2013.00098","volume":"7","author":"F Ponulak","year":"2013","unstructured":"Ponulak F, Hopfield JJ (2013) Rapid, parallel path planning by propagating wavefronts of spiking neural activity. Front Comput Neurosci 7:98","journal-title":"Front Comput Neurosci"},{"issue":"2","key":"9478_CR56","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1162\/neco.2009.11-08-901","volume":"22","author":"F Ponulak","year":"2009","unstructured":"Ponulak F, Kasi\u0144ski A (2009) Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting. Neural Comput 22(2):467\u2013510","journal-title":"Neural Comput"},{"issue":"4","key":"9478_CR57","doi-asserted-by":"crossref","first-page":"409","DOI":"10.55782\/ane-2011-1862","volume":"71","author":"F Ponulak","year":"2011","unstructured":"Ponulak F, Kasinski A (2011) Introduction to spiking neural networks: information processing, learning and applications. Acta Neurobiol Exp 71(4):409\u2013433","journal-title":"Acta Neurobiol Exp"},{"issue":"12","key":"9478_CR58","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.1162\/neco.2006.18.12.2959","volume":"18","author":"E Ros","year":"2006","unstructured":"Ros E, Carrillo R, Ortigosa EM, Barbour B, Ag\u00eds R (2006) Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics. Neural Comput 18(12):2959\u20132993","journal-title":"Neural Comput"},{"issue":"6","key":"9478_CR59","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386\u2013408","journal-title":"Psychol Rev"},{"key":"9478_CR60","doi-asserted-by":"crossref","unstructured":"Schemmel J, Br\u00fcderle D, Gr\u00fcbl A, Hock M, Meier K, Millner S (2010) A wafer-scale neuromorphic hardware system for large-scale neural modeling. In: Circuits and systems (ISCAS), proceedings of 2010 IEEE international symposium on, pp 1947\u20131950","DOI":"10.1109\/ISCAS.2010.5536970"},{"key":"9478_CR61","doi-asserted-by":"crossref","unstructured":"Schemmel J, Grubl A, Hartmann S, Kononov A, Mayr C, Meier K, Millner S, Partzsch J, Schiefer S, Scholze S, Schuffny R, Schwartz M (2012) Live demonstration: a scaled-down version of the BrainScaleS wafer-scale neuromorphic system. In: Circuits and systems (ISCAS), 2012 IEEE international symposium on, p 702","DOI":"10.1109\/ISCAS.2012.6272131"},{"key":"9478_CR62","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/978-3-642-30574-0_37","volume-title":"Springer handbook of bio-\/neuroinformatics","author":"S Schliebs","year":"2014","unstructured":"Schliebs S, Kasabov N (2014) Computational modeling with spiking neural networks. In: Kasabov N (ed) Springer handbook of bio-\/neuroinformatics. Springer, Berlin, pp 625\u2013646"},{"key":"9478_CR63","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"9478_CR64","first-page":"1","volume-title":"Encyclopedia of computational neuroscience","author":"W Senn","year":"2014","unstructured":"Senn W, Pfister JP (2014) Reinforcement learning in cortical networks. In: Jaeger D, Jung R (eds) Encyclopedia of computational neuroscience. Springer, New York, pp 1\u20139"},{"issue":"6","key":"9478_CR65","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1016\/S0896-6273(01)00542-6","volume":"32","author":"PJ Sj\u00f6str\u00f6m","year":"2001","unstructured":"Sj\u00f6str\u00f6m PJ, Turrigiano GG, Nelson SB (2001) Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32(6):1149\u20131164","journal-title":"Neuron"},{"issue":"2","key":"9478_CR66","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1162\/NECO_a_00396","volume":"25","author":"I Sporea","year":"2012","unstructured":"Sporea I, Gr\u00fcning A (2012) Supervised learning in multilayer spiking neural networks. Neural Comput 25(2):473\u2013509","journal-title":"Neural Comput"},{"issue":"1\u20132","key":"9478_CR67","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0004-3702(99)00052-1","volume":"112","author":"RS Sutton","year":"1999","unstructured":"Sutton RS, Precup D, Singh S (1999) Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning. Artif Intell 112(1\u20132):181\u2013211","journal-title":"Artif Intell"},{"issue":"4","key":"9478_CR68","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/JPROC.2014.2308604","volume":"102","author":"J Tani","year":"2014","unstructured":"Tani J (2014) Self-organization and compositionality in cognitive brains: a neurorobotics study. Proc IEEE 102(4):586\u2013605","journal-title":"Proc IEEE"},{"key":"9478_CR69","unstructured":"The Human Brain Project (2015) Project homepage. https:\/\/www.humanbrainproject.eu"},{"key":"9478_CR70","doi-asserted-by":"crossref","unstructured":"Walter F, R\u00f6hrbein F, Knoll A (2015) Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks. Neural Netw","DOI":"10.1016\/j.neunet.2015.07.004"},{"issue":"10","key":"9478_CR71","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/5.58337","volume":"78","author":"PJ Werbos","year":"1990","unstructured":"Werbos PJ (1990) Backpropagation through time: what it does and how to do it. Proc IEEE 78(10):1550\u20131560","journal-title":"Proc IEEE"},{"key":"9478_CR72","series-title":"Lecture notes in computer science","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/11840817_7","volume-title":"Artificial neural networks\u2014ICANN 2006","author":"S Wysoski","year":"2006","unstructured":"Wysoski S, Benuskova L, Kasabov N (2006) On-line learning with structural adaptation in a network of spiking neurons for visual pattern recognition. In: Kollias S, Stafylopatis A, Duch W, Oja E (eds) Artificial neural networks\u2014ICANN 2006, vol 4131., Lecture notes in computer scienceSpringer, Berlin, pp 61\u201370"},{"issue":"6","key":"9478_CR73","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1162\/NECO_a_00450","volume":"25","author":"Y Xu","year":"2013","unstructured":"Xu Y, Zeng X, Zhong S (2013) A new supervised learning algorithm for spiking neurons. Neural Comput 25(6):1472\u20131511","journal-title":"Neural Comput"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-015-9478-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-015-9478-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-015-9478-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T13:55:27Z","timestamp":1718200527000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-015-9478-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,17]]},"references-count":73,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2016,8]]}},"alternative-id":["9478"],"URL":"https:\/\/doi.org\/10.1007\/s11063-015-9478-6","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2015,11,17]]}}}