Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2011 | 71 | 4 |
Tytuł artykułu

Introduction to spiking neural networks: Information processing, learning and applications

Treść / Zawartość
Warianty tytułu
Języki publikacji
The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.
Słowa kluczowe
Opis fizyczny
  • Institute of Control and Information Engineering, Poznan University of Technology, Poznan, Poland
  • Princeton Neuroscience Institute and Department of Monecular Biology, Princeton University, Princeton, USA
  • Institute of Control and Information Engineering, Poznan University of Technology, Poznan, Poland
  • Abbott LF, Nelson SB (2000) Synaptic plasticity: taming the beast. Nat Neurosci 3: 1178-1183.
  • Abeles M (1982) Local Cortical Circuits: An Electrophysiological Study. Springer-Verlag, Berlin, DE.
  • Abeles M, Prut Y, Bergman H, Vaadia E (1994) Synchronisation in neuronal transmission and its impor­tance for information processing. In: Temporal Coding in the Brain (Buzsaki G, Llinas R, Singer W, Berthoz A, Christen T, Eds). Springer-Verlag, Berlin, DE. p. 39-50.
  • Achard P, De Schutter E (2008) Calcium, synaptic plasticity and intrinsic homeostasis in purkinje neuron models. Front Comput Neurosci 2: 8.
  • Adrian ED, Zotterman Y (1926) The impulses produced by sensory nerve-endings: Part II. The response of a single end-organ. J Physiol 61: 151-171.
  • Albus JS (1971) A theory of cerebellar function. Math Biosci 10: 25-61.
  • Amit DJ, Mongillo G (2003) Spike-driven synaptic dynam­ics generating working memory states. Neural Comput 15: 565-596.
  • Atiya AF, Parlos AG (2000) New results on recurrent net­work training: unifying the algorithms and accelerating convergence. IEEE Trans Neural Networks 11: 697­709.
  • Bair W, Koch C (1996) Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey. Neural Comput 8: 1185-1202.
  • Bao S, Chan VT, Merzenich MM (2001) Cortical remodel­ing induced by activity of ventral tegmental dopamine neurons. Nature 412: 79-83.
  • Baras D, Meir R (2007) Reinforcement learning, spike-ti- medependent plasticity, and the BCM rule. Neural Comput 19: 2245-2279.
  • Barea R, Boquete L, Mazo M, Lopez E, Bergasa L (2000) E.O.G. guidance of a wheelchair using spiking neural networks. In: Proceedings of the 8th European Symposium on Artificial Neural Networks, Bruges, BE. p. 233-238.
  • Barlow HB (1989) Unsupervised learning. Neural Comput 1: 295-311.
  • Baudry M (1998) Synaptic plasticity and learning and memory: 15 years of progress. Neurobiol Learn Mem 70: 113-118.
  • Bauer HU, Pawelzik K (1993) Alternating oscillatory and stochastic dynamics in a model for a neuronal assembly. Physica D 69: 380-393.
  • Beierholm U, Nielsen CD, Ryge J, Alstrom P, Kiehn O (2001) Characterization of reliability of spike timing in spinal interneurons during oscillating inputs. J Neurophysiol 86: 1858-1868.
  • Belatreche A, Maguire LP, McGinnity M, Wu QX (2003) A method for supervised training of spiking neural net­works. In: Proceedings of 2nd IEEE Systems, Man & Cybernetics United Kingdom & Republic of Ireland Chapter Conference „Cybernetic Intelligence, Challenges and Advances". Reading, UK. p. 39-44.
  • Belatreche A, Maguire LP, McGinnity M (2006) Advances in design and application of spiking neural networks. Soft Comput 11: 239-248.
  • Belter D, Ponulak F, Rotter S (2008) Adaptive movement control with spiking neural networks. Part II: composite control. In: Proceedings of Recent Advances in Neuro­Robotics, Symposium: Sensorimotor Control. Freiburg University, Freiburg, DE. p. 14.
  • Bennett MR (1999) The early history of the synapse: from Plato to Sherrington. Brain Res Bull 50: 95-118.
  • Berry MJ, Warland DK, Meister M (1997) The structure and precision of retinal spike trains. Proc Natl Acad Sci U S A 94: 5411-5416.
  • Bi GQ, Poo MM (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, syn­aptic strength, and post-synaptic cell type. J Neurosci 18: 10464-10472.
  • Bohte S, Kok J, La Poutré H (2000) Spike-prop: error- backprogation in multi-layer networks of spiking neu­rons. In: Proceedings of the 8th European Symposium on Artificial Neural Networks. Bruges, BE. p. 419­425.
  • Bohte S, Kok JN, La Poutr'e H (2002) Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48: 17-37.
  • Bohte S, La Poutr'e H, Kok JN (2002) Unsupervised clus­tering with spiking neurons by sparse temporal coding and multilayer RBF networks. IEEE Trans Neural Netw 13: 426-435.
  • Bohte S (2003) Spiking Neural Networks (Ph. D. thesis). University of Amsterdam, Faculty of Mathematics and Natural Sciences, Amsterdam, NL. (available on-line at
  • Bohte SM (2004) The evidence for neural information pro­cessing with precise spike-times: a survey. Nat Comput 4: 195-206.
  • Bohte S, Kok JN (2005) Applications of spiking neural net­works. Information Processing Letters 95: 519-520.
