Modeling and simulation

Computational modeling uses standard differential equations like the following

 

 

where V is the membrane potential of the neuron. The numerical simulation of such equations is however handicaped by the discontinuities during the spikes. Depending on the modeling formalism (Hodgkin-Huxley or integrate-and-fire), we employ different simulation schemes.

Time-stepping simulation : sirene

With time-stepping (Fig. 1), time is discretized by arbitrary time-steps. Our time-stepping simulator is Sirene.

 

Fig. 1. Time-stepping

 

Voltage-stepping and event-driven simulation : Mvaspike

With voltage-stepping (Fig. 2), V is discretized by arbitrary voltage-steps. Adaptive time-steps are implicitly defined through discretization of the voltage state-space (Zheng et al., 2009).

 

Fig. 2. Voltage-stepping

With event-driven (Fig. 3), the spike timings are given analytically and calculated with arbitrary precision (Rochel and Martinez, 2003;Tonnelier et al., 2007).

 

Fig. 3. Event-driven

Our event-driven simulator is Mvaspike. Recently, we implemented voltage-stepping as a local event-driven strategy within Mvaspike (Kaabi et al., 2011).

Relevant publications:


Kaabi M.G., Tonnelier A. and Martinez D. (2011) On the performance of voltage-stepping for the simulation of adaptive, nonlinear, integrate-and-fire neuronal networks. Neural Computation, 23, 1187-1204.

Zheng G., Tonnelier A. and Martinez D. (2009) Voltage-stepping schemes for the simulation of spiking neural networks. Journal of Computational Neuroscience, 26:3, 409-423.

Tonnelier A., Belmabrouk H. and Martinez D. (2007) Event-driven simulations of nonlinear integrate-and-fire neurons. Neural Computation, Vol. 19, No. 12, pp. 3226-3238.  

Rochel O. and Martinez D. (2003) An event driven framework for the simulation of networks of spiking neurons. European Symposium on Artificial Neural Networks (ESANN), 2003, Bruges, Belgium.

Publications

Journal papers

  • Marco S., Gutiérrez-Gálvez A., Lansner A., Martinez D., Rospars J.-P., Beccherelli R., Perera A., Pearce T. C., Verschure P. F. M. J., Persaud K. (2014) A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation Microsyst. Technol. 20:729–742.
  • J. Fonollosa, A. Gutierrez-Galvez, A. Lansner, D. Martinez, J.P. Rospars, R. Beccherelli, A. Perera, T. Pearce, P. Vershure, K. Persaud, S. Marco (2011), Biologically Inspired Computation for Chemical Sensing, Procedia Computer Science, 7, 226–227.
  • Shoushun C., Bermak A., Yan W. and Martinez D. (2007) Adaptive-Quantization Digital Image Sensor for Low-Power, Real-Time, Image Compression.  IEEE Trans. Circuits and Systems, part I, 54(1), pp. 13-25.  
  • Ambard M. and Martinez D. (2006) Inhibitory control of spike timing precision.  Neurocomputing, Vol. 70, Issues 1-3, pp. 200-205.  
  • Hugues E. and Martinez D. (2005) Encoding in a network of sparsely connected spiking  neurons :  application to locust olfaction. Neurocomputing, Vol. 65-66, pp. 537-542.  
  • Bermak A. and Martinez D. (2003) A compact 3D VLSI Classifier using bagging threshold network ensembles. IEEE Transactions on Neural Networks, Special Issue on Neural Networks Hardware Implementations, Vol 14, no 5, pp. 1097-1109.
  • Jervis B.W., Desfieux J., Jimenez J. and Martinez D. (2003) The quantification of gas concentrations in mixtures of known gases using an array of different tin oxide sensors. IEE Proceedings Science, Measurement and Technology, Vol 150, no 3, pp. 97- 106.
  • Schweizer-Berberich M., Zdralek M., Weimar U., Gopel W., Viard T., Martinez D., Seube A. and A. Peyre-Lavigne (2000) Pulsed mode operation and artificial neural network evaluation for improving the CO selectivity of SnO2 gas sensors. Sensors and Actuators, 65, pp. 91-93.
  • Martinez D. (2000) Intelligent sensors using neural networks: the example of a microsystem for visual inspection. IEE Engineering Science and Education journal, Vol. 9, no. 5, pp. 236-240.
  • Martinez D. (1998) Time-Adaptive Vector A/D Conversion. IEEE Transactions on Circuits And Systems II, Vol. 45, no. 10, pp. 1420-1424.
  • Bermak A., Martinez D. and Noullet J.-L. (1997) High-density 16/8/4-bit configurable multiplier. IEE Proceedings Circuits Devices Syst., Vol. 144, no. 5, pp. 272-276.
  • Martinez D. and Estève D. (1995). Adaptive quantization and fault detection in smart sensors. Sensors and Actuators, Vol.46/47, no 1/3, pp.530-533.
  • Van Hulle M. and Martinez D. (1994). On a novel unsupervised competitive learning algorithm for scalar quantization. IEEE Transactions on Neural Networks, Vol. 5, no. 3, pp. 498-501.
  • Dilhan M., Martinez D. and Jaffrezic N. (1994) Les micro-capteurs chimiques. L'Onde Electrique, Vol. 74, no 2, pp.28-35.
  • Van Hulle M.M and Martinez D. (1993) On an unsupervised learning rule for scalar quantization following the maximum entropy principle. Neural Computation, Vol. 5, no. 6, pp. 939-953.

