Hélène Paugam-Moisy                              Computer Science Professor, Université de Lyon LIRIS - Lyon 2 - CNRS  --  Bron, France Associate researcher at INRIA Saclay - Île-de-France TAO research team, at LRI - Paris-Sud  --  Orsay, France Research  --  CV  --  Funding  --  Management  --  Teaching  --  Publications  --  Contact B

Spiking Neuron Networks are the latest generation of neural networks. They are strongly inspired by neuronal processing in the brain. Taking into account the precise spike-timing of neurons, these models have the capacity to process spatio-temporal data. Reservoir Computing is a new family of recurrent neural networks that let freely the dynamics of an internal network (the "reservoir") compute an expansion function of temporal input data, with fading memory. Internal states can be compared to echos or ripples on water. Although learning only the readout weights (red arrows on the figure) can be sufficient, I investigate the effect of synaptic plasticity in a reservoir of spiking neurons, regarding it as a complex system. Deep Neural Networks are multilayer feedforward neural networks, revisited with new layerwise learning rules. I study how to optimize their architecture and learning performance.

• ANR-Blanc, MAPS: "Mappings, Adaptation, Plasticity and Spatial computation" (2007-2011), scientific head of the LIRIS-Lyon 2 partner.
  
• ANR-DEFIS, ASAP: "Apprentissage Statistique par une Architecture Profonde / Statistical Learning in Deep Networks" (2009-2012), scientific head of the TAO-INRIA partner.

• ACI-NIC, "Distributed models of spiking neuron networks for multimodal integration” + DYNN (2003-2006)
• ACI-Cognitique, "Action and causality" (2001-2003) + "Spatial cognition" (1999-2001)
• Contract with ELF-TOTAL (1997 and 2000)
• European Project PARALIN, INCO-DC (1995-1998)
• Contract with France Télécom (1994-1997)

• Independent expert for the European Commission, FP6 (2003 and 2005) and FP7 (2007).
• Member of the "Comité National du CNRS", section 07 (Computer Science, Signal Processing, Automatics) and CID 45 (Cognition), 2004-2008
• Co-head of the PRASC: animation of research and project funding in Cognitive Science, in Rhône-Alpes, 2000-2007
• Head of the Cognitive Science Master of Lyon (2000-2004), co-head of the Computer Science Master of Lyon (1993-1998)
  
• Organization of Special Sessions at ESANN, European Symposium in Artificial Neural Networks: "Neural Netwoks and Cognitive Science" (2002), "Neural Netwoks for Computational Neuroscience" (2008), "Deep Learning" (2011)
• Organizing committee of ECAI'2002, European Conference in Artificial Intelligence
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• Member of international conferences program committees (e.g. NIPS, IJCNN, ICANN, ESANN, IJCAI, ICML, ACML)

• Ludovic ARNOLD, with Philippe Tarroux, Université Paris-Sud 11, since 2009
• Régis MARTINEZ, Université Lyon 2, since 2006

• Sylvain CHEVALLIER, PhD in 2009, post-doc at Télécom-ParisTech, Paris (F)
• Anthony MOURAUD, PhD in 2009, post-doc at CEA-LIST, Saclay (F)
• David MEUNIER, PhD in 2007, post-doc at "Brain Mapping Unit", Cambridge (UK)
• Johnny MARIETHOZ, PhD in 2006, engineer at IDIAP, Martigny (CH)
• Hyungsung JI, PhD in 2004, assistant professor at University of Seoul (KR)
• Emanuelle REYNAUD, PhD in 2002, assistant professor at Université Lyon 2 (F)
• Thomas VOEGTLIN, PhD in 2002, INRIA researcher at LORIA, Nancy (F)
• Olivier TEYTAUD, PhD in 2001, INRIA researcher at TAO, Orsay (F)
• André ELISSEEFF, PhD in 2000, researcher at IBM-Research, Zurich (CH)
  