  • Booij O, Nguyen HT (2005) A gradient descent rule for spik­ing neurons emitting multiple spikes. Information Processing Letters 95: 552-558.
  • Borst A, Theunissen FE (1999) Information theory and neu­ral coding. Nat Neurosci 2: 947-957.
  • Brembs B, Lorenzetti FD, Reyes FD, Baxter DA, Byrne JH (2002) Operant learning in aplysia: neuronal correlates and mechanisms. Science 296: 1706-1709.
  • Brody CD, Hopfield JJ (2003) Simple networks for spiketim- ing-based computation, with application to olfactory processing. Neuron 37: 843-852.
  • Burgsteiner H (2005) Training networks of biological realis­tic spiking neurons for real-time robot control. In: Proceedings of 9th European Symposium on Artificial Neural Networks, Bruges, BE. p. 129-136.
  • Buzsaki G (2006) Rhythms of the Brain. Oxford University Press, New York, NY.
  • Carey MR, Medina JF, Lisberger SG (2005) Instructive sig­nals for motor learning from visual cortical area MT. Nat Neurosci 8: 813-819.
  • Carrillo RR, Ros E, Boucheny C, Coenen OJMD (2008) A real-time spiking cerebellum model for learning robot control. Biosystems 94: 18-27.
  • Cassidy A, Ekanayake V (2006) A biologically inspired tac­tile sensor array utilizing phase-based computation. Proceedings of IEEE International Workshop on Biomedical Circuits and Systems (BioCAS), London, UK.
  • Chapin JK, Moxon KA (2000) Neural Prostheses for Restoration of Sensory and Motor Function. CRC Press, Boca Raton, FL.
  • Chen HT, Ng KT, Bermak A, Law MK, Martinez D (2011) Spike latency coding in a biologically inspired microelec­tronic nose. IEEE Trans Biomed Circuits Syst 5: 160-168.
  • Choe Y, Miikkulainen R (2000) A self-organizing neural network for contour integration through synchronized fir­ing. In: Proceedings of 17th National Conference on Artificial Intelligence. MIT Press, Cambridge, MA. p. 123-128.
  • Cios KJ, Sala DM (2000) Advances in applications of spik­ing neuron networks. In: Applications and Science of Computational Intelligence III (Priddy KL, Keller PE, Fogel DB, Eds). Proceedings of SPIE vol. 4055: 324­336.
  • Citri A, Malenka RC (2008) Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33: 18-41.
  • Clopath C, Longtin A, Gerstner W (2008) An online Hebbian learning rule that performs independent component anal­ysis. In: Advances in Neural Information Processing Systems (Platt J, Koller D, Singer Y, Roweis S, Eds) 20: 321-328.
  • Conn K (2007) Supervised Reinforcement Learning Application to an Embodied Mobile Robot. VDM Verlag Saatfbrucken, DE.
  • De Ruyter van Steveninck RR, Lewen GD, Strong SP, Koberle R, Bialek W (1997) Reproducibility and vari­ability in neural spike trains. Science 275: 1805-1808.
  • De Schutter E, Steuber V (2009) Patterns and pauses in Purkinje cell simple spike trains: experiments, modeling and theory. Neuroscience 162: 816-826.
  • De Sousa G, Adams R, Davey N, Maex R, Steuber V (2009) The effect of different forms of synaptic plasticity on pat­tern recognition in the cerebellar cortex. In: Adaptive and Natural Computing Algorithms (Proceedings of 9th International Conference, ICANNGA2009) (Kolehmainen V, Toivanen P, Beliczynski B, Eds). Springer-Verlag Berlin, Heidelberg, DE. p. 413-422.
  • deCharms RC (1998) Information coding in the cortex by independent or coordinated populations. Proc Natl Acad Sci U S A 95: 15166-15168.
  • Diesmann M, Gewaltig MO, Aertsen A (1999) Stable propa­gation of synchronous spiking in cortical neural networks. Nature 402: 529-533.
  • Dominey PF, Ramus F (2000) Neural network processing of natural language: I. Sensitivity to serial, temporal and abstract structure of language in the infant. Lang Cog Proc 15: 87-127.
  • Doya K (1999) What are the computations of the cerebel­lum, the basal ganglia and the cerebral cortex? Neural Networks 12: 961-974.
  • Du Bois-Reymond EH (1848) Studies on Animal Electricity. Vol.1 (In German). G Reimer, Berlin, DE.
  • Eckhorn R, Bauer R, Jordan W, Brosch M, Kruse W, Munk M, Reitboeck, HJ (1988) Coherent oscillations: a mecha­nism for feature linking in the visual cortex? Biol Cybern 60: 121-130.
  • Escobar MJ, Masson GS, Vieville T, Kornprobst P (2009) Action recognition using a bio-inspired feedforward spik­ing network. Int J Comput Vision 82: 284-301.
  • Faisal AA, Selen LPJ, Wolpert DM (2008) Noise in the ner­vous system. Nat Rev Neurosci 9: 292-303.
  • Farries M, Fairhall A (2007) Reinforcement learning with modulated spike timing-dependent synaptic plasticity. J Neurophysiol 98: 3648-3665.
  • Fetz EE, Baker MA (1973) Operantly conditioned patterns of precentral unit activity and correlated responses in adjacent cells and contralateral muscles. J Neurophysiol 36: 179-204.