 
Book chapters
 

  • Martinez D. (2012) Olfaction artificielle et inspiration biologique. in Odorat et Goût : de la neurobiologie des sens chimiques aux applications agronomiques, industrielles et médicales,  R. Salesse, R. Gervais (eds.), QUAE, ISBN: 978-2-7592-1770-0, pp. 393-399.
  • Bermak A., Belhouari S., Shi M., and Martinez D. (2006) Pattern Recognition Techniques for Odor Discrimination in Gas Sensor Array, in The Encyclopedia of Sensors, C.A. Grimes, E.C. Dickey and M.V. Pishko (eds.), ISBN:1-58883-063-2, American Scientific Publishers, Vol. 7, pp. 349-365.
  • Martinez D. and 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, J.W. Gardner and J. Yinon (eds.), Chapter 14, pp. 209-234, Kluwer Academic Publishers.

 
International conference papers
 

  • Bouajila, Boutayeb & Martinez (2015) 3rd CEET Conf., Kuala Lumpur, Malaisia (April, 2015)

  • Marco, Gutiérrez-Gálvez, Lansner, Martinez, Rospars, Beccherelli, Perera, Pearce, Vershure & Persaud (2013) Proc. SPIE 8763, Smart Sensors, Actuators, and MEMS VI, 876303 (May 17, 2013)

