Niceto Luque

Niceto Luque

Associate Professor

University of Granada

Short bio

I received my B.S in Electronics Engineering and a 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. In 2015, I obtained an IF Marie Curie Post-Doc Fellowship from the EU Commission, I moved to Dr. Arleo’s lab in Paris to work in cerebellar ageing. Finally, I obtained in 2018 a Juan de la Cierva Incorporation Post-Doc Fellowship from the Spanish Government, I moved to Prof. Ros’ lab to study cerebellar adaptation and its involvements in ageing.

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.

You can find more information about my professional career in my personal website.

  • Cerebellum
  • Learning
  • Ageing
  • Motor control
  • PhD in Control Engineering and Computer Science, 2013

    University of Granada

  • MSc in Computer Engineering and Networks, 2008

    University of Granada

  • MSc in Automatics and Industrial Electronics, 2006

    University of Cordoba

  • BSc in Electronics Engineering, 2003

    University of Cordoba