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Erschienen in:
Basic Concepts of Cellular Automata Kapitel lesen Erstes Kapitel lesen

2012 | OriginalPaper | Buchkapitel

Computing with Spiking Neuron Networks

verfasst von : Hélène Paugam-Moisy, Sander Bohte

Erschienen in: Handbook of Natural Computing

Verlag: Springer Berlin Heidelberg

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Abstract

Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks. Highly inspired by natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike firing. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity to memorize and a strong ability to do fast adaptation. Today, the main challenge is to discover efficient learning rules that might take advantage of the specific features of SNNs while keeping the nice properties (general-purpose, easy-to-use, available simulators, etc.) of traditional connectionist models. This chapter relates the history of the “spiking neuron” in Sect. 1 and summarizes the most currently-in-use models of neurons and synaptic plasticity in Sect. 2. The computational power of SNNs is addressed in Sect. 3 and the problem of learning in networks of spiking neurons is tackled in Sect. 4, with insights into the tracks currently explored for solving it. Finally, Sect. 5 discusses application domains, implementation issues and proposes several simulation frameworks.

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Vorheriges Kapitel Cellular Automata and Lattice Boltzmann Modeling of Physical Systems
Nächstes Kapitel Image Quality Assessment — A Multiscale Geometric Analysis-Based Framework and Examples
Literatur
Abbott LF, Nelson SB (2000) Synaptic plasticity: taming the beast. Nat Neurosci 3:1178–1183CrossRef
Abeles M (1991) Corticonics: neural circuits of the cerebral cortex. Cambridge University Press, CambridgeCrossRef
Achard S, Bullmore E (2007) Efficiency and cost of economical brain functional networks. PLoS Comput Biol 3(2):e17CrossRef
Atiya A, Parlos AG (2000) New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Trans Neural Netw 11(3):697–709CrossRef
Auer P, Burgsteiner H, Maass W (2008) A learning rule for very simple universal approximators consisting of a single layer of perceptrons. Neural Netw 21(5):786–795CrossRef
Azhar H, Iftekharuddin K, Kozma R (2005) A chaos synchronization-based dynamic vision model for image segmentation. In: IJCNN 2005, International joint conference on neural networks. IEEE–INNS, Montreal, pp 3075–3080
Barber D (2003) Learning in spiking neural assemblies. In: Becker S, Thrun S, Obermayer K (eds) NIPS 2002, Advances in neural information processing systems, vol 15. MIT Press, Cambridge, MA, pp 165–172
Barber MJ, Clark JW, Anderson CH (2005) Neural representation of probabilistic information, vol 15. MIT Press, Cambridge, MA
Belatreche A, Maguire LP, McGinnity M (2007) Advances in design and application of spiking neural networks. Soft Comput-A Fusion Found Methodol Appl 11:239–248
Bell A, Parra L (2005) Maximising information yields spike timing dependent plasticity. In: Saul LK, Weiss Y, Bottou L (eds) NIPS 2004, Advances in neural information processing systems, vol 17. MIT Press, Cambridge, MA, pp 121–128
Bi G-q, Poo M-m (1998) Synaptic modification in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and polysynaptic cell type. J Neurosci 18(24):10464–10472
Bi G-q, Poo M-m (2001) Synaptic modification of correlated activity: Hebb's postulate revisited. Annu Rev Neurosci 24:139–166CrossRef
Bialek W, Rieke F, de Ruyter R, van Steveninck RR, Warland D (1991) Reading a neural code. Science 252:1854–1857CrossRef
Blum A, Rivest R (1989) Training a 3-node neural net is NP-complete. In: Proceedings of NIPS 1988, advances in neural information processing systems. MIT Press, Cambridge, MA, pp 494–501