  • Yamani J., F. Boussaid, A. Bermak, and D. Martinez (2013), Experimental evaluation of latency coding for gas recognition, 8th IEEE International Design and Test Symposium (IDT), Marrakesh.
  • Yamani J., F. Boussaid, A. Bermak, and D. Martinez (2012), Bio-Inspired Gas Recognition Based on the Organization of the Olfactory Pathway, IEEE International Symposium on Circuits and Systems ISCAS 2012.
  • Tarzan-Lorente M., Gutierrez-Galvez A., Martinez D. and Marco Colas S. (2010), A Biologically Inspired Associative Memory for Artificial Olfaction, Int. Joint Conference on Neural Networks IJCNN 2010, Barcelona.
  • Lechon M., Martinez D., Verschure P. and Bermudez i Badia S. (2010), The role of neural synchrony and rate in high-dimensional input systems. The Antennal Lobe: a case study. Int. Joint Conference on Neural Networks IJCNN 2010, Barcelona.
  • Chen H.T., Bermak A. and Martinez D. (2009) Towars a bio-inspired micro-electronic nose, The 13th International Symposium on Olfaction and Electronic Nose, Int. symposium on Olfaction and Electronic Nose ISOEN 2009, Brescia, Italy, April 15-17, 2009.
  • Ng K.T., Guo B., Bermak A., Martinez D. and Boussaid F. (2009) Characterization of a logarithmic spike timing encoding scheme for a 4x4 tin oxide sensor array, IEEE sensors conference, 25-28 October 2009 Christchurch, New Zealand.
  • Ng K.T., Chen H.T., Boussaid F., Bermak A. and Martinez D. (2009) A robust spike-based gas identification technique for Sn02 Gas sensors, IEEE International Symposium on Circuits and Systems, ISCAS 2009,Taipei, Taiwan from 24-27 May 2009
  • Buckley C. L., Martinez D., Rospars J.-P., Nowotny T. (2009) Transient winner takes-all dynamics in the pheromone system of the moth, SFN Neuroscience. Oct. 17-21 2009, Chicago, USA
  • Ng T.K., Guo B., Boussaid F., Bermak A. and Martinez D. (2008) A 4×4 Tin Oxide Gas Sensor Array based on Spike Sequence Matching. IEEE International Conference on SCS.
  • Ambard M., Martinez D., Guo B. and Bermak A. (2008) A Spiking Neural Network for Gas Discrimination using a Tin Oxide Sensor Array. 4th IEEE International Symposium on Electronic Design, Test & Applications.
  • Chen S., Bermak A., Yan W. and Martinez D. (2007) A CMOS Image Sensor with on Chip Image Compression based on Predictive Boundary Adaptation and QTD Algorithm, the 6th IEEE Sensors Conference, Atlanta, Georgia, USA, pp. 531-534 Oct, 2007.
  • Hu H.L., Bermak A. and Martinez D. (2007) A new video Compression Scheme combining conditional replenishment and address event representation, IEEE Workshop on Signal Processing Systems, pp. 573-578, Shanghai, China, 2007.
  • Guo B. and Bermak A. and Martinez D. (2006) A 4x4 Logarithmic Spike Timing Encoding Scheme for Olfactory Sensor Applications. IEEE International Symposium on Circuits and Systems ISCAS2007, pp. 3554-3557, New Orleans, USA, 2007
  • Chen S., Bermak A., Wan W. and Martinez D. (2006a) A CMOS image sensor with combined adaptive-quantization and QTD-based on-chip compression processor. IEEE Custom Integrated Circuits Conference (CICC), pp. 329-332, SJ, California, USA, Sept. 2006.
  • Chen S., Bermak A., Wan W. and Martinez D. (2006b) Smooth Boundary Point Adaptive Quantizer for On-Chip Image Compression. 8th International Conference on Microelectronics, Dhahran, S.A Dec. 2006, pp. 214-217.
  • Climie D., Chen S., Bermak A. and Martinez D. (2005) Frame based adaptation for CMOS imager. Fith International Conference on Information, Communications and Signal Processing (ICICS 2005), Bangkok, Thailand, 6-9 Dec. 2005.
  • Shi M., Brahim-Belhouari S., Bermak A. and Martinez D. (2005) Committee machine for odor discrimination in gas sensor array, Proceedings of the 11th International Symposium on olfaction and electronic nose (ISOEN), pp. 74-76, Barcelona 13-15 April 2005.
  • Bermak A.and Martinez D. (2004) A reconfigurable hardware implementation of tree classifiers based on a custom chip and a CPLD for gas sensor applications. IEEE Tencon, Chiang Mai, Thailand, Nov. 21-24 2004. 
  • David F. and Hugues E. and Buonviso N. and Martinez D. (2004) Evoked gammma oscillations in the olfactory bulb: a modelling study. AMSE International Conference on modelling and Simulation, Lyon, July 5-7 2004. 
  • Hugues E., Rochel O. and Martinez D. (2003) Navigation strategies for a robot in a turbulent odor plume using bilateral comparison. International Conference on Advanced Robotics (ICAR), June 30 - July 3, 2003, University of Coimbra, Portugal.
  • Bermak A. and Martinez D. (2003) A Very High Density VLSI Implementation of Threshold Network Ensembles (TNE). IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 6-10, 2003, Hong Kong.