  • Finelli LA, Haney S, Bazhenov M, Stopfer M, Sejnowski TJ (2008) Synaptic learning rules and sparse coding in a model sensory system. PLoS Comput Biol 4: 1-18.
  • Florian R (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 2005) (Zaharie D, et al. Eds), Timisoara, Romania. IEEE Computer Society, Los Alamitos, CA. p. 299-306.
  • Florian R (2007) Reinforcement learning through modula­tion of spike-timing-dependent synaptic plasticity. Neural Comput 19: 1468-1502.
  • Gabbiani F, Midtgaard J (2001) Neural information process­ing. Encyclopedia of Life Sciences, Nature Publishing Group 0: 1-12 (available online at:
  • Gaze R, Keating M, Szekely G, Beazley L (1970) Binocular interaction in the formation of specific inter- tectal neuronal connexions. Proc Roy Soc Biol 175: 107-147.
  • Georgopoulos AP (1986) On reaching. Annu Rev Neurosci 9: 147-170.
  • Gerstner W, van Hemmen LJ (1992) Associative memory in a network of spiking neurons. Network: Computation in Neural Systems 3: 139-164.
  • Gerstner W, van Hemmen LJ (1993) Coherence and incoher­ence in a globally coupled ensemble of pulse-emitting units. Phys Rev Lett 7: 312-315.
  • Gerstner W, Kempter R, van Hemmen JL, Wagner H (1996) A neuronal learning rule for sub-millisecond temporal coding. Nature 386: 76-78.
  • Gerstner W, Kistler W (2002a) Mathematical formulations of Hebbian learning. Biol Cybern 87: 404-415.
  • Gerstner W, Kistler W (2002b) Spiking Neuron Models. Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge, MA.
  • Ghosh-Dastidar S, Adeli H (2009) A new supervised learn­ing algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Networks 22: 1419-1431.
  • Girard P, Jouffrais C, Kirchner CH (2008) Ultra-rapid cate­gorization in non-human primates. Anim Cogn 11: 485­493.
  • Glackin C, McDaid L, Maguire L, Sayers H (2008) Implementing fuzzy reasoning on a spiking neural net­work. In: Proceedings of 18th International Conference on Artificial Neural Networks, ICANN' 2008 (Kurkova V, Neruda R, Koutnik J, Eds). Springer-Verlag, Berlin­Heidelberg, DE. p. 258-267.
  • Gray CM, König P, Engel AK, Singer W (1989) Oscillatory responses in cat visual cortex exhibit intercolumnar syn­chronization which reflects global stimulus properties. Nature 338: 334-337.
  • Gray CM, Singer W (1989) Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc Natl Acad Sci U S A 86: 1698-1702.
  • Gurden H, Takita M, Jay TM (2000) Essential role of D1 but not D2 receptors in the NMDA receptor-dependent long- term potentiation at hippocampal-prefrontal cortex syn­apses in vivo. J Neurosci 20: RC106.
  • Gütig R, Sompolinsky H (2006) The tempotron: a neuron that learns spike timing-based decisions. Nat Neurosci 9: 420-428.
  • Gütig R, Sompolinsky H (2009) Time-warp invariant neu­ronal processing. PLoS Biology 7: e1000141.
  • Guyonneau R, VanRullen R, Thorpe SJ (2004) Temporal codes and sparse representations: A key to understanding rapid processing in the visual system. J Physiol Paris 98: 487-497.
  • Häusler S, Markram H, Maass W (2003) Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts. Complexity 8: 39-50.
  • Hebb DO (1949) The Organization of Behavior. Wiley, New York, NY.
  • Hertz J, Krogh, A, Palmer R (1991) Introduction to the Theory of Neural Computation. Addison-Wesley, Redwood City, CA.
  • Hines M, Morse T, Migliore M, Carnevale N, Shepherd G (2004) ModelDB: A database to support computational neuroscience. J Comput Neurosci 17: 7-11.
  • Hinton G, Sejnowski TJ (Eds) (1999) Unsupervised Learning. Foundations of Neural Computation. MIT Press, Cambridge, MA.
  • Hopfield JJ (1995) Pattern recognition computation using action potential timing for stimulus representation. Nature 376: 33-36.
  • Hopfield JJ, Hertz AVM (1995) Rapid local synchronization of action potentials: Toward computation with coupled integrate-and-fire neurons. Proc Natl Acad Sci U S A 92: 6655-6662.
  • Hopfield JJ, Brody CD (2000) What is a moment? "Cortical" sensory integration over a brief interval. Proc Natl Acad Sci U S A 97: 13919-13924.
  • Hopfield JJ, Brody CD (2001) What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. Proc Natl Acad Sci U S A 98: 1282-1287.
  • Hopfield JJ (2010) Neurodynamics of mental exploration. Proc Natl Acad Sci U S A 107: 1648-1653.
  • Hofstötter C, Mintz M, Verschure PFMJ (2002) The cerebel­lum in action: a simulation and robotics study. Eur J Neurosci 16: 1361-76.
  • Huys QJM, Zemel RS, Natarajan R, Dayan P (2007) Fast population coding. Neural Comput 19: 404-441.
  • Inagaki K, Hirata Y, Blazquez PM, Highstein SM (2007) Computer simulation of vestibulo-ocular reflex motor learning using a realistic cerebellar cortical neuronal net­work model. In: Neural Information Processing. Lecture Notes in Computer Science vol. 4984 (Ishikawa M, Doya K, Miyamoto H, Yamakawa T, Eds). Springer-Verlag, Berlin-Heidelberg, DE. p. 902-912.