Blumer A, Ehrenfeucht A, Haussler D, Warmuth MK (1989) Learnability and the Vapnik-Chervonenkis dimension. J ACM 36(4):929–965MathSciNetCrossRefMATH
Bobrowski O, Meir R, Shoham S, Eldar YC (2007) A neural network implementing optimal state estimation based on dynamic spike train decoding. In: Schölkopf B, Platt JC, Hoffman T (eds) NIPS 2006, Advances in neural information processing systems, vol 20. MIT Press, Cambridge, MA, pp 145–152
Bohte SM, Mozer MC (2007) Reducing the variability of neural responses: a computational theory of spike-timing-dependent plasticity. Neural Comput 19:371–403MathSciNetCrossRefMATH
Bohte SM, Kok JN, La Poutre H (2002a) Spike-prop: error-backpropagation in multi-layer networks of spiking neurons. Neurocomputing 48:17–37CrossRefMATH
Bohte SM, La Poutre H, Kok JN (2002b) Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. IEEE Trans Neural Netw 13:426–435CrossRef
Booij O, tat Nguyen H (2005) A gradient descent rule for spiking neurons emitting multiple spikes. Info Process Lett 95:552–558MathSciNetCrossRefMATH
Bouchut Y, Paugam-Moisy H, Puzenat D (2003) Asynchrony in a distributed modular neural network for multimodal integration. In: PDCS 2003, International conference on parallel and distributed computing and systems. ACTA Press, Calgary, pp 588–593
Bower JM, Beeman D (1998) The book of GENESIS: exploring realistic neural models with the General Simulation system, 2nd edn. Springer, New York
Brette R, Rudolph M, Hines T, Beeman D, Bower JM et al. (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23(3):349–398MathSciNetCrossRef
Brunel N, Latham PE (2003) Firing rate of the noisy quadratic integrate-and-fire neuron, vol 15. MIT Press, Cambridge
Buchs NJ, Senn W (2002) Spike-based synaptic plasticity and the emergence of direction selective simple cells: simulation results. J Comput Neurosci 13:167–186CrossRef
Büsing L, Maass W (2008) Simplified rules and theoretical analysis for information bottleneck optimization and PCA with spiking neurons. In: NIPS 2007, Advances in neural information processing systems, vol 20. MIT Press, Cambridge
Câteau H, Fukai T (2003) A stochastic method to predict the consequence of arbitrary forms of spike-timing-dependent plasticity. Neural Comput 15(3):597–620CrossRefMATH
Cessac B, Paugam-Moisy H, Viéville T (2010) Overview of facts and issues about neural coding by spikes. J Physiol (Paris) 104:5–18CrossRef
Chechik G (2003) Spike-timing dependent plasticity and relevant mutual information maximization. Neural Comput 15(7):1481–1510CrossRefMATH
Chevallier S, Tarroux P (2008) Covert attention with a spiking neural network. In: ICVS'08, Computer Vision Systems, Santorini, 2008. Lecture notes in computer science, vol 5008. Springer, Heidelberg, pp 56–65
Chevallier S, Paugam-Moisy H, Lemaître F (2005) Distributed processing for modelling real-time multimodal perception in a virtual robot. In: PDCN 2005, International conference on parallel and distributed computing and networks. ACTA Press, Calgary, pp 393–398
Chevallier S, Tarroux P, Paugam-Moisy H (2006) Saliency extraction with a distributed spiking neuron network. In: Verleysen M (ed) ESANN'06, Advances in computational intelligence and learning. D-Side Publishing, Evere, Belgium, pp 209–214
Chicca E, Badoni D, Dante V, d'Andreagiovanni M, Salina G, Carota L, Fusi S, Del Giudice P (2003) A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory. IEEE Trans Neural Netw 14(5):1297–1307CrossRef
Crépet A, Paugam-Moisy H, Reynaud E, Puzenat D (2000) A modular neural model for binding several modalities. In: Arabnia HR (ed) IC-AI 2000, International conference on artificial intelligence. CSREA Press, Las Vegas, pp 921–928
