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Administrative & technical support
Position
Informations pratiques
LNC2
Laboratoire:

LNC2

Équipe
Human reinforcement learning
Responsable
Stefano Palminteri
Date de limite de candidature
23 juin 2019

Position available: research assistant

Duration: initial contract 6 months (extension: 6 months)

Starting date: September (not negotiable)

Laboratory: Human Reinforcement Learning team
(https://lnc2.dec.ens.fr/en/human-reinforcement-learning)

Supervisor: Stefano Palminteri
(https://sites.google.com/site/stefanopalminteri/home)

Prerequisite education:

Master or similar in fields related to cognitive science (computer science, psychology, economics, neuroscience)

Requested ability:

  • Very good programming proficiency

  • Capacity and willingness to work in group

  • Very good understanding of written and spoken english, good production in English

  • Solid interpersonal skills to develop and maintain effective and cooperative working relationships with a multi-diverse community.

Job description
This job involves conducting and/or supporting laboratory research, which may include routine or non-routine laboratory operations, data interpretation, development and performance of technical protocols and procedures, design of cognitive experiments, literature studies, preparation of scientific papers. The RA will be mainly focused on the development and analysis of online cognitive experiments (languages likely to be used:  Matlab, R, Python, Java, HTLM).

It also involves coordinating people involved in lab research (post-doc, graduate students and undergraduate research assistants), organizing lab meetings and invited speakers’ visits, and helping with other administrative or social needs of the lab.

Research and administrative duties will include but is not limited to organizing lab meetings and other occasional lab social events. Provide assistance with training undergraduate research assistants on laboratory tasks and techniques such as (but is not limited to) online experiments and data analyses.  Collaboratively write and/or edit IRB protocols, grants, and other scientific reports as needed. May contribute towards articles for publication in scientific journals or for presentations at conferences or symposiums. Consults with academic supervisors or professional researchers on the nature and objectives of the research project or instructional design goals.

Application
Detailed CV (with grades), Letter of motivation (why this job? What after this job?), Name and email of two referees

Timeline
Deadline for application  (to be sent to stefano dot palminter at gmail dot com) submission: 23/06/19

https://docs.google.com/document/d/1k18-w16ka_zf01KRmXsA10EkpyKTrS7d4FhHH91L1kg/edit?fbclid=IwAR20d9dfG3WY_iQoUsNZD65qU121B7T8jEUXcmZ4x_UCKLBQJMXWaLaIfpU

Personnel administratif et technique
Poste
Informations pratiques
LNC2
Laboratoire:

LNC2

Équipe
Human reinforcement learning
Responsable
Stefano Palminteri
Date de limite de candidature
23 juin 2019

Position available: research assistant

Duration: initial contract 6 months (extension: 6 months)

Starting date: September (not negotiable)

Laboratory: Human Reinforcement Learning team
(https://lnc2.dec.ens.fr/en/human-reinforcement-learning)

Supervisor: Stefano Palminteri
(https://sites.google.com/site/stefanopalminteri/home)

Prerequisite education:

Master or similar in fields related to cognitive science (computer science, psychology, economics, neuroscience)

Requested ability:

  • Very good programming proficiency

  • Capacity and willingness to work in group

  • Very good understanding of written and spoken english, good production in English

  • Solid interpersonal skills to develop and maintain effective and cooperative working relationships with a multi-diverse community.

Job description
This job involves conducting and/or supporting laboratory research, which may include routine or non-routine laboratory operations, data interpretation, development and performance of technical protocols and procedures, design of cognitive experiments, literature studies, preparation of scientific papers. The RA will be mainly focused on the development and analysis of online cognitive experiments (languages likely to be used:  Matlab, R, Python, Java, HTLM).

It also involves coordinating people involved in lab research (post-doc, graduate students and undergraduate research assistants), organizing lab meetings and invited speakers’ visits, and helping with other administrative or social needs of the lab.

Research and administrative duties will include but is not limited to organizing lab meetings and other occasional lab social events. Provide assistance with training undergraduate research assistants on laboratory tasks and techniques such as (but is not limited to) online experiments and data analyses.  Collaboratively write and/or edit IRB protocols, grants, and other scientific reports as needed. May contribute towards articles for publication in scientific journals or for presentations at conferences or symposiums. Consults with academic supervisors or professional researchers on the nature and objectives of the research project or instructional design goals.