  • Ito M (2000a) Mechanisms of motor learning in the cerebel­lum. Brain Res 886: 237-245.
  • Ito M (2000b) Neural control of cognition and language. In: Image, Language, Brain (Marantz A, Miyashita Y, O'Neil W, Eds). MIT Press, Cambridge, MA.
  • Ito M (2005) Bases and implications of learning in the cere­bellum-adaptive control and internal model mechanism. Prog Brain Res 148: 95-109.
  • Ito M (2008) Control of mental activities by internal models in the cerebellum. Nat Rev Neurosci 9: 304-313.
  • Izhikevich EM (2002) Resonance and selective communica­tion via bursts in neurons having subthreshold oscilla­tions BioSystems 67: 96-102.
  • Izhikevich EM, Desai NS, Walcott EC, Hoppensteadt FC (2003) Bursts as a unit of neural information: selective com­munication via resonance. Trends Neurosci 26: 161-167.
  • Izhikevich EM (2007) Solving the distal reward problem through linkage of STDP and dopamine signaling. Cereb Cortex 17: 2443-2452.
  • Izhikevich EM, Edelman GM (2008) Large-scale model of mammalian thalamocortical systems. Proc Natl Acad Sci U S A 105: 3593-3598.
  • Jaeger H (2001) Short Term Memory in Echo State Networks. GMD Report 152. German National Research Center for Information Technology, St. Augustine, DE.
  • Jaksa R, Sincak P, Majernik P (1999) Backpropagation in supervised and reinforcement learning for mobile robot control. J Electr Eng 50: 185-189.
  • Jefferys JG, Traub RD, Whittington MA (1996) Neuronal networks for induced '40 Hz' rhythms. Trends Neurosci 19: 202-208.
  • Jensen O, Lisman JE (1996) Hippocampal CA3 region pre­dicts memory sequences: accounting for the phase pre­cession of place cells. Learn Mem 3: 279-287.
  • Jin DZ, Seung HS (2002) Fast computation with spikes in a recurrent neural network. Physical Review E 65: 051922.
  • Johansson SJ, Birznieks I (2004) First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nat Neurosci 7: 170-177.
  • Joshi P, Maass W (2005) Movement generation with circuits of spiking neurons. Neural Comp 17: 1715-1738.
  • Jorntell H, Hansel Ch (2006) Synaptic memories upside down: bidirectional plasticity at cerebellar parallel fiber- Purkinje cell synapses. Neuron 52: 227-238.
  • Kandel ER, Schwartz TMJ, Jessel TM (1991) Principles of Neural Sciences. Elsevier, New York, NY.
  • Kasinski A, Ponulak F (2006) Comparison of supervised learning methods for spike time coding in spiking neural networks. International Journal of Applied Mathematics and Computer Science 16: 101-113.
  • Kawato M, Gomi H (1992a) A computational model of four regions of the cerebellum based on feedback-error-learn­ing. Biol Cybern 68: 95-103.
  • Kawato M, Gomi H (1992b) The cerebellum and VOR/OKR learning models. Trends Neurosci 15: 445-453.
  • Kim EK, Gerling GJ, Wellnitz SA, Lumpkin EA (2010) Using force sensors and neural models to encode tactile stimuli as spike-based response. In: Proceedings of the 2010 IEEE Haptic Interfaces for Virtual Environment and Teleoperator Systems, Boston, MA. p. 195-198.
  • Kiss T, Orban G, Erdi P (2006) Modeling hippocampal theta oscillation: Applications in neuropharmacology and robot navigation. International Journal of Intelligent Systems 21: 903-917.
  • Klampfl S, Legenstein R, Maass W (2009) Spiking neurons can learn to solve information bottleneck problems and to extract independent components. Neural Comput 21: 911-959.
  • Knoblauch A (2003) Synchronization and pattern separation in spiking associative memories and visual cortical areas (PhD thesis). Department of Neural Information Processing, University of Ulm, Ulm, DE. [http://vts.uni-]
  • Knudsen EI (1991) Visual instruction of the neural map of auditory space in the developing optic tectum. Science 5015: 85-87.
  • Knudsen EI (1994) Supervised Learning in the Brain. J Neurosci 14: 3985-3997.
  • Kornprobst P, Vieville T, Dimov IK (2005) Could early visual processes be sufficient to label motions? In: Proceedings of IEEE International Joint Conference on Neural Networks. p. 1687-1692.
  • Kroese B, van der Smagt P (1996) An Introduction to Neural Networks (8th ed.). University of Amsterdam , Amsterdam, NL.
  • Kumar A, Rotter S, Aertsen A (2010) Spiking activity propa­gation in neuronal networks: reconciling different perspec­tives on neural coding. Nat Rev Neurosci 11: 615-627.
  • Landis F, Ott T, Stoop R (2010) Hebbian self-organizing integrate-and-fire networks for data clustering. Neural Comput 22: 273-288.
  • Lapicque L (1907) Quantitative investigations of electrical nerve excitation treated as polarization (in French). J Physiol Pathol Gen 9: 620-635.
  • Laurent G (1996) Dynamical representation of odors by oscillating and evolving neural assemblies. Trends Neurosci 19: 489-496.