  • Rochel O. and Martinez D. (2003) An event driven framework for the simulation of networks of spiking neurons. European Symposium on Artificial Neural Networks (ESANN), 2003, Bruges, Belgium.
  • Martinez D. and Perrinet L. (2002) Cooperation between vision and olfaction in a koala robot.   Telluride, CO, report on the workshop on neuromorphic engineering 2002 , pp. 51-53.  
  • Rochel O., Martinez D., Hugues E. and Sarry F. Stereo-olfaction with a sniffing neuromorphic robot using spiking neurons.In 16th European Conference on Solid-State Transducers - EUROSENSORS. Prague, Czech Republic.
  • Bermak A., Hopfinger M. and Martinez D. (2002) Image segmentation using Spiking Pixel Architecture. In Special session on Next Generation Image Sensors for Multimedia applications - 6th World Multi-Conference on Systemics,Cybernetics and Informatics, (SCI). July 2002, Orlando, Florida, USA).
  • Bermak A. and Martinez D. (2001) A compact Multi-Chip-Module implementation of a multi-precision neural network classifier. Proceedings of the IEEE International Symposium on Circuits and Systems ISCAS2001, Volume III, pp. 249-252, Sydney, Australia.
  • Martinez D. and Millerioux G. (2000) Support Vector Committee Machines. European Symposium on Artificial Neural Networks (ESANN), 26-28 April 2000, Bruges, Belgium, pp. 43-48.
  • Bermak A. and Martinez D. (1999) Digital VLSI implementation of a multi-precision neural network classifier. 6th International Conference on Neural Information Processing ICONIP'99, November 1999, Perth, Australia. 
  • Reyna R., Estève D. and Martinez D. (1999) An integrated vision system: object detection and localization.  3rd International Workshop on design of mixed mode integrated circuits and applications,  26-28 Juillet 1999, Puerto Vallartha (Mexique) pp. 118-121.
  • Albu F. and Martinez D. (1999) The Application of Support Vector Machines with Gaussian Kernels for Overcoming Co-channel Interference. IEEE Workshop on Neural Networks for Signal Processing IX,  23-25 August, 1999, Madison, Wisconsin (USA), pp.49-57.
  • 71.  Albu F. and Martinez D. (1998) Support vector classifiers: application to digital communications channel equalization. The fourth all-ukrainian international conference on signal/image processing and pattern recognition UKROBRAZ'98,  19-23 Oct. 1998 Kyjiv (Ukraine), pp. 57-62. 
  • Martinez D. (1998) On-line adaptive histogram equalization.  IEEE Workshop on Neural Networks for Signal Processing VIII, Cambridge (UK), 31 August - 3 Sept. 1998, pp.531-538.   
  • Martinez D. and Yang W. (1996) Competitive learning algorithms for channel optimized vector quantizers. IEEE International Conference on Neural Networks (ICNN'96), Washington (USA), 3-6 Juin 1996, pp.1462-1467.
  • Bermak A. and Martinez D. (1996) A variable-precision systolic architecture for ANN computation. Fifth International Conference on Microelectronics for Neural Networks and Fuzzy Systems. (MicroNeuro'96), Lausanne (Suisse), 12-14 Février 1996, pp.347-354.
  • Fairclough S., Planque S., Martinez D. and Brookhuis K. (1994). Behavioural responses to a driver impairment monitoring system.World Congress on Advanced Transport Telematics, 30 Nov.-3 Dec. 1994, Paris (France).
  • Van Hulle M. and Martinez D. (1994). Adaptive compendor design using the boundary adaptation rule. IEEE ICNN International Conference on Neural Networks, June 28 - July 2, pp 3597-3600. 1994.
  • Noullet J.L. and Martinez D. (1994) Switched capacitor adaptive non-uniform A/D conversion. TEMPUS JEP 4343 Workshop on Mixed design VLSI Circuits, pp. 136-141. Debe (Poland), 5-9 April 1994.
  • Martinez D. and Van Hulle M. (1993). Recurrent neural network for adaptive non-uniform A/D conversion. World congress on neural networks, Vol. 4, pp. 576-579. Portland (USA), July 11-15 1993.
  • Martinez D., Poulard H., Chan M., Herrera A., Abadie L. and Estève D. (1993). Incremental learning in a multilayer neural network for process-fault detection, ToolDiag'93 International Conference on Fault Diagnosis, pp. 154-159, Toulouse (France), April 5-7 1993.
  • Martinez D. (1992). Dealing with continuous inputs for the Offset algorithm.The International Conference on signal processing applications and technology, 2-5 Novembre 1992, Boston (USA), 1992, pp. 1167-1175.
  • Martinez D., Chan M. and Estève D. (1992). Construction of layered quadratic Perceptrons. Proc. Neuro-Nimes'92, 2-5 Novembre 1992, Nimes (France), pp. 655-665.
  • Martinez D. and Estève D. (1991). A simple strategy for building multilayer neural networks. International Joint Conference on Neural Networks (IJCNN), Juillet 1991, Seattle (USA).
  • Martinez D., Estève D. and Demmou H. (1990). Une approche modulaire pour la reconnaissance de situations de danger en conduite automobile. Proc. Neuro-Nimes'90, 2-5 Novembre 1990, Nimes (France), pp. 71-80.