Cybenko G (1988) Approximation by superpositions of a sigmoidal function. Math Control Signal Syst 2:303–314MathSciNetCrossRef
Daw ND, Courville AC (2008) The pigeon as particle filter. In: NIPS 2007, Advances in neural information processing systems, vol 20. MIT Press, Cambridge, MA
Delorme A, Gautrais J, Van Rullen R, Thorpe S (1999) SpikeNET: a simulator for modeling large networks of integrate and fire neurons. Neurocomputing 26–27:989–996CrossRef
Devert A, Bredeche N, Schoenauer M (2007) Unsupervised learning of echo state networks: a case study in artificial embryogeny. In: Montmarché N et al. (eds) Artificial evolution, selected papers, Lecture Notes in Computer Science, vol 4926/2008, pp 278–290
Eguíluz VM, Chialvo GA, Cecchi DR, Baliki M, Apkarian AV (2005) Scale-free brain functional networks. Phys Rev Lett 94(1):018102CrossRef
Ermentrout GB, Kopell N (1986) Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM J Appl Math 46:233MathSciNetCrossRefMATH
Floreano D, Zufferey JC, Nicoud JD (2005) From wheels to wings with evolutionary spiking neurons. Artif Life 11(1–2):121–138CrossRef
Floreano D, Epars Y, Zufferey J-C, Mattiussi C (2006) Evolution of spiking neural circuits in autonomous mobile robots. Int J Intell Syst 21(9):1005–1024CrossRef
Funahashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2(3):183–192CrossRef
Gerstner W (1995) Time structure of the activity in neural network models. Phys Rev E 51:738–758CrossRef
Gerstner W, Kistler WM (2002a) Mathematical formulations of Hebbian learning. Biol Cybern 87(5–6):404–415CrossRefMATH
Gerstner W, Kistler W (2002b) Spiking neuron models: single neurons, populations, plasticity. Cambridge University Press, CambridgeCrossRefMATH
Gerstner W, van Hemmen JL (1994) How to describe neuronal activity: spikes, rates or assemblies? In: Cowan JD, Tesauro G, Alspector J (eds) NIPS 1993, Advances in neural information processing system, vol 6. MIT Press, Cambridge, MA, pp 463–470
Gerwinn S, Macke JH, Seeger M, Bethge M (2007) Bayesian inference for spiking neuron models with a sparsity prior. In: NIPS 2006, Advances in neural information processing systems, vol 19. MIT Press, Cambridge, MA
Hartland C, Bredeche N (2007) Using echo state networks for robot navigation behavior acquisition. In: ROBIO'07, Sanya, China
Hebb DO (1949) The organization of behavior. Wiley, New York
Heiligenberg W (1991) Neural nets in electric fish. MIT Press, Cambridge, MA
Hellmich HH, Geike M, Griep P, Rafanelli M, Klar H (2005) Emulation engine for spiking neurons and adaptive synaptic weights. In: IJCNN 2005, International joint conference on neural networks. IEEE-INNS, Montreal, pp 3261–3266
Hines ML, Carnevale NT (1997) The NEURON simulation environment. Neural Comput 9:1179–1209CrossRef
Hines ML, Carnevale NT (2004) Discrete event simulation in the NEURON environment. Neurocomputing 58–60:1117–1122CrossRef
Hines ML, Carnevale NT (2008) Translating network models to parallel hardware in NEURON. J Neurosci Methods 169:425–455CrossRef
Hodgkin AL, Huxley AF (1952) A quantitative description of ion currents and its applications to conduction and excitation in nerve membranes. J Physiol 117:500–544
Holmberg M, Gelbart D, Ramacher U, Hemmert W (2005) Automatic speech recognition with neural spike trains. In: Interspeech 2005 – Eurospeech, 9th European conference on speech communication and technology, Lisbon, pp 1253–1256
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554–2558MathSciNetCrossRef
Hopfield JJ (1995) Pattern recognition computation using action potential timing for stimulus representation. Nature 376:33–36CrossRef
Hopfield JJ, Brody CD (2000) What is a moment? “Cortical” sensory integration over a brief interval. Proc Natl Acad Sci 97(25):13919–13924CrossRef
Hopfield JJ, Brody CD (2001) What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. Proc Natl Acad Sci 98(3):1282–1287