  • Lee K, Kwon DS (2008) Synaptic plasticity model of a spik­ing neural network for reinforcement learning. Neurocomputing 71: 3037-3043.
  • Legenstein R, Naeger C, Maass W (2005) What can a neu­ron learn with spike-timing-dependent plasticity? Neural Comput 17: 2337-2382.
  • 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: 1-27.
  • Lestienne R (2001) Spike timing, synchronization and infor­mation processing on the sensory side of the central ner­vous system. Prog Neurobiol 65: 545-591.
  • Linster Ch, Cleland TA (2010) Decorrelation of odor repre­sentations via spike timing-dependent plasticity. Frontiers Comp Neurosci 4: 157.
  • Liu RC, Tzonev S, Rebrik S, Miller KD (2001) Variability and information in a neural code of the cat lateral genicu­late nucleus. J Neurophysiol 86: 2789-2806.
  • Ljungberg T, Apicella P, Schultz W (1992) Responses of monkey dopamine neurons during learning of behavioral reactions. J Neurophysiol 67: 145-163.
  • Lovinger DM (2010) Neurotransmitter roles in synaptic modulation, plasticity and learning in the dorsal striatum. Neuropharmacol 58: 951-961.
  • L0mo T (1966) Frequency potentiation of excitatory synap­tic activity in the dentate area of the hippocampal forma­tion. Acta Physiol Scand 68: S128.
  • Lukosevicius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Computer Science Review 3: 127-149.
  • Lumer ED (2000) Effects of spike timing on winner-take-all competition in model cortical circuits. Neural Comp 12: 181-194.
  • Ma J, Wu J (2007) Multistability in spiking neuron models of delayed recurrent inhibitory loops. Neural Comp 19: 2124-2148.
  • Maass W (1996) Lower bounds for the computational power of networks of spiking neurons. Neural Comp 8: 1-40.
  • Maass W (1997) Networks of spiking neurons: The third generation of neural network models. Neural Networks 10: 1659-1671.
  • Maass W, Natschlaeger T (1998) Associative memory with networks of spiking neurons in temporal coding. In: Neuromorphic Systems: Engineering Silicon from Neurobiology (Smith LS, Hamilton A, Eds). World Scientific Publishers Co., Singapore, New Jersey, London, Hong-Kong. p. 21-32.
  • Maass W (2002) Paradigms for computing with spiking neurons. In: Models of Neural Networks. Vol 4: Early Vision and Attention (van Hemmen JL, Cowan JD, Domany E, Eds). New York, NY. p. 373-402.
  • Maass W, Legenstein RA, Markram H (2002b) A new approach towards vision suggested by biologically realis­tic neural microcircuit models. In: Proceedings of 2nd International Workshop "Biologically Motivated Computer Vision". Springer-Verlag, Berlin-Heidelberg, DE.
  • Maass W, Natschlaeger T, Markram H (2002b) Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Comp 14: 2531-2560.
  • Maass W, Natschlaeger T, Markram H (2003) A model for real-time computation in generic neural microcircuits. In: Advances in Neural Information Processing Systems vol. 15. Proceedings of the 2002 Conference (Becker S, Thrun S, Obermayer K, Eds). MIT Press, Cambridge, MA, p. 229-236.
  • Maass W, Natschlaeger T, Markram H (2004) Computational models for generic cortical microcircuits. In: Computational Neuroscience: a Comprehensive Approach. Chapman & Hall/CRC, Boca Raton, FL. p. 575-605.
  • Magleby KL, Zengel JE (1976) Long term changes in aug­mentation, potentiation, and depression of transmitter release as a function of repeated synaptic activity at the frog neuromuscular junction. J Physiol 257: 471-494.
  • Markram H, Luebke J, Frotscher M, Sakmann B (1997) Regulation of synaptic efficacy by coincidence of post- synaptic APs and EPSPs. Science 275: 213-215.
  • Marr D (1969) Theory of cerebellar cortex. J Physiol 202: 437-455.
  • Martinez D, Hugues E (2004) A spiking neural network model of the locust antennal lobe: towards neuromorphic electronic noses inspired from insect olfaction. In: Electronic Noses/Sensors for Detection of Explosives (Gardner J, Yinon J, Eds). Kluwer Academic Publishers, Dordrecht, Boston, London. p. 209-234.
  • Medina JF, Mauk MD (1999) Simulations of cerebellar motor learning: computational analysis of plasticity at the mossy fiber to deep nucleus synapse. J Neurosci 19: 7140-7151.
  • Melamed O, Barak O, Silberberg G, Markram H, Tsodyks M (2008) Slow oscillations in neural networks with facilitat­ing synapses. J Comput Neurosci 25: 308-316.
  • Miall CR, Wolpert DM (1996) Forward models for physio­logical motor control. Neural Networks 9: 1265-1279.
  • Mongillo G, Barak O, Tsodyks M (2008) Synaptic theory of working memory. Science 19: 1543-1546.
  • Montgomery J, Carton G, Bodznick D (2002) Error-driven motor learning in fish. Biol Bull 203: 238-239.
  • Moore SC (2002) Back-Propagation in Spiking Neural Networks (MSc thesis). University of Bath, Bath, UK. (Available online at:
  • Mulder AJ, Boom HBK, Hermens HJ and Zilvold G (1990) Artificial-reflex stimulation for FES-induced standing with minimum quadriceps force. Med Biol Eng Comp 28: 483-488.
