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Accueil du site > Equipes de recherche > Memory and Learning of Objects and Scenes (MAOS) > Memory and Learning of Objects and Scenes (MAOS)

Memory and Learning of Objects and Scenes (MAOS)

Head : Emmanuel BARBEAU

The primate’s visual system shows a surprising efficiency. In a fraction of a second, it is possible to detect, categorize, recognize and identify objects in the complex surrounding world. The PROS team focuses on the recognition and the neural representation of objects (common, famous and unique) and object categories. One of its specificity is the use of biologically pertinent stimuli, more specifically photographs of natural scenes. By combining experimental projects in healthy humans and patients (psychophysic, EEG, fMRI, intracranial recordings) with theoretical approaches and models, the aims of the team include : elucidating the key mechanisms underlying the amazing efficiency of the biological visual systems, analysing how learning and expertise can shape visual representations and the neural substrates ensuring their memorization, describing the effects of healthy and pathological aging. Computational neuroscience studies have had a major impact. Specifically, via the development of a software for object recognition commercialized through SpikeNet Technology (a spin-off company created in 1999 by 3 of Cerco’s researchers).

Research themes :

  • Changes in neural representations following learning and attention Responsable : Leila Reddy
  • Familiarity for well- or less well-known stimuli. Responsable : Emmanuel Barbeau
  • Very long term memories. Responsable : Simon Thorpe
  • Acquisition of new objects and words throughout life. Responsable : Florence Rémy
  • Memory pathologies. Responsable : Emmanuel Barbeau
  • Models of learning. Responsable : Simon Thorpe

Permanent research staff :

Research support :

  • Karine BOUYER
  • Charles DAVIS
  • Martin DEUDON
  • Sophie MURATOT

Post-Doctoral fellows :

  • Katharina DOBS
  • Jacob MARTIN
  • Evelina THUNELL

PhD students :

  • Jonathan CUROT
  • Elodie DESPOUY
  • Paul FERRE
  • Pierre-Yves JONIN
  • Christelle LARZABAL
  • Danaé REMON
  • Jaya VISWANATHAN

Selected recent publications :

  • Alexandre V, Mercedes B, Valton L, Maillard L, Bartolomei F, Szurhaj W, Hirsch E, Marchal C, Chassoux F, Petit J, Crespel A, Nica A, Navarro V, Kahane P, De Toffol B, Thomas P, Rosenberg S, Denuelle M, Jonas J, Ryvlin P, Rheims S ; REPO2MSE study group. (2015). Risk factors of postictal generalized EEG suppression in generalized convulsive seizures. Neurology, 85(18):1598-603.
  • Aubert S, Bonini F, Curot J, Valton L, Szurhaj W, Derambure P, Rheims S, Ryvlin P, Wendling F, McGonigal A, Trébuchon A, Bartolomei F. (2016). The role of subhippocampal versus hippocampal regions in bitemporal lobe epilepsies. Clinical Neurophysiology, 127(9):2992-9.
  • Barragan-Jason G, Cauchoix M, Barbeau EJ. (2015). The neural speed of familiar face recognition. Neuropsychologia, 75:390-401
  • Besson G, Ceccaldi M, Tramoni E, Felician O, Didic M, Barbeau EJ. (2015). Fast, but not slow, familiarity is preserved in patients with amnestic Mild Cognitive Impairment. Cortex, 65 : 36-49.
  • Boucart M, Lenoble Q, Quettelart J, Szaffarczyk S, Despretz P, Thorpe SJ. (2016). Finding faces, animals, and vehicles in far peripheral vision. Journal of Vision 16(2):10.
  • Busigny T, de Boissezon X, Puel M, Nespoulous JL, Barbeau EJ. (2015). Proper name anomia with preserved lexical and semantic knowledge after left anterior temporal lesion : a convergent effect. Cortex, 65 : 1-18.
  • Kheradpisheh SR, Ghodrati M, Ganjtabesh M, and Masquelier T. (2016). Humans and deep networks largely agree on which kinds of variation make object recognition harder. Frontiers in Computational Neuroscience, 10(92).
  • Kheradpisheh SR, Ganjtabesh M, and Masquelier T. Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition. Neurocomputing, in press.
  • Masquelier T, Portelli G, and Kornprobst P. (2016). Microsaccades enable efficient synchrony-based coding in the retina : a simulation study. Scientific Reports, 6(24086).
  • Reddy L, Poncet M, Self MW, Peters JC, Douw L, van Dellen E, Claus S, Reijneveld JC, Baayen JC, Roelfsema PR. (2015). Learning of anticipatory responses in single neurons of the human medial temporal lobe. Nature Communication, 6:8556.
  • Reddy L, Thorpe SJ. (2014). Concept cells through associative learning of high-level representations. Neuron, 84(2):248-51.
  • Rémy F, Vayssière N, Saint-Aubert L, Barbeau E, Pariente J. (2015). White matter disruption at the prodromal stage of Alzheimer’s disease : relationships with hippocampal atrophy and episodic memory performance. Neuroimage Clinical 7:482-492.
  • Rémy F, Vayssière N, Pins D, Boucart M, Fabre-Thorpe M. (2014). Incongruent object/context relationships in visual scenes : where are they processed in the brain ? Brain and Cognition, 84 (1) 34-43.
  • Senoussi M, Berry I, VanRullen R, Reddy L. (2016). Multivoxel object representations in adult human visual cortex are flexible : an associative learning study. Journal of Cognitive Neuroscience, 28(6):852-68.
  • Voltzenlogel V, Hirsch E, Vignal JP, Valton L, Manning L. (2015). Preserved anterograde and remote memory in drug-responsive temporal lobe epileptic patients. Epilepsy Research, 115:126-32.

Mise à jour 15/12/2016