Application
Detailed CV (with grades), Letter of motivation (why this job? What after this job?), Name and email of two referees

Timeline
Deadline for application  (to be sent to stefano dot palminter at gmail dot com) submission: 23/06/19

https://docs.google.com/document/d/1k18-w16ka_zf01KRmXsA10EkpyKTrS7d4FhHH91L1kg/edit?fbclid=IwAR20d9dfG3WY_iQoUsNZD65qU121B7T8jEUXcmZ4x_UCKLBQJMXWaLaIfpU

M2
Stage
Informations pratiques
LNC2
Laboratoire:

LNC2

Équipe
Mathematics of Neural Circuits
Responsable
Boris Gutkin
Durée du stage
5-6 months
Langue
French and/or English

Alzheimer’s disease (AD) is characterized by progressive cognitive deterioration from early mild cognitive impairment (MCI)) to dementia. The multi-scale causes of such pathologies, from the subcellular to the brain level, remain elusive to experimental approaches alone. The masters project is geared towards developing new models of cell-specific pathologies associated with AD and examining their effects at teh population and functional level. Specifically the models developed will identify AD-linked beta amyloid (Ba) role in neural activity alterations and cognitive impairments. The models, constrained by state-of-the-art data, will relate abnormalities at the receptor and cellular level with deficits in cognitive functions. We will focus on data-based modeling of early AD when low levels of Ba interact with elements of the neuronal circuit (data from collaborator labs). We will examine the Ba action through nicotinic acetylcholine receptors (NNRs) on brain activity seen in AD/MCI: notably the hyperfrontality and perturbations of the theta rhythm.

The intern taking part in this project will work on one of the two aspects to specifically delineate the role of Ba action on NNRs of multiple subtypes (homomeric alpha7, heteromeric alpha7, beta2, alpha 5) in 1. the progressive onset of fronto-cortical hyperactivity or 2. the role of Ba-a7 action on failure of theta rhythm in the hippocampus. This they will be able to participate in one of two specific Aims:

Aim1: Guided by cutting edge data from Maskos lab (Pasteur Institute), we will build on our earlier developed models of NNR action in PFC circuitry to discover precise mechanisms by which soluble Ba acts on the NNR expressed on inhibitory circuitry to provoke hyperactivity. 
 

Aim2: Guided by data from Yakel lab (NIEHS) our framework will extend to a mesoscopic spiking circuit model of theta induction in the hippocampus and Ba-a7 induced dynamics pathology. 

Aim3: We will use these to model key cognitive tasks (e.g. working memory) and identify links between Ba-NNR pathways and MCI. We will profile potential nicotinic agents for functional amelioration. 

Intern will receive training in computational modelling, analysis and the neurobiology of AD. Intern will take part in the scientific life of the GNT: seminars, lectures, presentations.

M2
Internship
Informations pratiques
LNC2
Laboratoire:

LNC2

Équipe
Mathematics of Neural Circuits
Responsable
Boris Gutkin
Durée du stage
5-6 months
Langue
French and/or English

Alzheimer’s disease (AD) is characterized by progressive cognitive deterioration from early mild cognitive impairment (MCI)) to dementia. The multi-scale causes of such pathologies, from the subcellular to the brain level, remain elusive to experimental approaches alone. The masters project is geared towards developing new models of cell-specific pathologies associated with AD and examining their effects at teh population and functional level. Specifically the models developed will identify AD-linked beta amyloid (Ba) role in neural activity alterations and cognitive impairments. The models, constrained by state-of-the-art data, will relate abnormalities at the receptor and cellular level with deficits in cognitive functions. We will focus on data-based modeling of early AD when low levels of Ba interact with elements of the neuronal circuit (data from collaborator labs). We will examine the Ba action through nicotinic acetylcholine receptors (NNRs) on brain activity seen in AD/MCI: notably the hyperfrontality and perturbations of the theta rhythm.

The intern taking part in this project will work on one of the two aspects to specifically delineate the role of Ba action on NNRs of multiple subtypes (homomeric alpha7, heteromeric alpha7, beta2, alpha 5) in 1. the progressive onset of fronto-cortical hyperactivity or 2. the role of Ba-a7 action on failure of theta rhythm in the hippocampus. This they will be able to participate in one of two specific Aims:

Aim1: Guided by cutting edge data from Maskos lab (Pasteur Institute), we will build on our earlier developed models of NNR action in PFC circuitry to discover precise mechanisms by which soluble Ba acts on the NNR expressed on inhibitory circuitry to provoke hyperactivity. 
 

Aim2: Guided by data from Yakel lab (NIEHS) our framework will extend to a mesoscopic spiking circuit model of theta induction in the hippocampus and Ba-a7 induced dynamics pathology. 

