Dr. Niceto R. Luque Sola (Postdoc Marie Skłodowska-Curie)

Vision Institute
Aging in Vision and Action Lab
CNRS – INSERM – University Pierre&Marie Curie
17, rue Moreau F-75012 Paris, France
Phone: +33 (0)1 53 46 25 61



Research interests

Experimental studies about the Central Nervous System (CNS) in all levels (sub cellular, cellular and at system level) are performed in order to obtain a better understanding of its anatomic structures and the physiological processes that the CNS seems to possess. Nevertheless, the observations to be done with that aim must be managed within a representative scenario where the functional description of the CNS is available. This is possible just in case when all the needed conceptual elements that properly describe the CNS functionality are available too. Both Physiologists and Neurophysiologists have traditionally used the performance (or the lack of performance in presence of pathologies), as the basis for the functional assessment of the CNS components, thus producing useful qualitative and phenomenal models. Although these models are often, more than enough for clinical issues they do not provide a detailed comprehension of the whole CNS.

The current technology allows a restricted in vivo access to the CNS (mainly to the more external areas) by means of functional magnetic resonance imaging and magnetoencephalography. Similarly, it is of common use, recordings by means of electrode matrices; however, these recordings just allow extracellular access of barely a hundred neurons at best.

Nevertheless, most of the functional neural networks related to the hippocampus and the cerebellum (two of the best-known regions) are sized from just a hundred of thousand to several millions of cells. The information process within these neural networks occurs thanks to the self-organized dynamic patterns of the neural activity that covers a large proportion of the nervous system. These emerging patterns can be hardly understood taking into account just individual activities of individual cells (or even hundreds of cells) in the same way that it is tough to understand a book just taken into account individual words. Even the data collected from very large-scale studies do not present the necessary resolution for observing these patterns and making the corresponding cellular interaction matches.

The biologically plausible computational models (cerebellum, inferior olive nucleus, cuneate nucleus …etc) give answer to this problem allowing the study of neural network models ” as large as it is needed” using neuronal models that have been developed according to experimental cellular data. These neural network models can be computationally simulated in pretty different conditions and circumstances to give a pretty consistent idea about how the CNS neural networks may operate. In many cases, these models are becoming a fundamental tool in the neuroscience hypothesis-experimentation cycle. The computational models allow researchers to test their “what’s up when …?” in simulation. This fact leads to make better hypothesis and better experiments designed with greater probability of success.

From this perspective, and thanks to the exponential computational performance evolution, the computational neuroscience has positioned over the last years as a promising sub-field in neuroscience. The computational neuroscience must not be considered as just a tool to better understand the behaviour of a functional neural network within the CNS by using a mathematical analysis and massive computational simulations but also as a fundamental element to determine 1) what the different parts of the CNS do 2) and how these different parts do what they do.

In such scenario I have been developing my research during these years in the framework of three European projects (SENSOPAC, REALNET and HBP ) helping to develop different models of diverse nervous system elements(cerebellum, inferior olive nucleus and cuneate nucleus) in cooperation with different research groups from neurophysiology trough computational neurobiology to robotics. My main research interest aims at better understanding the functional involvement of the cerebellar spiking nervous sub-circuits embedded in biological plausible control loops as a whole.

Short bio:

I received my B.S in Electronics Engineering and an M.S. in Automatics and Industrial Electronics from the University of Cordoba (Spain) in 2003 and 2006, respectively. In April 2007 I officially joined to the University of Granada with a National Grant as a researcher of the European Project SENSOPAC .I also received my M.S. in Computer Architecture and Networks from the University of Granada in 2007. Finally, I received my Doctorate from the University of Granada in 2013 in Control Engineering and Computer Science.

From 2012 to 2014 I participated in an EU project related to adaptive learning mechanisms and bio-inspired control REALNET. In August 2014, I officially joined the Human Brain Project (HBP); a ten-year, large-scale European research initiative whose goal is to better understand the human brain and its diseases and ultimately to emulate its computational capabilities. Finally, in 2015 I obtained an IF Marie Curie Post-Doc Fellowship from the EU Commission.


Biologically processing control schemes, lightweight robots, cerebellar spiking neural networks, cerebellar plasticity

CV and Publication List (pdf): [ download ]


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Show All - Journals - Peer-reviewed conference proceedings - Others


  1. Naveros F, Luque NR, Ros E and Arleo A (2019) VOR Adapta9on on a Humanoid iCub Robot using a Spiking Cerebellar Model. IEEE T Cybernetics, (in press).
  2. Luque NR, Naveros F, Carrillo RR, Ros E and Arleo A (2019) Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation. PLoS Comput. Biol., (in press).


  1. Luque NR, Naveros F, Carrillo RR, Ros E and Arleo A (2018) Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation. bioRxiv:347252.
  2. Carrillo RR, Naveros F, Ros E and Luque NR (2018) A Metric for Evaluating Neural Input Representation in Supervised Learning Networks. Frontiers in neuroscience, 12.
  3. Naveros F, Garrido JA, Arleo A, Ros E and Luque NR (2018) Exploring Vestibulo-Ocular Adaptation in a Closed-Loop Neuro-Robotic Experiment Using STDP. A Simulation Study. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1-9.


  1. Naveros F, Garrido J, Carrillo R, Ros E and Luque N (2017) EDLUT: a real-time spiking neural network simulator for embodiment experiments. In CNS 2017, Antwerp, Belgium.
  2. Luque N, Naveros F, Carrillo R, Ros E and Arleo A (2017) Silent and bursting states of Purkinje cell activity modulate VOR adaptation. In CNS 2017, Antwerp, Belgium.
  3. Naveros F, Garrido JA, Carrillo RR, Ros E and Luque NR (2017) Event-and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks. Frontiers in Neuroinformatics, 11.


