Institut des Systèmes Intelligents
et de Robotique


Sorbonne Universite



Tremplin CARNOT Interfaces



Short bio

khamassi Mehdi
Title : Researcher
Address : 4 place Jussieu, CC 173, 75252 Paris cedex 05
Phone : +33 (0) 1 44 27 28 85
Email : khamassi(at)
Group : AMAC (AMAC)

Brief biography

In 2003, I graduated from both Université Pierre et Marie Curie, Paris (Master in Cognitive Science) and from an engineering school ENSIIE, Evry (Master in Computer Science). Then from 2003 until 2007, I prepared a PhD thesis between Université Pierre and Marie Curie and Collège de France under the supervision of Agnès Guillot and Sidney I. Wiener about learning and navigation in animals and robots. In 2008, I spent a short period at Kenji Doya's lab at Okinawa Institute of Science and Technology, Japan. Then I pursued a postdoctoral fellowship at INSERM in Lyon, where my work was at the interface between Emmanuel Procyk's neurophysiology team and Peter F. Dominey's modelling and robotics team.

Since 2010, I have been holding a tenured research scientist position at the French National Center for Scientific Research (CNRS) in the Institute of Intelligent Systems and Robotics at Sorbonne Université (ex: Université Pierre et Marie Curie), Paris, France. I am also director of studies and pedagogical council member for the Cogmaster program at Ecole Normale Supérieure / EHESS / Univ. Paris Descartes. I obtained my Habilitation to Direct Researches in Biology from Université Pierre et Marie Curie, Paris 6, on May 6th 2014. I have been an invited researcher at the Center for Mind/Brain Sciences, University of Trento, Italy in 2014-2015, where I was mainly collaborating with Giorgio Coricelli. Since January 2016, I have been an invited researcher at the Robotics Laboratory of the National Technical University of Athens, Greece, where I mainly collaborate with Costas Tzafestas. Since March 2017, I have also been an invited researcher at the Department of Experimental Psychology, University of Oxford, where I mainly collaborate with Matthew Rushworth and Jérôme Sallet.

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Research Activities

My research interest is at the interface between Neuroscience and Robotics focusing on: animals' reinforcement learning and flexible decision-making abilities; the associated neural mechanisms in the prefrontal cortex, hippocampus and basal ganglia; and their applications to learning in autonomous robots. I am studying how the brain efficiently coordinates different learning systems in parallel, with the hippocampus-prefrontal cortex network detecting the different states of the world (e.g. new vs familiar environment or context A vs context B) and the different performances of the agent (e.g progressing, stagnating, or dropping) to adaptively choose: the appropriate learning system in each situation (e.g. learning a cognitive graph of the environment or not), and the learning state (e.g. explore or exploit). These novel computational models are then tested on robotic platforms in the real-world with the dual goal of improving robots’ behavioral flexibility and testing biological hypotheses.



