Connectionists: Fully funded PhD position in the fields of neuro-inspired robotics, CY Cergy-Paris University, France

[Alexandre PITTI]

The Neurocybernetic team of ETIS Lab (CNRS, CY Cergy-Paris University, ENSEA) is seeking applicants for a fully funded PhD place providing an exciting opportunity to pursue a postgraduate research in the fields of bio/neuro-inspired robotics, ethology, neuroscience. 

This PhD is funded by the French ANR, under a 4 years' project on Sensorimotor integration of variability during birdsong learning. 

Motor variability, by allowing the exploration of the motor space, is an essential component of how sensorimotor circuits change across the learning course and may adapt to different conditions. Yet, knowledge remains sparse on how the variability of the input model contributes to the efficiency of sensorimotor integration during both speech acquisition and birdsong learning. 
This proposal will model the learning processes at play to eventually feed neurocomputational models of speech learning. 

The applicant will develop an artificial neural model, developmental and brain-inspired, to learn the sound structure in real time and without explicit supervision. Until now, AI models for developmental learning of vocalizations have been solely validated by comparison against a human-annotated corpus and not yet via direct sensorimotor interactions with living animals. We expect to do so with an interactive robot under the framework of active inference and predictive coding. 

some references: 

[1] Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization 
Pitti A, Quoy M, Boucenna S, Lavandier C (2021) Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization. PLOS Computational Biology 17(2): e1008566. [ https://doi.org/10.1371/journal.pcbi.1008566 | https://doi.org/10.1371/journal.pcbi.1008566 ] 

[2] Alexandre Pitti, Mathias Quoy, Catherine Lavandier, Sofiane Boucenna, 
Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE), 
Neural Networks, Volume 121, 2020, Pages 242-258, [ https://doi.org/10.1016/j.neunet.2019.09.023 | https://doi.org/10.1016/j.neunet.2019.09.023 ] . 

[3] Annabi, L., Pitti, A., & Quoy, M. (2021). Bidirectional interaction between visual and motor generative models using predictive coding and active inference. Neural Networks , 143 , 638-656. 

The PhD lasts for 3 years and includes a small teaching component. 

deadline: till found 

Duration: 
January 2024-December 2027 

To apply, send us an email first 
Please include: 
1. A statement of research interests. 
2. A detailed CV. 
3. A transcript of your diplomas. 
4. Your Master/Diploma thesis, and any draft or published papers. 

Contact: 
Prof Alex Pitti alexandre.pitti at cyu.fr 
& Prof assistant Sofiane Boucenna sofiane.boucenna at cyu.fr 



Alex Pitti 
team NeuroCybernetics 
Professor 

Laboratoire ETIS, CY Cergy Paris Université, ENSEA, CNRS, UMR8051 
[ https://www.etis-lab.fr/neuro | https://www.etis-lab.fr/neuro ] 
[ https://sites.google.com/view/alexpitti/home/ | https://sites.google.com/view/alexpitti/home/ ] 

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