• Master module on Neural Networks and Machine Learning
• Master module on Neural Networks and Associative Memories
• Master module on Artificial Intelligence
• Master module on Statistical Learning Theory
• Course on Modelling, Cognition and Complex Systems
• Course on Algorithmic Analysis and Computational Geometry
• Course on Networks and Distributed Systems
• Course on Parallel Programming Languages (PVM, MPI)
• Course on Programming Languages (Pascal, C, C++, Java)

• Book chapters:

H. Paugam-Moisy and S.M. Bohte. Computing with Spiking Neuron Networks, (40 pages). In G. Rozenberg, T. Bäck and J.N. Kok, Eds, Handbook of Natural Computing. Springer Verlag: Heidelberg, 2011.

Y. Guermeur et H. Paugam-Moisy. Théorie de l’apprentissage de Vapnik et SVM, Support Vector Machines. Dans M. Sebban et G. Venturini, éditeurs, Apprentissage automatique, pages 109–138. Hermès: Paris, 1999.

H. Paugam-Moisy. Multiprocessor simulation of neural networks. In M.A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 605–608. The MIT Press, 1995.

H. Paugam-Moisy. Parallel neural computing based on network duplicating. In I. Pitas, editor, Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks, pages 305–340. John Wiley, 1993.

H. Paugam-Moisy and A. Pétrowski. Parallel neural computing based on algebraic partitioning. In I. Pitas, editor, Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks, pages 259–304. John Wiley, 1993.

• Journal articles:

B. Cessac, H. Paugam-Moisy and T. Viéville. Overview of facts and issues about neural coding by spikes. Journal of Physiology: Paris. 104:5-18. 2010

H. Paugam-Moisy, R. Martinez and S. Bengio. Delay learning and polychronization for reservoir computing. NeuroComputing, 71: 1143-1158, Elsevier, 2008.

Y. Guermeur, G. Pollastri, A. Elisseeff, D. Zelus, H. Paugam-Moisy and P. Baldi. Combining Protein Secondary Structure Prediction Models with Ensemble Methods of Optimal Complexity. NeuroComputing, 56: 305–327, Elsevier, 2004.

J.-P. Royet, O. Koenig, H. Paugam-Moisy, D. Puzenat and J.-L. Chasse. Levels of processing effects on a task of olfactory naming. Perceptual & Motor Skills, 98: 197-213, 2004.

P. Estévez, H. Paugam-Moisy, D. Puzenat and M. Ugarte. A scalable parallel algorithm for training a hierarchical mixture of experts. Parallel Computing, 28: 861-891. Elsevier, 2002.

E. Reynaud, A. Crépet, H. Paugam-Moisy and D. Puzenat. A computational model for binding sensory modalities. Abstract in Consciousness and Cognition, 9(2): 87–88. Academic Press, 2000.

A. Elisseeff and H. Paugam-Moisy. JNN, a Randomized Algorithm for Training Multilayer Networks in Polynomial Time. NeuroComputing, 29: 3–24, Elsevier, 1999.

C. Kenyon and H. Paugam-Moisy. Multilayer neural networks and polyhedral dichotomies. Annals of Mathematics and Artificial Intelligence, 24(1-4): 115–128, Baltzer, 1998.

J.-P. Royet, H. Paugam-Moisy, C. Rouby, A.D. Zighed, N. Nicoloyannis, S. Amghar, and G. Sicard. Is short-term odour recognition predictable from odour profile ? Chemical Senses, 21 :553–566, Oxford University Press, 1996.

M. Cosnard, P. Koiran, and H. Paugam-Moisy. Bounds on the number of units for computing arbitrary dichotomies by multilayer perceptrons. Journal of Complexity, 10: 57–63, Academic Press, 1994.

• International conference papers:

S. Chevallier, N. Bredeche, H. Paugam-Moisy and M. Sebag. Emergence of temporalB and spatial synchronous behaviors in a foraging swarm . In Proc. of ECAL'2011, (to appear). Paris, France, August 2011.

