Dominique Martinez homepage

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:

Pannequin R., Jouaiti M., Boutayeb M., Lucas P., Martinez D. (2020) Automatic tracking of free-flying insects using a cable-driven robot. Science Robotics.

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.

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Neurophysiological principles for technical implementations

We apply neurophysiological principles to build invariant representations from sensory data.

• Robotic source seeking in turbulent medium. Searching for a source releasing particles in a turbulent medium, e.g. CBRN materials, is particularily challenging. The sensory landscape is heterogeneous and unsteady and the searcher has to rely on intermittent binary cues. Insects can be very efficient at solving this problem. For robotic searches, we apply information-theoric strategies like Infotaxis as it has been shown to produce trajectories similar to insects, e.g. moths attracted by a sexual pheromone.

• Latency coding in artificial olfaction. In the context of fast olfactory processing, relative latencies of different neurons may carry concentration-invariant information about stimulus identity. We designed simple yet robust latency coding schemes for processing gas sensor data (Chen et al., 2011; Yamani et al., 2012). The response of the gas sensor array is mapped into  latency patterns, whose rank order is concentration-invariant. This study pioneers the translation of neurophysiological findings into hardware for the processing of electronic noses.

• Invariant representations and blind source separation. Perception results from the way sensory information is transformed in the brain. Whereas sensory input varies rapidly (e.g. pixel intensity across an input image), hidden perceptual parameters (e.g. surface depth) vary smoothly over time.We derived a non-linear technique for extracting slowly varying features from rapidly varying signals and for non-linear blind source separation (Martinez and Bray, 2003).

Relevant publications:

Martinez D., Burgués J., Marco S. (2019) Fast measurements with MOX sensors: A least-squares approach to blind deconvolution. Sensors.

Zhang S., Martinez D., Masson JB. (2015) Multi-robot searching with sparse binary cues and limited space perception. Frontiers in Robotics and AI.

Martin-Moraud E. and Martinez D. (2010) Effectiveness and robustness of robot infotaxis for searching in dilute conditions. Frontiers in neurorobotics.

Yamani J., Boussaid F., Bermak A., and Martinez D. (2012) Glomerular latency coding in artificial olfaction. Frontiers in Neuroengineering, 4:18. doi: 10.3389/fneng.2011.00018.

Chen H.T., Ng K.T., Bermak A., Law M.K. and Martinez D. (2011) Spike latency coding in a biologically inspired micro-electronic nose. IEEE Trans. Biomedical Circuits and Systems, 5:2, 160-168.

Martinez D. and Bray A. (2003) Nonlinear blind source separation using kernels. IEEE Transactions on Neural Networks, Vol 14, no 1, pp. 228-236.

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