Aim3: We will use these to model key cognitive tasks (e.g. working memory) and identify links between Ba-NNR pathways and MCI. We will profile potential nicotinic agents for functional amelioration. 

Intern will receive training in computational modelling, analysis and the neurobiology of AD. Intern will take part in the scientific life of the GNT: seminars, lectures, presentations.

Article dans une revue internationale  

Lefebvre, G., Nioche, A., Bourgeois-Gironde, S. & Palminteri, S. (2018). Contrasting temporal difference and opportunity cost reinforcement learning in an empirical money-emergence paradigm. PNAS, 115(49), E11446-E11454. doi:10.1073/pnas.1813197115

Article dans une revue internationale  

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2018). A bright future for financial agent-based models. arxiv, nc

Article dans une revue internationale  

Novikov, N. & Gutkin, B. (2018). and Gamma Oscillations in Working Memory Functions. Psychology. Journal of Higher School of Economics, 15(1), 174-182

Article dans une revue internationale  

Palminteri, S. & Chevallier, C. (2018). Can We Infer Inter-Individual Differences in Risk-Taking From Behavioral Tasks? Frontiers in psychology, 9, 2307. doi:10.3389/fpsyg.2018.02307

Article dans une revue internationale  

Volk, D., Dubinin, I., Gutkin, B., Myasnikova, A. & Nikulin, V. (2018). Cross-Frequency Synchrony Analysis. Frontiers in Neuroinformatics , 12, 72. doi:10.3389/fninf.2018.00072

Article dans une revue internationale  

Zakharov, D., Krupa, M., Gutkin, B. & Kuznetsov, A. (2018). High-frequency forced oscillations in neuronlike elements. Physical Review E , 97(6). doi:10.1103/PhysRevE.97.062211

Article dans une revue internationale  

Bobashev, G., Holloway, J., Solano, E. & Gutkin, B. (2017). A Control Theory Model of Smoking. Methods report (RTI Press), 10. doi:10.3768/rtipress.2017.op.0040.1706

Article dans une revue internationale  

Chalk, M., Masset, P., Denève, S. & Gutkin, B. (2017). Sensory noise predicts divisive reshaping of receptive fields. PLoS computational biology, 13(6), e1005582. doi:10.1371/journal.pcbi.1005582

Article dans une revue internationale  

Dumont, G., Ermentrout, G. & Gutkin, B. (2017). Macroscopic phase-resetting curves for spiking neural networks. Physical review. E, 96, 042311. doi:10.1103/PhysRevE.96.042311

Article dans une revue internationale  

Dumont, G., Payeur , A. & Longtin , A. (2017). A stochastic-field description of finite-size spiking neural networks. PLoS Comput Biol, 13(8), e1005691. doi:10.1371/journal.pcbi.1005691

Article dans une revue internationale  

Hertz, U., Palminteri, S., Brunetti, S., Olesen, C., Frith, C. & Bahrami, B. (2017). Neural computations underpinning the strategic management of influence in advice giving. Nature communications, 8(1), 2191. doi:10.1038/s41467-017-02314-5

Article dans une revue internationale  

Keramati, M., Ahmed, S. & Gutkin, B. (2017). Misdeed of the need: towards computational accounts of transition to addiction. Current opinion in neurobiology, 46, 142-153. doi:10.1016/j.conb.2017.08.014

Article dans une revue internationale  

Keramati, M., Durand, A., Girardeau, P., Gutkin, B. & Ahmed, S. (2017). Cocaine addiction as a homeostatic reinforcement learning disorder. Psychological review, 124(2), 130-153. doi:10.1037/rev0000046

Article dans une revue internationale  

Koukouli, F., Rooy, M., Tziotis, D., Sailor, K., O'Neill, H., Levenga, J., Witte, M., Nilges, M., Changeux, J., Hoeffer, C., Stitzel, J., Gutkin, B., Digregorio, D. & Maskos, U. (2017). Nicotine reverses hypofrontality in animal models of addiction and schizophrenia. Nature medicine, 23(3), 347-354. doi:10.1038/nm.4274

Article dans une revue internationale  

Lefebvre, G., Lebreton, M., Meyniel, F., Bourgeois-Gironde, S. & Palminteri, S. (2017). Behavioural and neural characterization of optimistic reinforcement learning. Nature Human Behaviour , 1(4), 0067

Article dans une revue internationale  

Maex, R. & Gutkin, B. (2017). Temporal integration and 1/f power scaling in a circuit model of cerebellar interneurons. Journal of neurophysiology, 118(1), 471-485. doi:10.1152/jn.00789.2016