Selected publications

  • Lee, B., Gentry, R., Bissonette, G.B., Herman, R.J., Mallon, J.J., Bryden, D.W., Calu, D.J., Schoenbaum, G., Coutureau, E., Marchand, A., Khamassi, M.  and Roesch, M.R. (2018). Manipulating the revision of reward value during the intertrial interval increases sign tracking and dopamine releases. PLoS Biology, to appear. Commented by Eshel, N. & Steinberg, E.E. in the same issue.
  • Dollé, L. and Chavarriaga, R. and Guillot, A. and Khamassi, M. (2018). Interactions of spatial strategies producing generalization gradient and blocking: a computational approach. PLoS Computational Biology, 14(4):e1006092.
  • Bavard, S., Lebreton, M., Khamassi, M., Coricelli, G. and Palminteri, S. (2018). Reference point and range-adaptation produce both rational and irrational choices in human reinforcement learning. Nature Communications, to appear.
  • Viejo, G., Girard, B., Procyk, E. and Khamassi, M. (2018). Adaptive coordination of working-memory and reinforcement learning in non-human primates performing a trial-and-error problem solving task. Behavioural Brain Research, 355:76-89.
  • Khamassi, M., Quilodran, R., Enel, P., Dominey, P.F. and Procyk, E. (2015). Behavioral regulation and the modulation of information coding in the lateral prefrontal and cingulate cortex. Cerebral Cortex, 25(9):3197-218.
  • Palminteri, S., Khamassi, M., Joffily, M. and Coricelli, G. (2015). Contextual modulation of value signals in reward and punishment learning. Nature Communications, 6:8096.
  • Lesaint, F., Sigaud, O., Flagel, S.B., Robinson, T.E. and Khamassi, M. (2014). Modelling individual differences observed in Pavlovian autoshaping in rats using a dual learning systems approach and factored representations. PLoS Computational Biology, 10(2):e1003466.
  • Khamassi, M., Enel, P., Dominey P.F. and Procyk, E. (2013). Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters. Progress in Brain Research, 202:441-64.
  • Benchenane, K., Peyrache, A., Khamassi, M., Tierney, P.L., Gioanni, Y., Battaglia, F.P. and Wiener, S.I. (2010). Coherent theta oscillations and reorganization of spike timing in the hippocampal-prefrontal network upon learning. Neuron, 66(6):912-36.
  • Peyrache, A., Khamassi, M., Benchenane, K., Wiener, S.I. and Battaglia, F.P. (2009). Replay of rule-learning related neural patterns in the prefrontal cortex during sleep. Nature Neuroscience, 12(7):919-26.
  • Velentzas, G., Tsitsimis, T., Rano, I., Tzafestas, C. and Khamassi, M. (2018). Adaptive reinforcement learning with active state-specific exploration for engagement maximization during simulated child-robot interaction. Paladyn Journal of Behavioral Robotics, 9:235-253.
  • Chatila, R., Renaudo, E., Andries, M., Chavez Garcia, R.O., Luce-Vayrac, P., Gottstein, R., Alami, R., Clodic, A., Devin, S., Girard, B. and Khamassi, M. (2018). Towards Self-Aware Robots. Frontiers in Robotics and AI, 5:88.
  • Khamassi, M., Velentzas, G., Tsitsimis, T. and Tzafestas, C. (2018). Robot fast adaptation to changes in human engagement during simulated dynamic social interaction with active exploration in parameterized reinforcement learning. IEEE Transactions on Cognitive and Developmental Systems. To appear.
  • Aklil, N., Girard, B., Denoyer, L. and Khamassi, M. (2018). Sequential action selection and active sensing for budgeted localization in robot navigation. International Journal of Semantic Computing, 12(1):102-127.
  • Khamassi, M., Girard, B., Clodic, A., Devin, S., Renaudo, E., Pacherie, E., Alami, R. and Chatila, R. (2016). Integration of Action, Joint Action and Learning in Robot Cognitive Architectures. Intellectica, 2016(65):169-203.
  • Caluwaerts, K., Staffa, M., N'Guyen, S., Grand, C., Dollé, L., Favre-Félix, A., Girard, B. and Khamassi, M. (2012). A biologically inspired meta-control navigation system for the Psikharpax rat robot. Bioinspiration & Biomimetics, 7(2):025009.
  • Khamassi, M., Lallée, S., Enel, P., Procyk, E. and Dominey P.F. (2011). Robot cognitive control with a neurophysiologically inspired reinforcement learning model. Frontiers in Neurorobotics, 5:1.
  • Khamassi, M., Martinet, L.-E. and Guillot, A. (2006). Combining Self-Organizing Maps with Mixture of Experts: Application to an Actor-Critic Model of Reinforcement Learning in the Basal Ganglia. From Animals to Animats 9 (SAB 2006), Berlin, Heidelberg: Springer-Verlag, publisher. Pages 394-405.
  • Khamassi, M., Lachèze, L., Girard, B., Berthoz, A. and Guillot, A. (2005). Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats. Adaptive Behavior, 13(2):131-148
  • Meyer, J.-A., Guillot, A., Girard, B. Khamassi, M., Pirim, P. and Berthoz, A. (2005). The Psikharpax Project: Towards Building an Artificial Rat. Robotics and Autonomous Systems, 50(4):211-223.