 
International conference abstracts

  • Martinez D. (2014) Neural correlates of a search strategy. Flavour. 3(1):O26
  • Defaix C., Anton S., Rospars J-P., Martinez D., Lucas P (2014) Firing properties and ionic currents of antennal lobe neurons of a moth. 11th international workshop Neural Coding, Versailles., 6-10 October.
  • Voges N, Chaffiol A, Lucas P, Martinez D (2013). Post-stimulus firing and the corresponding olfactory search strategy. 10th meeting of the German Neuroscience Society, Goettingen
  • Voges N., Chaffiol A., Lucas P., Martinez D. (2012). Post-stimulus firing and the corresponding olfactory search strategy. 3rd Workshop on Multielectrodes arrays. Marseille, 25-26 October.
  • Voges N., Chaffiol A., Buhry L., Lucas P., Martinez D. (2012). Post-stimulus firing and the corresponding olfactory search strategy. Society For Neuroscience (SFN) conference. Poster 501. Neuroethology: Sensory Systems I, New Orleans, 13-17 October.
  • Martinez D. (2011). Stereotyped firing response patterns in moth antennal lobe neurons: experiments, models and functional implications. Workshop on Bioinspired computation for chemical sensing. Barcelona, Spain, 9-11 March.
  • Belmabrouk H., Rospars J.P. and Martinez D. (2011). A computational model of the moth macroglomerular complex Belmabrouk. CNS*2011, July 23-28, 2011 in Stockholm, Sweden
  • Gu Y., Belmabrouk H., Chaffiol A., Rospars J.-P. and Martinez  D. (2011). Modelling the cellular mechanisms underlying the multiphasic response of moth pheromone-sensitive projection neurons (PNs). 9th Göttingen Meeting of the German Neuroscience Society, 23-27 Mars, 2011
  • Voegtlin T., Martinez D. (2011) The role of sinusoidal undulations for klinotaxis in c. elegans. Workshop on Bioinspired computation for chemical sensing, Barcelone, Spain, 9-11 March.
  • Belmabrouk H., Gu Y., Nowotny T., Rospars J.-P., Martinez M. (2011) Role of local inhibition and neuronal properties in a model of the moth macroglomerular complex. Workshop on Bioinspired computation for chemical sensing, p. 40. Barcelone, Spain, 9-11 March.
  • Gu Y., Belmabrouk H., Chaffiol A., Rospars J-P. and Martinez D. (2010) Modelling the cellular mechanisms underlying the multiphasic response of moth pheromone-sensitive projection neurons. European Chemoreception Research Organization (ECRO) XXth CONGRESS 14-19 September 2010 Le Palais des Papes, Avignon, France
  • Martinez D., Chaffiol A., Gu Y., Anton S. and Rospars J-P. (2010) Stereotyped firing response patterns in moth antennal lobe neurons: from experiments to models. European Chemoreception Research Organization (ECRO) XXth CONGRESS 14-19 September 2010 Le Palais des Papes, Avignon, France.
  • Grémiaux A., Jarriault D., Chaffiol A., Anton S., Martinez D. and Rospars J.P. (2010) Analysis of the Signal Transformation From First- to Second-order Neurons in the Moth Sex-Pheromone Olfactory System p 41 Abstract book, 9th international workshop Neural Coding, Limassol, Cyprus, 29 Oct.-3 Nov 2010.
  • Zavada A., Buckley C. L., Martinez D., Rospars J-P and Nowotny T. (2010) Minimal model of blend recognition in the moth pheromone system p6 Abstract book, Dynamical olfaction workshop, Brighton (UK) 30 June-2 July 2010.