L. Arnold, S. Rebecchi, S. Chevallier and H. Paugam-Moisy. An introduction to Deep Learning. Tutorial of ESANN'2011 Special Session, Artificial Neural Networks, Computational Intelligence and Machine Learning, 477-488. Bruges, Belgium, April 2011.

S. Chevallier, H. Paugam-Moisy and M. Sebag. SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system . In Proc. of NIPS'2010, Advances in Neural Information Processing Systems, 379-387. Vancouver, Canada, December 2010.

A. Mouraud, A. Guillaume and H. Paugam-Moisy. The DAMNED simulator for implementing a dynamic model of the network controlling saccadic eye movements. In Proc. of ICANN'2010, Lecture Notes in Computer Science 6352, Springer, 271-281. Thessaloniki, Greece, September 2010.

L. Arnold, H. Paugam-Moisy and M. Sebag. Unsupervised layer-wise model selection in deep neural networks. In Proc. of ECAI'2010, Frontiers in Artificial Intelligence and Applications 215, IOS Press, 915-920. Lisbon, Portugal, August 2010.

R. Martinez and H. Paugam-Moisy. Algorithms for structural and dynamical polychronous groups detection. In Proc. of ICANN'2009, Lecture Notes in Computer Science 5769, Springer, 75-84. Limassol, Cyprus, September 2009.

D. Meunier and H. Paugam-Moisy. Neural networks for computational neuroscience [tutorial]. In Proc. ofB ESANN’2008, Advances in Computational Intelligence and Learning, M. Verleysen Ed., 367–378. Bruges, Belgium, April 2008.

A. Mouraud, H. Paugam-Moisy and D. Puzenat. DAMNED: A distributed and multithreaded neural event driven simulation framework. In Proc. of Int. Conf. on Parallel Distributed Computing and Networks, PDCN’2006, ACTA Press, 212–217. Innsbruck, Austria, February 2006.

D. Meunier and H. Paugam-Moisy. Cluster detection algorithm in neural networks. In Proc. of Europ. Symp. on Artificial Neural Networks, ESANN’2006, M. Verleysen Ed., 19–24. Bruges, Belgium, April 2006.

S. Chevallier, P. Tarroux and H. Paugam-Moisy. Saliency extraction with a distributed spiking neuron network. In Proc. of Europ. Symp. on Artificial Neural Networks, ESANN’2006, M. Verleysen Ed., 209–214. Bruges, Belgium, April 2006.

A. Mouraud and H. Paugam-Moisy. Learning and discrimination through STDP in a top-down modulated associative memory. In Proc. of Europ. Symp. on Artificial Neural Networks, ESANN’2006, M. Verleysen Ed., 611–616. Bruges, Belgium, April 2006.

D. Meunier and H. Paugam-Moisy. Evolutionary supervision of a dynamical neural network allows learning with on-going weights. In Proc. of Int. Joint Conf. on Neural Networks, IJCNN’2005, IEEE-INNS, 1493–1498. Montréal, Canada, July 2005.

E. Reynaud and H. Paugam-Moisy. A multiple BAM for hetero-association and multisensory integration modelling. In Proc. of Int. Joint Conf. on Neural Networks, IJCNN’2005, IEEE-INNS, 2117–2122. Montréal, Canada, July 2005.

S. Chevallier, H. Paugam-Moisy and F. Lemaître. Distributed processing for modelling real-time multimodal perception in a virtual robot. In Proc. of Int. Conf. on Parallel Distributed Computing and Networks, PDCN’2005, ACTA Press, 393–398. Innsbruck, Austria, February 2005.

D. Meunier and H. Paugam-Moisy. A "spiking" Bidirectional Associative Memory for modeling intermodal priming. In Proc. of Int. Conf. on Neural Networks and Computational Intelligence, NCI’2004, ACTA Press. 25–30. Grindelwald, Switzerland, February 2004.