  1. Luque NR, Garrido JA, Naveros F, Carrillo RR, D'Angelo E and Ros E (2016) Distributed cerebellar motor learning: a spike-timing-dependent plasticity model. Frontiers in Computational Neuroscience, 10.
  2. Garrido JA, Luque NR, Tolu S and DíAngelo E (2016) Oscillation-driven spike-timing dependent plasticity allows multiple overlapping pattern recognition in inhibitory interneuron networks. International Journal of Neural Systems, 26(05):1650020.
  3. D'Angelo E, Antonietti A, Casali S, Casellato C, Garrido JA, Luque NR, Mapelli L, Masoli S, Pedrocchi A, Prestori F and thers (2016) Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue. Frontiers in Cellular Neuroscience, 10.


  1. Antonietti A, Casellato C, Garrido J, Luque N, Naveros F, Ros E, D Angelo E and Pedrocchi A (2015) Spiking Neural Network with Distributed Plasticity Reproduces Cerebellar Learning in Eye Blink Conditioning Paradigms. IEEE transactions on bio-medical engineering.
  2. Naveros F, Luque NR, Garrido JA, Carrillo RR, Anguita M and Ros E (2015) A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study. Neural Networks and Learning Systems, IEEE Transactions on, 26(7):1567-1574.
  3. D’Angelo E, Mapelli L, Casellato C, Garrido JA, Luque N, Monaco J, Prestori F, Pedrocchi A and Ros E (2015) Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning. The Cerebellum:1-13.


  1. Casellato C, Antonietti A, Garrido JA, Carrillo RR, Luque NR, Ros E, Pedrocchi A and D'Angelo E (2014) Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network. PLoS ONE, 9(11):e112265.
  2. Luque NR, Garrido JA, Carrillo RR, D'Angelo E and Ros E (2014) Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation. Frontiers in computational neuroscience, 8.
  3. Luque NR, Carrillo RR, Naveros F, Garrido JA and Sáez-Lara MJ (2014) Integrated neural and robotic simulations. Simulation of cerebellar neurobiological substrate for an object-oriented dynamic model abstraction process. Robotics and Autonomous Systems, 62(12):1702-1716.


  1. Tolu S, Vanegas M, Garrido JA, Luque NR and Ros E (2013) Adaptive and predictive control of a simulated robot arm. International journal of neural systems, 23(03):1350010.
  2. Naveros F, Luque NR, Garrido JA, Carrillo RR and Ros E (2013) CPU-GPU hybrid platform for efficient spiking neural-network simulation. In BMC Neuroscience, vol. 14, pages P328.
  3. Luque NR, Garrido JA, Carrillo RR and Ros E (2013) Connection control implications in a distributed plasticity cerebellar model. In BMC Neuroscience, vol. 14, pages P329.
  5. Garrido JA, Luque NR, D'Angelo E and Ros E (2013) Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation. Frontiers in neural circuits, 7.
  6. Passot J-B, Luque NR and Arleo A (2013) Coupling internal cerebellar models enhances online adaptation and supports offline consolidation in sensorimotor tasks. Frontiers in Computational Neuroscience, 7:95.


  1. Casellato C, Pedrocchi A, Garrido JA, Luque NR, Ferrigno G, Angelo ED and Ros E (2012) An integrated motor control loop of a human-like robotic arm: feedforward, feedback and cerebellum-based learning. In Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference on, pages 562-567.
  2. Tolu S, Vanegas M, Luque NR, Garrido JA and Ros E (2012) Bio-inspired adaptive feedback error learning architecture for motor control. Biological cybernetics, 106(8-9):507-522.
  3. Luque NR, Garrido JA, Ralli J, Laredo JJ and Ros E (2012) From sensors to spikes: evolving receptive fields to enhance sensorimotor information in a robot-arm. International Journal of Neural Systems, 22(04).


  1. Luque NR, Garrido JA, Carrillo RR, Tolu S and Ros E (2011) Adaptive cerebellar spiking model embedded in the control loop: context switching and robustness against noise. International Journal of Neural Systems, 21(05):385-401.
  2. Luque NR, Garrido J, Carrillo RR, Coenen OJ, Ros E and thers (2011) Cerebellar input configuration toward object model abstraction in manipulation tasks. Neural Networks, IEEE Transactions on, 22(8):1321-1328.
  3. Luque NR, Garrido JA, Carrillo RR, Coenen OJ and Ros E (2011) Cerebellarlike corrective model inference engine for manipulation tasks. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 41(5):1299-1312.
  4. Garrido JA, Carrillo RR, Luque NR and Ros E (2011) Event and time driven hybrid simulation of spiking neural networks. In Advances in Computational Intelligence, pages 554-561, Springer Berlin Heidelberg.
  5. Luque NR, Garrido JA, Carrillo RR and Ros E (2011) Context separability mediated by the granular layer in a spiking cerebellum model for robot control. In Advances in Computational Intelligence, pages 537-546, Springer Berlin Heidelberg.


  1. Luque NR, Garrido JA, Carrillo RR and Ros E (2010) Cerebellar spiking engine: Towards objet model abstraction in manipulation. In Neural Networks (IJCNN), The 2010 International Joint Conference on, pages 1-8.
  2. Passot J-B, Luque N and Arleo A (2010) Internal models in the cerebellum: a coupling scheme for online and offline learning in procedural tasks. In Doncieux, S. et al., editors, LNAI - Simulation of Adaptive Behavior, vol. 6226, pages 435-446, Springer-Verlag.