  • Muresan R (2002) Complex object recognition using a bio­logically plausible neural model. In: Proceedings of 2nd WSEAS International Conference on Robotics, Distance Learning and Intelligent Communication Systems, Skiathos, Greece.
  • Neuenschwander S, Singer W (1996) Long-range synchroni­zation of oscillatory light responses in the cat retina and lateral geniculate nucleus. Nature 379: 728-733.
  • Natschlaeger T, Ruf B (1998a) Spatial and temporal pattern analysis via spiking neurons. Network Comput Neural Syst 9: 319-332.
  • Natschlaeger T, Ruf B (1998b) Online clustering with spik­ing neurons using temporal coding. In: Neuromorphic Systems: Engineering Silicon from Neurobiology (Smith LS, Hamilton A., Eds). World Scientific Publishers Co., Singapore, New Jersey, London, Hong-Kong. p. 33-42.
  • Natschlaeger T (1999) Efficient Computation in Networks of Spiking Neurons - Simulations And Theory (PhD Dissertation). Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria.
  • Newman D, Hettich S, Blake C, Merz C (1998) UCI reposi­tory of machine learning databases. (Available online at:
  • Nikolic D, Haeusler S, Singer W, Maass W (2009) Distributed fading memory for stimulus properties in the primary visual cortex. PLoS Biology 7: 1-19.
  • O'Keefe J (1993) Hippocampus, theta, and spatial memory. Curr Opin Neurobiol 3: 917-924.
  • Otani S, Blond O, Desce JM, Crepel F (1998) Dopamine facilitates long-term depression of glutamatergic trans­mission in rat prefrontal cortex. Neurosci 85: 669-676.
  • Otmakhova NA, Lisman JE (1996) D1/D5 dopamine recep­tor activation increases the magnitude of early long-term potentiation at CA1 hippocampal synapses. J Neurosci 16: 7478-7486.
  • Paolo EAD (2003a) Evolving spike-timing-dependent plas­ticity for single-trial learning in robots. Phil Trans R Soc LondA 361:2299-2319.
  • Paolo EAD (2003b) Spike-timing dependent plasticity for evolved robots. Adapt Behav 10: 243-263.
  • Paugam-Moisy H (2006) Spiking Neuron Networks - a Survey. Raport Technique RR11, vol. 592, IDIAP Martigny, Switzerland.
  • Perrinet L, Samuelides M (2002) Sparse image coding using an asynchronous spiking neural network. In: Proceedings of European Symposium on Artificial Neural Networks ESANN'2002, p. 313-318.
  • Perrinet L, Samuelides M, Thorpe S (2004) Sparse spike coding in an asynchronous feed-forward multi-layer neu­ral network using matching pursuit. Neurocomputing 57: 125-134.
  • Pfister JP, Toyoizumi T, Barber D, Gerstner W (2006) Optimal spike-timing dependent plasticity for precise action potential firing. Neural Comput 18: 1318-1348.
  • Ponulak F (2005) ReSuMe - new supervised learning meth­od for spiking neural networks. Institute of Control and Information Engineering, Poznan University of Technology. (Available online at: http://d1.cie.put.poznan. pl/~fp/research.html)
  • Ponulak F, Kasinski A (2006a) Generalization properties of SNN trained with ReSuMe. In: Proceedings of European Symposium on Artificial Neural Networks, ESANN'2006. p. 623-629.
  • Ponulak F, Kasinski A (2006b) ReSuMe learning method for spiking neural networks dedicated to neuroprostheses control. In: Proceedings of EPFL LATSIS Symposium 2006, Dynamical Principles for Neuroscience and Intelligent Biomimetic Devices. p. 119-120.
  • Ponulak F, Belter D, Kasinski A (2006) Adaptive central pat­tern generator based on spiking neural networks. In: Proceedings of EPFL LATSIS Symposium 2006, Dynamical Principles for Neuroscience and Intelligent Biomimetic Devices. p. 121-122.
  • Ponulak F, Rotter S (2008) Biologically inspired spiking neural model for motor control and motor learning (abstract). In: Proceedings of NIN Conference on Perceptual Learning, Motor Learning and Automaticity. Amsterdam. NL, p. 56.
  • Ponulak F, Belter D, Rotter S (2008) Adaptive movement control with spiking neural networks, Part I: feedforward control. In: Proceedings of Recent Advances in Neuro­Robotics, Symposium: Sensorimotor Control. Freiburg, Germany. p. 47.
  • Ponulak F, Rotter S (2009) Encoding sequences of spikes in spiking neural networks through reinforcement learning. In: Proceedings of Multidisciplinary Symposium on Reinforcement Learning. Montreal, Canada.
  • Ponulak F, Kasinski A (2010) Supervised learning in spiking neural networks with ReSuMe: sequence learning, clas­sification and spike-shifting. Neural Comp 22: 467-510.
  • Popovic D, Sinkjaer T (2000) Control of Movement for the Physically Disabled. Springer-Verlag, London, UK.
  • Raman B, Gutierrez-Osuna R (2004) Chemosensory pro­cessing in a spiking model of the olfactory bulb: chemo- topic convergence and center surround inhibition. In: Proceedings of Advances in Neural Information Processing Systems 17: 1105-1112.
  • Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W (1997) Spikes: Exploring the Neural Code. MIT Press, Boston, MA.