Y. Bouchut, H. Paugam-Moisy and D. Puzenat. Asynchrony in a distributed modular neural network for multimodal integration. In Proc. of Int. Conf. on Parallel and Distributed Computing & Systems, PDCS’2003, ACTA Press, 588-593. Marina del Rey, USA, November 2003.

H. Paugam-Moisy, D. Puzenat, E. Reynaud and J.-Ph. Magué. Neural networks for modelling memory : Case studies [tutorial]. In Proc. of European Conference on Artificial Neural Networks, ESANN’2002, D-facto, 71-82. Bruges, Belgium, April 2002.

O. Teytaud and H. Paugam-Moisy. Bounds on the generalization ability of Bayesian inference and Gibbs algorithms. In Proc. of International Conference on Artificial Neural Networks, ICANN’2001, Lecture Notes in Computer Science 2130, Springer, 263–268. Vienne, Austria, August 2001.

H. Paugam-Moisy and E. Reynaud. Multi-network system for sensory integration. In Proc. of Int. Joint Conf. on Neural Networks, IJCNN’2001, IEEE-INNS, 2343–2348. Washington DC, US, July 2001.

Y. Guermeur, A. Elisseeff and H. Paugam-Moisy. A new multi-class SVM based on a uniform convergence result. In Proc. of International Joint Conference on Neural Networks, IJCNN’2000, IEEE-INNS, IV :183–188. Como, Italy, July 2000.

H. Paugam-Moisy, A. Elisseeff and Y. Guermeur. Generalization performance of multiclass discriminant models. In Proc. of IJCNN’2000, IEEE-INNS, IV :177-182. Como, Italy, July 2000.

A. Crépet, H. Paugam-Moisy, E. Reynaud and D. Puzenat. A modular neural network for binding several modalities. In Proc. of International Conference on Artificial Intelligence, IC-AI’2000, 921–928. CSREA Press. Las Vegas, US, June 2000.

E. Reynaud, A. Crépet, H. Paugam-Moisy and D. Puzenat. A modular neural network model for a multi-modal associative memory. In Proc. of 4th International Conference on Cognitive and Neural Systems, ICCNS’2000, 17. Boston, US, June 2000.

Y. Guermeur, A. Elisseeff, and H. Paugam-Moisy. Estimating the sample complexity of a multiclass discriminant model. In Proc. of 9th International Conference on Artificial Neural Networks, ICANN’99, 310–315. IEE, 1999.

C. Bertolini, H. Paugam-Moisy, and D. Puzenat. Priming an Artificial Associative Memory. In J. Mira and J.V. Sanchez-Andres, editors, Proc. of International Workshop on Artificial Neural Networks, IWANN’99, LNCS, 1606 :348–356. Springer, 1999.

Y. Guermeur, H. Paugam-Moisy, and P. Gallinari. Multivariate Linear Regression on Classifier Outputs : A Capacity Study. In L. Niklasson, L. Bodén and T. Ziemke, editors, Proc. of 8th International Conference on Artificial Neural Networks, ICANN’98, 693–698. Springer, 1998.

A. Elisseeff, and H. Paugam-Moisy. Jacobian Neural Network Learning Algorithms. In L. Niklasson, L. Bodén and T. Ziemke, editors, Proc. of 8th International Conference on Artificial Neural Networks, ICANN’98, 573–578. Springer, 1998.

H. Paugam-Moisy. Neural networks : from massively parallel processing towards modular distributed processing. Invited talk, in Proc. of VIII Congresso Latinoamericano de Control Automatica, 1: 31–36. Viña del Mar, CHILI. 1998.

P. Estevez, M. Ugarte, H. Paugam-Moisy, and D. Puzenat. Modular Parallel Implementation for HME Neural Networks. In Proc. of 1998 Int. Conf. on Parallel and Distributed Processing Technique and Applications, PDPTA’98, III :1365–1372. CSREA Press, ISBN 1-892512-06-8, 1998.

P. Estevez, M. Ugarte, H. Paugam-Moisy, and D. Puzenat. Parallel Simulation of Modular Neural Networks. In Proc. of XII Congreso Chileno de Ingenieria Electrica, 555–560. Universidad de La Frontera, Temuco, CHILI, 1997.