  • Rospars J-P, Grémiaux A., Gu Y., Jarriault D., Kostal L., Lansky P., Anton S., Nowotny T., Lucas P. and Martinez D. (2010) Olfactory receptor neurons: A comparative analysis of their response properties with diverse stimuli in different species p 8 Abstract book, Dynamical olfaction workshop, Brighton (UK) 30 June-2 July 2010.
  • Buckley C. L., Martinez D., Rospars J.-P., Chaffiol A. and Nowotny T. (2010) Critical Rate Dynamics Explain the Dynamic Range of the Moth Pheromone System p 14 Abstract book, Dynamical olfaction workshop, Brighton (UK) 30 June-2 July 2010.
  • Martinez D., Chaffiol A., Belmabrouk H., Gu Y., Anton S., Lucas P. and Rospars  J-P (2010) Stereotyped firing response patterns in moth antennal lobe neurons: experiments and models p 20 Abstract book, Dynamical olfaction workshop, Brighton (UK) 30 June-2 July 2010.
  • Martin Moraud E. and Martinez D. (2010) Scent-tracking by autonomous robots: Infotaxis and beyond p 24 Abstract book, Dynamical olfaction workshop, Brighton (UK) 30 June-2 July 2010.
  • Gu Y., Belmabrouk H., Rospars J-P., Chaffiol A. and Martinez D. (2010) A biophysical model reproduces the multiphasic firing patterns observed in moth antennal lobe neurons p 30 Abstract book, Dynamical olfaction workshop, Brighton (UK) 30 June-2 July 2010.
  • Belmabrouk H., Gu Y., Nowotny T., Rospars J-P. and Martinez D. (2010) A model of the moth macroglomerular complex: interplay between interglomerular inhibition and neuronal intrinsic properties p 31 Abstract book, Dynamical olfaction workshop, Brighton (UK) 30 June-2 July 2010.
  • Grémiaux A., Jarriault D., Chaffiol A, Anton S., Martinez D. and Rospars J.P. (2010) Signal transformation from olfactory receptor neurons to central neurons p 36 Abstract book, Dynamical olfaction workshop, Brighton (UK) 30 June-2 July 2010
  • Chaffiol A., Anton S., Rospars J-P. and Martinez D. (2009), Spike timing precision of pheromone sensitive neurons in the antennal lobe of the moth Agrotis ipsilo, ESITO XI European Symposium for Insect Taste and Olfaction, 19-24 September 2009, 102. Villasimius, Sardinia, Italy.
  • Montejo N. and Martinez D. (2007) Opposite role of slow and fast GABAergic inhibition in synchronization and spike timing precision. Sixteenth Annual Computational Neuroscience Meeting: CNS, Toronto, Canada.July 2007.
  • Voegtlin T. and Martinez D. (2006) Effect of Asynchronous GABA Release on the Oscillatory Dynamics of Inhibitory Coupled Neurons. Poster at the computational neuroscience meeting (CNS 2006).
  • Ambard M. and Martinez D. (2006) Effect of variable inhibition on spike timing precision in the olfactory bulb. Poster at the computational and systems Neuroscience conference (COSYNE 2006), Salt Lake City, USA, March 5-8, 2006, Abstract book p. 44.
  • Martinez D. (2005) From oscillatory synchronization with spiking neurons to binary neurons. Computational neuroscience meeting (CNS 2005), Abstract book p. 47.
  • Hugues E. and Martinez D. (2004) Encoding in a network of sparsely connected spiking  neurons:  application to locust olfaction. Computational neuroscience meeting (CNS 2004), Baltimore, July 18-22 2004.