  • Rochel O, Martinez D, Hugues E, Sarry F (2002) Stereoolfaction with a sniffing neuromorphic robot using spiking neurons. In: Proceedings of 16th European Conference on Solid-StateTransducers - EUROSENSORS, Prague, CZ.
  • Rojas R (1996) Neural Networks - A Systematic Introduction. Springer-Verlag, Berlin, DE.
  • Rom R, Erel J, Glikson M, Lieberman RA, Rosenblum K, Binah O, Ginosar R, Hayes DL (2007) Adaptive cardiac resynchronization therapy device based on spiking neu­rons architecture and reinforcement learning scheme. IEEE Trans Neural Networks 18: 542-550.
  • Rosenblatt F (1958) The perceptron: A probabilistic model for information storage and organization in the brain. Psychol Rev 65: 386-408.
  • Rosenstein M and Barto AG (2004) Supervised actor-critic reinforcement learning. In: Learning and Approximate Dynamic Programming: Scaling up the Real World (Si J, Barto A, Powell WB, Wunsch D, Eds). John Wiley & Sons, Inc., New York, NY. p. 359-380.
  • Ruf B (1998) Computing and Learning with Spiking Neurons - Theory and Simulations (Ph.D. thesis). Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria.
  • Ruf B, Schmitt M (1998) Self-organization of spiking neu­rons using action potential timing. IEEE Trans Neural Networks 9: 575-578.
  • Rumelhart D, Hinton G, Williams R (1986) Learning represen­tations by back-propagating errors. Nature 323: 533-536.
  • Saal HP, Vijayakumar S, Johansson RS (2009) Information about complex fingertip parameters in individual human tactile afferent neurons. J Neurosci 29: 8022-8031.
  • Sanchez-Montanes MA, Konig P, Verschure PFMJ (2002) Learning sensory maps with real-world stimuli in real time using a biophysically realistic learning rule. IEEE Trans Neural Networks 13: 619-632.
  • Savin C, Joshi P, Triesch J (2010) Independent component analysis in spiking neurons. PLoS Comput Biol 6: 533­536, e1000757.
  • Sayenko DS, Vette AH, Kamibayashi K, Nakajima T, Akai M, Nakazawa K (2007) Facilitation of the soleus stretch reflex induced by electrical excitation of plantar cutane­ous afferents located around the heel. Neurosci Lett 415: 294-298.
  • Schrauwen B, Campenhout JV (2004) Improving spikeprop: enhancements to an error-backpropagation rule for spik­ing neural networks. In: Proceedings of 15th ProRISC Workshop, Veldhoven, the Netherlands.
  • Schrauwen B, Campenhout JV (2006) Backpropagation for population-temporal coded spiking neural networks. In: Proceedings of IEEE International Conference on Neural Networks. p. 1797-1804.
  • Schrauwen B, Verstraeten D, Campenhout JV (2007) An overview of reservoir computing: theory, applications and implementations. In: Proceedings of 15th European Symposium on Artificial Neural Networks, p. 471-482.
  • Schuetze SM (1983) The discovery of the action potential. Trends Neurosci 6: 164-168.
  • Schultz W (2002) Getting formal with dopamine and reward. Neuron 36: 241-263.
  • Sejnowski TJ (1977) Statistical constraints on synaptic plas­ticity. J Theor Biol 69: 385-389.
  • Sejnowski TJ, Tesauro G (1989) The hebb rule for synaptic plasticity: algorithms and implementations. In: Neural Models of Plasticity (Byrne JH, Berry WO, Eds). Academic Press, San Diego, CA. p. 94-103.
  • Shadlen MN, Newsome WT (1998) The variable discharge of cortical neurons: implications for connectivity, compu­tation and information coding. J Neurosci 18: 3870­3896.
  • Sherrington CS (1897) The central nervous system. In: A Textbook of Physiology (7th ed.) part III (Macmillian FM, Ed). Macmillan, London, UK, p. 929.
  • Shidara M, Kawano K, Gomi H, Kawato M (1993) Inversedynamics model of eye movement control by purkinje cells in the cerebellum. Nature 365: 50-52.
  • Shin JH, Smith D, Swiercz W, Staley K, Rickard JT, Montero J, Kurgan LA, Cios KJ (2010) Recognition of partially occluded and rotated images with a network of spiking neurons. IEEE Trans Neural Networks 21: 1697-1709.
  • Singer W (1999) Neuronal synchrony: a versatile code for the definition of relations? Neuron 24: 49-65.
  • Skinner BF (1953) Science and Human Behavior. Macmillan, Oxford, UK.
  • Soltani A, Wang XJ (2010) Synaptic computation underly­ing probabilistic inference. Nat Neurosci 13: 112-119.
  • Sommer FT, Wennekers T (2001) Associative memory in networks of spiking neurons. Neural Networks 14: 825­834.
  • Sougne JP (2001) A learning algorithm for synfire chains. In: Connectionist Models of Learning, Development and Evolution (French RM, Sougne JP, Eds). Springer-Verlag, London, UK. p. 23-32.
  • Steil JJ (2004) Backpropagation-Decorrelation: online recur­rent learning with O(N) complexity. In: Proceedings of IEEE International Joint Conference on Neural Networks.
  • Stein RB (1967) Some models of neuronal variability. Biophys J 7: 37-68.
  • Stein RB, Gossen ER, Jones KE (2005) Neuronal variabili­ty: noise or part of the signal? Nat Rev Neurosci 6: 389­397.