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366CrossRef
Izhikevich EM (2004) Which model to use for cortical spiking neurons? IEEE Trans Neural Netw 15(5):1063–1070CrossRef
Izhikevich EM (2007) Solving the distal reward problem through linkage of STDP and dopamine signaling. Cereb Cortex 17(10):2443–2452CrossRef
Izhikevich EM, Desai NS (2003) Relating STDP and BCM. Neural Comput 15(7):1511–1523CrossRefMATH
Izhikevich EM, Gally JA, Edelman GM (2004) Spike-timing dynamics of neuronal groups. Cereb Cortex 14:933–944CrossRef
Jaeger H (2001) The “echo state” approach to analysing and training recurrent neural networks. Technical Report TR-GMD-148, German National Research Center for Information Technology
Jaeger H (2002) Tutorial on training recurrent neural networks, covering BPTT, RTRL, EKF and the “echo state network” approach. Technical Report TR-GMD-159, German National Research Center for Information Technology
Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunication. Science 304(5667):78–80CrossRef
Jaeger H, Lukoševičius M, Popovici D, Siewert U (2007) Optimization and applications of echo state networks with leaky-integrator neurons. Neural Netw 20(3):335–352CrossRefMATH
Jiang F, Berry H, Schoenauer M (2008a) Supervised and evolutionary learning of echo state networks. In: Rudolph G et al. (eds) Proceedings of the 10th international conference on parallel problem solving from nature: PPSN X. Lecture notes in computer science, vol 5199. Springer, pp 215–224
Jiang F, Berry H, Schoenauer M (2008b) Unsupervised learning of echo state networks: balancing the double pole. In: GECCO’08: Proceedings of the 10th annual conference on genetic and evolutionary computation, ACM, New York, pp 869–870
Johnston S, Prasad G, Maguire L, McGinnity T (2005) Comparative investigation into classical and spiking neuron implementations on FPGAs. In: ICANN 2005, International conference on artificial neural networks. Lecture notes in computer science, vol 3696. Springer, New York, pp 269–274
Judd JS (1990) Neural network design and the complexity of learning. MIT Press, Cambridge, MA
Kempter R, Gerstner W, van Hemmen JL (1999) Hebbian learning and spiking neurons. Phys Rev E 59(4):4498–4514MathSciNetCrossRef
Kistler WM (2002) Spike-timing dependent synaptic plasticity: a phenomenological framework. Biol Cyber 87(5–6):416–427CrossRefMATH
Kistler WM, Gerstner W, van Hemmen JL (1997) Reduction of Hodgkin–Huxley equations to a single-variable threshold model. Neural Comput 9:1015–1045CrossRef
Klampfl S, Legenstein R, Maass W (2009) Spiking neurons can learn to solve information bottleneck problems and extract independent components. Neural Comput 21(4):911–959MathSciNetCrossRefMATH
Koerding KP, Wolpert DM (2004) Bayesian integration in sensorimotor learning. Nature 427:244–247CrossRef
Kutch JJ (2004) Neuromorphic approaches to rehabilitation. Neuromorphic Eng 1(2):1–2
Kuwabara N, Suga N (1993) Delay lines and amplitude selectivity are created in subthalamic auditory nuclei: the brachium of the inferior colliculus of the mustached bat. J Neurophysiol 69:1713–1724
LeCun Y, Jackel LD, Bottou L, Cortes C, Denker JS, Drucker H, Guyon I, Muller UA, Sackinger E, Simard P (1995) Learning algorithms for classification: a comparison on handwritten digit recognition, vol 276. World Scientific, Singapore
Legenstein R, Maass W (2005) What makes a dynamical system computationally powerful? In: Haykin S, Principe JC, Sejnowski TJ, McWhirter JG (eds) New directions in statistical signal processing: from systems to brain. MIT Press, Cambridge, MA
Legenstein R, Näger C, Maass W (2005) What can a neuron learn with spike-time-dependent plasticity? Neural Comput 17(11):2337–2382MathSciNetCrossRefMATH