 
National Conference Papers
 

  •  Chaffiol A., Rospars J-P. and Martinez D. (2009) Etude de la précision de la décharge des neurones sensibles la phéromone dans le lobe antennaire d’un papillon de nuit Agrotis Ipsilon. 10ème rencontre du club de neurobiologie des invertébrés, p. 14, Dijon, 14-15 Mai 2009
  •  Grémiaux A., Martinez D. and Rospars J-P. (2009) Characteristics of olfactory receptor neuron population responses to sexual pheromone in the moth, 10ème rencontre du club de neurobiologie des invertébrés, p. 30, Dijon, 14-15 Mai 2009
  •  Belmabrouk H., Rospars J.-P., Ezzine J. and Martinez D. (2008). Rôle de l'inhibition intra- et inter-glomérulaire dans la synchronisation neuronale et le codage phéromanal chez Manduca sexta. Neurocomp08, Marseille, France
  •  Kaabi M.G., Gang Z., Tonnelier A. and Martinez D. (2008). Simulation de réseaux de neurones intègre-et-tire non linéaires par discrétisation du potentiel. Neurocomp08, Marseille, France
  •  Rochel O. and Martinez D. (2002) Un modèle générique de neurone impulsionnel adapté à la simulation événementielle. Neurosciences et Sciences pour l'Ingénieur - NSI'2002
  •  Martinez D. (2002) Une introduction aux machines à vecteurs supports (support vector machines). Neurosciences et Sciences pour l'Ingénieur - NSI'2002
  •  Hugues E., Ginder G. and Martinez D. (2002) Etude de lois de navigation pour la recherche de sources de gaz. Neurosciences et Sciences pour l'Ingénieur - NSI'202.
  •  Rochel O. and Martinez D. (2000) Réjection du bruit dans des populations de neurones synchronisés. Neurosciences et Sciences pour l'Ingénieur - NSI'2000.

 
Vulgarisation, press releases
 

  • Des robots doués d'odorat ne sont plus un rêve, Science et Vie no 1166, p. 40, Nov. 2014. 
  •  Robot détecteur d'odeurs, Pour la Science no 320, p. 22, June 2004.
  • Un modèle biologique pour détecter des odeurs, Inédit de l'INRIA no 44, Mai 2004

 

Welcome to my homepage

I am a research scientist from the CNRS and work at LORIA, a computer science laboratory in Nancy, France. My research is based on an interdisciplinary approach using engineering techniques for system neuroscience and applying neurophysiological principles to technical implementations.

 

Engineering techniques for system neuroscience

We apply engineering techniques like neuronal modeling and biorobotics to study : 

  • Search strategies and their neural correlates: Animals and robots searching for chemical sources face similar problems. We investigate olfactory search strategies in insects by means of physiological recordings, computational modeling and robotic experiments. Models of search processes are important not only to biology, but also to applications in robotics (e.g. environmental monitoring, detection and localization of chemical, biological and radiological risks).
  • GABAergic mechanisms in neuronal synchrony and brain oscillations: Neuronal synchrony and brain oscillations are thought to be involved in the processing of sensory and motor information. We use computational modelling to gain insights into the role of GABAergic inhibition in brain oscillations.

Relevant publications:

Martinez D., Clement M., Messaoudi B., Gervasoni D., Litaudon P., Buonviso N. (2017) Adaptive quantization of local field potentials in freely moving animals: an open-source neural recording device. Journal of Neural Engineering.

Zennir M.N., Benmohammed M., Martinez D. (2017) Robust path planning by propagating rhythmic spiking activity in a hippocampal network model. Biologically Inspired Cognitive Architectures.

Martinez D. (2014) Klinotaxis as a basic form of navigation. Front. Behav. Neurosci., 8:38, doi: 10.3389/fnbeh.2014.00275: PDF with Supplementary Information

Martinez D., Arhidi L., Demondion E., Masson J.-B. and Lucas P. (2014) Using insect electroantennogram sensors on autonomous robots for olfactory searches. J. Vis. Exp (JoVE), (90), e51704, doi:10.3791/51704: VIDEO

Martinez D., Chaffiol A., Voges N., Gu Y., Anton S., Rospars J.-P. and Lucas P. (2013) Multiphasic On/Off pheromone signaling in moths as neural correlates of a search strategy. PLoS ONE, 8(4): e61220. doi:10.1371/journal.pone.0061220: PDF with Supplementary Information

Martinez D. (2007) On the right scent. Nature, 445, pp. 371-372.

Belmabrouk  H., Nowotny T., Rospars J.-P. and Martinez D. (2011) Interaction of cellular and network mechanisms for efficient pheromone coding in moths. Proc. Natl. Acad. Sci. USA. 108(49):19790-19795.

Martinez D. (2005) Oscillatory synchronization requires precise and balanced feedback inhibition in a model of the insect antennal lobe. Neural Computation, 17, pp. 2548-2570.

Read more about Engineering techniques for system neuroscience

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