Mehdi Khamassi

ORCID iD
https://orcid.org/0000-0002-2515-1046
  • Country
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France

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Mehdi Khamassi (2016-01-24)

  • Keywords
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cognitive robotics,

Sources:
Mehdi Khamassi (2014-03-26)

computational neuroscience,

Sources:
Mehdi Khamassi (2014-03-26)

machine learning,

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Mehdi Khamassi (2014-03-26)

decision-making,

Sources:
Mehdi Khamassi (2014-03-26)

reinforcement learning,

Sources:
Mehdi Khamassi (2014-03-26)

cognitive monitoring,

Sources:
Mehdi Khamassi (2014-03-26)

basal ganglia,

Sources:
Mehdi Khamassi (2014-03-26)

prefrontal cortex,

Sources:
Mehdi Khamassi (2014-03-26)

dopamine,

Sources:
Mehdi Khamassi (2014-03-26)

hippocampus

Sources:
Mehdi Khamassi (2014-03-26)

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Academic page

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Mehdi Khamassi (2016-01-13)

  • Other IDs
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Scopus Author ID: 6508136456

Sources:
Scopus to ORCID (2014-03-26)

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 work 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 Université Pierre et Marie Curie, Paris 6. I obtained my Habilitation to Direct Researches in Biology from Université Pierre et Marie Curie, Paris 6, on May 6th 2014. 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.
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