Legenstein R, Pecevski D, Maass W (2008) Theoretical analysis of learning with reward-modulated spike-timing-dependent plasticity. In: NIPS 2007, Advances in neural information processing systems, vol 20. MIT Press, Cambridge, MA
Lewicki MS (2002) Efficient coding of natural sounds. Nat Neurosci 5:356–363CrossRef
Loiselle S, Rouat J, Pressnitzer D, Thorpe S (2005) Exploration of rank order coding with spiking neural networks for speech recognition. In: IJCNN 2005, International joint conference on neural networks. IEEE–INNS Montreal, pp 2076–2080
Lukoševičius M, Jaeger H (July 2007) Overview of reservoir recipes. Technical Report 11, Jacobs University Bremen
Ma WJ, Beck JM, Pouget A (2008) Spiking networks for Bayesian inference and choice. Curr Opin Neurobiol 18(2):217–222CrossRef
Maass W (1997a) Fast sigmoidal networks via spiking neurons. Neural Comput 10:1659–1671
Maass W (1997b) Networks of spiking neurons: the third generation of neural network models. Neural Netw 10:1659–1671CrossRef
Maass W (2001) On the relevance of time in neural computation and learning. Theor Comput Sci 261:157–178 (extended version of ALT'97, in LNAI 1316:364–384)MathSciNetCrossRefMATH
Maass W, Bishop CM (eds) (1999) Pulsed neural networks. MIT Press, Cambridge, MA
Maass W, Natschläger T (1997) Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding. Netw: Comput Neural Syst 8(4):355–372CrossRefMATH
Maass W, Schmitt M (1997) On the complexity of learning for a spiking neuron. In: COLT'97, Conference on computational learning theory. ACM Press, New York, pp 54–61
Maass W, Steinbauer G, Koholka R (2002a) Autonomous fast learning in a mobile robot. In: Hager GD, Christensen HI, Bunke H, Klein R (eds) Sensor based intelligent robots, vol 2238. Springer, Berlin, pp 345–356CrossRef
Maass W, Natschläger T, Markram H (2002b) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14(11):2531–2560CrossRefMATH
Makino T (2003) A discrete event neural network simulator for general neuron model. Neural Comput Appl 11(2):210–223MATH
Markram H, Tsodyks MV (1996) Redistribution of synaptic efficacy between neocortical pyramidal neurones. Nature 382:807–809CrossRef
Markram H, Lübke J, Frotscher M, Sakmann B (1997) Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275:213–215CrossRef
Masquelier T, Thorpe SJ, Friston KJ (2007) Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput Biol 3:e31CrossRef
Mattia M, Del Giudice P (2000) Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses. Neural Comput 12: 2305–2329CrossRef
McKennoch S, Voegtlin T, Bushnell L (2009) Spike-timing error backpropagation in theta neuron networks. Neural Comput 21(1):9–45MathSciNetCrossRefMATH
Meunier D, Paugam-Moisy H (2004) A “spiking” bidirectional associative memory for modeling intermodal priming. In: NCI 2004, International conference on neural networks and computational intelligence. ACTA Press, Calgary, pp 25–30
Meunier D, Paugam-Moisy H (2005) Evolutionary supervision of a dynamical neural network allows learning with on-going weights. In: IJCNN 2005, International joint conference on neural networks. IEEE–INNS, Montreal, pp 1493–1498
Meunier D, Paugam-Moisy H (2006) Cluster detection algorithm in neural networks. In: Verleysen M (ed) ESANN'06, Advances in computational intelligence and learning. D-Side Publishing, Evere, Belgium, pp 19–24
Mitra S, Fusi S, Indiveri G (2006) A VLSI spike-driven dynamic synapse which learns only when necessary. In: Proceedings of IEEE international symposium on circuits and systems (ISCAS) 2006. IEEE Press, New York, p 4CrossRef
Mouraud A, Paugam-Moisy H (2006) Learning and discrimination through STDP in a top-down modulated associative memory. In: Verleysen M (ed) ESANN'06, Advances in computational intelligence and learning. D-Side Publishing, Evere, Belgium, pp 611–616