G. Brightwell, C. Kenyon, and H. Paugam-Moisy. Multilayer neural networks : one or two hidden layers ? In M.C. Mozer, M.I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, Proc. NIPS*96, 148–154. MIT Press, 1997.

A. Elisseeff and H. Paugam-Moisy. Size of multilayer networks for exact learning : analytic approach. In M.C. Mozer, M.I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, Proc. NIPS*96, 162–168. MIT Press, 1997.

V. Demian, F. Desprez, H. Paugam-Moisy, and M. Pourzandi. Parallel implementation of RBF neural networks. In Parallel Processing, Proc. EuroPar’96, LNCS, 1124 :243–250. Springer, 1996.

B. Girau and H. Paugam-Moisy. Load sharing in the training set partition algorithm for parallel neural learning. In 9th Int. Parallel Processing Symposium, Proc. IPPS’95, 586–591. IEEE Press, 1995.

D. Girard and H. Paugam-Moisy. Strategies of weight updating for parallel back-propagation. In C. Girault, editor, Applications in Parallel and Distributed Computing, volume A-44 of IFIP Transactions, 335–336. North-Holland, 1994.

A. Azcarraga, H. Paugam-Moisy, and D. Puzenat. An incremental neural classifier on a MIMD parallel computer. In C. Girault, editor, Applications in Parallel and Distributed Computing, volume A-44 of IFIP Transactions, 13–22. North-Holland, 1994.

M. Cosnard, P. Koiran, and H. Paugam-Moisy. A step towards the frontier between one-hidden layer and two-hidden layer neural networks. In Proc. Int. Joint Conf. Neural Networks, IJCNN’93, 3 :2292–2295. Nagoya, Japan, 1993.

S. Amghar, H. Paugam-Moisy, and J.-P. Royet. Learning methods for odor recognition modeling. In B. Bouchon-Meunier, L. Valverde, and R.R. Yager, editors, Advanced Methods in Artificial Intelligence, Proc. IPMU’92, LNCS, 682: 361–367. Springer Verlag, 1992.

H. Paugam-Moisy. On a parallel algorithm for back-propagation by partitioning the training set. In N. Giambasi, J.-C. Rault, and M. Ribes, editors, Proc. Neuro-Nîmes’92, 53–66. EC2, 1992.

H. Paugam-Moisy. Optimal speedup conditions for a parallel back-propagation algorithm. In Parallel Processing, Proc. CONPAR’92-VAPP V, L. Bougé, M. Cosnard, Y. Robert, and D. Trystram, editors, LNCS, 634: 719–724. Springer Verlag, 1992.

H. Paugam-Moisy. On the convergence of a block-gradient algorithm for back-propagation learning. In Proc. Int. Joint Conf. Neural Networks, IJCNN’92-Baltimore, III :919–924, 1992.

M. Cosnard, P. Koiran, and H. Paugam-Moisy. Complexity issues in neural network computations. In I. Simon, editor, Latin American Symposium on Theoretical Informatics, Proc. LATIN’92, LNCS, 583: 530–544. Springer Verlag, 1992.

M. Cosnard, J.-C. Mignot, and H. Paugam-Moisy. Implementations of multilayer neural networks on parallel architectures. In 2nd Int. Specialist Seminar on the Design and Application of Parallel Digital Processors, Conf. Publi. 334: 43–47. IEE, 1991.

H. Paugam-Moisy. Selecting and parallelizing neural networks for improving performances. In T. Kohonen, K. M kisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks, Proc. ICANN-91, I :659–664. North-Holland, 1991.

H. Paugam-Moisy. A parallel tool for evaluating learning capabilities of neural networks. In Hamza, M.H., editor, Proc. IASTED Int’al Symposium in : Machine Learning and Neural Networks, 59–62. ACTA Press, 1990.

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Last update: June 29th, 2011