  • Stent GS (1973) A physiological mechanism for hebb's pos­tulate of learning. Proc Natl Acad Sci U S A 70: 997­1001.
  • Sutton RS, Barto AG (2002) Reinforcement Learning: An Introduction. Bradford Books, MIT Press, Cambridge, MA.
  • Szatmary B, Izhikevich EM (2010) Spike-timing theory of working memory. PLoS Comput Biol 6: e1000879.
  • Thach WT (1996) On the specific role of the cerebellum in motor learning and cognition: Clues from PET activation and lesion studies in man. Behav Brain Sci 19: 411-431.
  • Tham CK (1995) Reinforcement learning of multiple tasks using a hierarchical CMAC architecture. Robotics and Autonomous Systems 15: 247-274.
  • Thorndike EL (1901) Animal intelligence: An experimental study of the associative processes in animals. Psychol Rev Monogr Suppl 2: 1-109.
  • Thorpe SJ (1990) Spike arrival times: A highly efficient cod­ing scheme for neural networks. In: Parallel Processing in Neural Systems and Computers (Eckmiller R, Hartmann G, Hauske G, Eds). Elsevier, Amsterdam, NL. p. 91-94.
  • Thorpe SJ, Gautrais J (1997) Rapid visual processing using spike asynchrony. In: Neural Information Processing Systems (Mozer MC, Jordan MI, Petsche T, Eds). MIT Press, Cambridge, MA. p. 901-907.
  • Thorpe SJ, Delorme A, VanRullen R (2001) Spike-based strategies for rapid processing. Neural Networks 14: 715-726.
  • Tiesinga P, Fellous JM, Sejnowski TJ (2008) Regulation of spike timing in visual cortical circuits. Nat Rev Neurosci 9: 97-107.
  • Tino P, Mills AJ (2005) Learning beyond finite memory in recurrent networks of spiking neurons. In: Advances in Natural Computation, ICNC'2005, Lecture Notes in Computer Science (Wang L, Chen K, Ong Y, Eds). Springer-Verlag, Berlin-Heidelberg, DE. p. 666-675.
  • Tkacik G, Prentice JS, Balasubramanian V, Schneidman E (2010) Optimal population coding by noisy spiking neurons. Proc Natl Acad Sci U S A 107: 14419­14424.
  • Udin SB (1985) The role of visual experience in the forma­tion of binocular projections in frogs. Cell Mol Neurobiol 5: 85-102.
  • Udin SB, Keating M (1981) Plasticity in a central nervous pathway in Xenopus: anatomical changes in the isthmo- tectal projection after larval eye rotation. J Comp Neurol 203: 575-594.
  • VanRullen R, Thorpe SJ (2001a) Rate coding versus tempo­ral order coding: what the retinal ganglion cells tell the visual cortex. Neural Comp 13: 1255-1283.
  • VanRullen R, Thorpe SJ (2001b) Is it a bird? Is it a plane? Ultra-rapid visual categorization of natural and artifactual objects. Perception 30: 655-668.
  • VanRullen R, Guyonneau R, Thorpe SJ (2005) Spike times make sense. Trends Neurosci 28: 1-4.
  • Vasilaki E, Fremaux N, Urbanczik R, Senn W, Gerstner W (2009) Spike-based reinforcement learning in continuous state and action space: when policy gradient methods fail. PLoS Comput Biol 5: e1000586.
  • Veredas FJ, Mesa H, Martinez LA (2008) Imprecise corre­lated activity in self-organizing maps of spiking neurons. Neural Networks 6: 810-816.
  • Verstraeten D, Schrauwen B, Stroobandt D, Van Campenhout J (2005) Isolated word recognition with the liquid state machine: a case study. Inf Proc Lett 95: 521-528.
  • von der Malsburg, C (1985) Nervous structures with dynamical links. Ber Bunsenges Phys Chem 89: 703­710.
  • Wang XJ (2008) Decision making in recurrent neuronal cir­cuits. Neuron 60: 215-234.
  • Werbos P (1974) Beyond Regression: New Tools for Prediction and Analysis (PhD thesis). Harvard University, Cambridge, MA.
  • Widrow B, Hoff ME (1960) Adaptive switching circuits. IRE WESCON Convention Record 4: 96-104.
  • Widrow B (1962) Generalization and information storage in networks of adaline 'neurons'. In: Self-Organizing Systems (Yovitz M, Jacobi G, Goldstein G, Eds). Spartan Books, Washington, D.C. p. 435-461.
  • Witten IH (1977) An adaptive optimal controller for dis­crete-time Markov environments. Information and Control 34: 286-295.
  • Wolpert DM, Miall CR, Kawato M (1998) Internal models in the cerebellum. Trends Cogn Sci 2: 338-347.
  • Xin J, Embrechts MJ (2001) Supervised learning with spik­ing neuron networks. In: Proceedings oflEEE International Joint Conference on Neural Networks, Washington D.C. p. 1772-1777.
  • Yamazaki T, Tanaka S (2007) A spiking network model for passage-of-time representation in the cerebellum. Eur J Neurosci 26: 2279-2292.
  • Zamani M, Sadeghian A, Chartier S (2010) A bidirectional associative memory based on cortical spiking neurons using temporal coding. In: Proceedings of IEEE International Joint Conference on Neural Networks, Washington D.C. p. 1-8.
Rekord w opracowaniu
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.