Mouraud A, Paugam-Moisy H, Puzenat D (2006) A distributed and multithreaded neural event driven simulation framework. In: PDCN 2006, International conference on parallel and distributed computing and networks, Innsbruck, Austria, February 2006. ACTA Press, Calgary, 2006
Mouraud A, Puzenat D (2009) Simulation of large spiking neuron networks on distributed architectures, the “DAMNED” simulator. In: Palmer-Brown D, Draganova C, Pimenidis E, Mouratidis H (eds) EANN 2009, Engineering applications of neural networks. Communications in computer and information science, vol 43. Springer, pp 359–370CrossRef
Natschläger T, Ruf B (1998a) Online clustering with spiking neurons using radial basis functions. In: Hamilton A, Smith LS (eds) Neuromorphic systems: engineering silicon from neurobiology. World Scientific, Singapore, Chap 4
Natschläger T, Ruf B (1998b) Spatial and temporal pattern analysis via spiking neurons. Netw: Comp Neural Syst 9(3):319–332CrossRefMATH
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113CrossRef
Nowotny T, Zhigulin VP, Selverston AI, Abardanel HDI, Rabinovich MI (2003) Enhancement of synchronization in a hybrid neural circuit by spike-time-dependent plasticity. J Neurosci 23(30):9776–9785
Olshausen BA, Fields DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–609CrossRef
Oster M, Whatley AM, Liu S-C, Douglas RJ (2005) A hardware/software framework for real-time spiking systems. In: ICANN 2005, International conference on artificial neural networks. Lecture notes in computer science, vol 3696. Springer, New York, pp 161–166
Panchev C, Wermter S (2006) Temporal sequence detection with spiking neurons: towards recognizing robot language instructions. Connect Sci 18:1–22CrossRefMATH
Paugam-Moisy H, Martinez R, Bengio S (2008) Delay learning and polychronization for reservoir computing. Neurocomputing 71(7–9):1143–1158CrossRef
Perrinet L, Samuelides M (2002) Sparse image coding using an asynchronous spiking neural network. In: Verleysen M (ed) ESANN 2002, European symposium on artificial neural networks. D-Side Publishing, Evere, Belgium, pp 313–318
Pfister J-P, Gerstner W (2006) Beyond pair-based STDP: a phenomenological rule for spike triplet and frequency effects. In: NIPS 2005, Advances in neural information processing systems, vol 18. MIT Press, Cambridge, MA, pp 1083–1090
Pfister J-P, Barber D, Gerstner W (2003) Optimal Hebbian learning: a probabilistic point of view. In: Kaynak O, Alpaydin E, Oja E, Xu L (eds) ICANN/ICONIP 2003, International conference on artificial neural networks. Lecture notes in computer science, vol 2714. Springer, Heidelberg, pp 92–98
Pfister J-P, Toyoizumi T, Barber D, Gerstner W (2006) Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. Neural Comput 18(6):1318–1348MathSciNetCrossRefMATH
Poggio T, Girosi F (1989) Networks for approximation and learning. Proc IEEE 78(9):1481–1497CrossRef
Rao RPN (2005) Hierarchical Bayesian inference in networks of spiking neurons. In: Saul LK, Weiss Y, Bottou L (eds) NIPS 2004, Advances in neural information processing systems, vol 17. MIT Press, Cambridge, MA, pp 1113–1120
Recce M (1999) Encoding information in neuronal activity. In: Maass W, Bishop CM (eds) Pulsed neural networks. MIT Press, Cambridge
Reutimann J, Giugliano M, Fusi S (2003) Event-driven simulation of spiking neurons with stochastic dynamics. Neural Comput 15(4):811–830CrossRefMATH
Rochel O, Martinez D (2003) An event-driven framework for the simulation of networks of spiking neurons. In: Verleysen M (ed) ESANN'03, European symposium on artificial neural networks. D-Side Publishing, Evere, Belgium, pp 295–300
Rubin J, Lee DD, Sompolinsky H (2001) Equilibrium properties of temporal asymmetric Hebbian plasticity. Phys Rev Lett 86:364–366CrossRef
Rudolph M, Destexhe A (2006) Event-based simulation strategy for conductance-based synaptic interactions and plasticity. Neurocomputing 69: 1130–1133CrossRef
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by back-propagating errors. Nature 323:533–536CrossRef
Sahani M, Dayan P (2003) Doubly distributional population codes: Simultaneous representation of uncertainty and multiplicity. Neural Comput 15:2255–2279CrossRefMATH
Salmen M, Plöger PG (2005) Echo state networks used for motor control. In: ICRA 2005, International joint conference on robotics and automation. IEEE, New York, pp 1953–1958CrossRef
Saudargiene A, Porr B, Wörgötter F (2004) How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comput 16(3): 595–625CrossRefMATH
Schmidhuber J, Wiestra D, Gagliolo D, Gomez M (2007) Training recurrent networks by Evolino. Neural Comput 19(3):757–779CrossRefMATH
Schmitt M (1998) On computing Boolean functions by a spiking neuron. Ann Math Artif Intell 24: 181–191CrossRefMATH
Schmitt M (2004) On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions. IEEE Trans Neural Netw 15(5): 995–1001CrossRef
Schrauwen B, Van Campenhout J (2004a) Extending SpikeProp. In: Proceedings of the international joint conference on neural networks, vol 1. IEEE Press, New York, pp 471–476
Schrauwen B, Van Campenhout J (2004b) Improving spikeprop: enhancements to an error-backpropagation rule for spiking neural networks. In: Proceedings of the 15th ProRISC workshop, vol 11
Schrauwen B, D'Haene M, Verstraeten D, Van Campenhout J (2007a) Compact hardware for real-time speech recognition using a liquid state machine. In: IJCNN 2007, International joint conference on neural networks, 2007, pp 1097–1102
Schrauwen B, Verstraeten D, Van Campenhout J (2007b) An overview of reservoir computing: theory, applications and implementations. In: Verleysen M (ed) ESANN'07, Advances in computational intelligence and learning. D-Side Publishing, Evere, Belgium, pp 471–482
Schrauwen B, Büsing L, Legenstein R (2009) On computational power and the order-chaos phase transition in reservoir computing. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) NIPS'08, advances in neural information processing systems, vol 21. MIT Press, Cambridge, MA, pp 1425–1432
Séguie R, Mercier D (2002) Audio-visual speech recognition one pass learning with spiking neurons. In: ICANN'02, International conference on artificial neural networks. Springer, Berlin, pp 1207–1212
Senn W, Markram H, Tsodyks M (2001) An algorithm for modifying neurotransmitter release probability based on pre- and post-synaptic spike timing. Neural Comput 13(1):35–68CrossRefMATH
Siegelmann HT (1999) Neural networks and analog computation, beyond the Turing limit. Birkhauser, Boston, MAMATH
Smith EC, Lewicki MS (2006) Efficient auditory coding. Nature 439:978–982CrossRef
Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-time dependent synaptic plasticity. Nat Neurosci 3(9):919–926CrossRef
Sporns O, Tononi G, Kotter R (2005) The human connectome: a structural description of the human brain. PLoS Comp Biol 1(4):e42CrossRef
Standage DI, Trappenberg TP (2005) Differences in the subthreshold dynamics of leaky integrate-and-fire and Hodgkin-Huxley neuron models. In: IJCNN 2005, International joint conference on neural networks. IEEE–INNS, Montreal, pp 396–399
Steil JJ (2004) Backpropagation-decorrelation: Online recurrent learning with O(n) complexity. In: IJCNN 2004, International joint conference on neural networks, vol 1. IEEE–INNS, Montreal, pp 843–848
Stein RB (1965) A theoretical analysis of neuronal variability. Biophys J 5:173–194CrossRef
Tenore F (2004) Prototyping neural networks for legged locomotion using custom aVLSI chips. Neuromorphic Eng 1(2):4, 8
Thorpe SJ, Gautrais J (1997) Rapid visual processing using spike asynchrony. In: Mozer M, Jordan MI, Petsche T (eds) NIPS 1996, Advances in neural information processing systems, volume 9. MIT Press, Cambridge, MA, pp 901–907
Thorpe S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381(6582):520–522CrossRef
Thorpe S, Delorme A, Van Rullen R (2001) Spike-based strategies for rapid processing. Neural Netw 14:715–725CrossRef
Toyoizumi T, Pfister J-P, Aihara K, Gerstner W (2005a) Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. Proc Natl Acad Sci USA 102(14):5239–5244CrossRef
Toyoizumi T, Pfister J-P, Aihara K, Gerstner W (2005b) Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model. In: Saul LK, Weiss Y, Bottou L (eds) NIPS 2004, Advances in neural information processing systems, vol 17. MIT Press, Cambridge, MA, pp 1409–1416
Upegui A, Peña Reyes CA, Sanchez E (2004) An FPGA platform for on-line topology exploration of spiking neural networks. Microprocess Microsyst 29:211–223CrossRef
van Hulle M (2000) Faithful representations and topographic maps: from distortion- to information-based self-organization. Wiley, New York
Van Rullen R, Thorpe S (2001) Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex. Neural Comput 13: 1255–1283CrossRefMATH
Vapnik VN (1998) Statistical learning theory. Wiley, New YorkMATH
Verstraeten D, Schrauwen B, Stroobandt D (2005) Isolated word recognition using a liquid state machine. In: Verleysen M (ed) ESANN'05, European symposium on artificial neural networks. D-Side Publishing, Evere, Belgium, pp 435–440
Verstraeten D, Schrauwen B, D'Haene M, Stroobandt D (2007) An experimental unification of reservoir computing methods. Neural Netw 20(3):391–403CrossRefMATH
Viéville T, Crahay S (2004) Using an Hebbian learning rule for multi-class SVM classifiers. J Comput Neurosci 17(3):271–287CrossRef
Volkmer M (2004) A pulsed neural network model of spectro-temporal receptive fields and population coding in auditory cortex. Nat Comput 3: 177–193MathSciNetCrossRef
Wang G, Pavel M (2005) A spiking neuron representation of auditory signals. In: IJCNN 2005, International joint conference on neural networks. IEEE–INNS, Montreal, pp 416–421
Watts L (1994) Event-driven simulation of networks of spiking neurons. In: Cowan JD, Tesauro G, Alspector J (eds) NIPS 1993, Advances in neural information processing systems, vol 6. MIT Press, Cambridge, MA, pp 927–934
Watts D, Strogatz S (1998) Collective dynamics of “small-world” networks. Nature 393:440–442CrossRef
Wennekers T, Sommer F, Aertsen A (2003) Editorial: cell assemblies. Theory Biosci (special issue) 122:1–4
Wu S, Chen D, Niranjan M, Amari S (2003) Sequential Bayesian decoding with a population of neurons. Neural Comput 15:993–1012CrossRefMATH
Wysoski SG, Benuskova L, Kasabov N (2008) Fast and adaptive network of spiking neurons for multi-view visual pattern recognition. Neurocomputing 71(13–15):2563–2575CrossRef
Xie X, Seung HS (2004) Learning in neural networks by reinforcement of irregular spiking. Phys Rev E 69(041909)MathSciNet
Xin J, Embrechts MJ (2001) Supervised learning with spiking neuron networks. In: Proceedings of the IJCNN 2001 IEEE international joint conference on neural networks, Washington, DC, vol 3. IEEE Press, New York, pp 1772–1777
Zador AM, Pearlmutter BA (1996) VC dimension of an integrate-and-fire neuron model. Neural Comput 8(3):611–624CrossRef
Zemel RS, Dayan P, Pouget A (1998) Probabilistic interpretation of population codes. Neural Comput 10:403–430CrossRef
Metadaten
Titel
Computing with Spiking Neuron Networks
verfasst von
Hélène Paugam-Moisy
Sander Bohte
Copyright-Jahr
2012
Verlag
Springer Berlin Heidelberg
Buch
Handbook of Natural Computing

Print ISBN: 978-3-540-92909-3

Electronic ISBN: 978-3-540-92910-9

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-3-540-92910-9_10