Current Research Projects (selected)
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Algorithms of the Brain Linking Mechanisms and Dynamics Underlying Network Computation
Algorithms of the Brain Linking Mechanisms and Dynamics Underlying Network Computation
Understanding brain function requires more than mapping neurons—it demands uncovering how networks compute and adapt. This project integrates computational models with advanced experimental neuroscience to reveal how neural ensembles drive perception, thought, and action. By bridging theory and practice, we aim to clarify the brain’s computational logic, advancing both neuroscience and AI. This work could lead to better treatments for brain disorders and inform new approaches in education and human-machine interaction, benefiting society through deeper insight into how we think, learn, and adapt.
Early Diagnosis of Cognitive Impairments
Early Diagnosis of Cognitive Impairments
This project develops an early diagnostic tool for cognitive decline—such as in Alzheimer’s, amongst other diseases—by combining sensitive cognitive tests with high-density EEG to monitor neocortical activity. The aim is to identify early brain markers that enable timely intervention. The work supports earlier diagnosis and treatment, improving patient outcomes and easing healthcare burdens. The aim is to advance the scientific understanding of the neural basis of cognitive decline, informing both clinical and research efforts
Past Research Projects and Collaborations with other Labs (selected)
Cognitive Maps in Neuro-Robotics and AI
Cognitive Maps in Neuro-Robotics and AI
Fernandez-Leon, J.A.; Uysal, A.K.; Ji, Daoyun (2022) Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model. Scientific Reports-Nature 12(1), 21443. doi: 10.1038/s41598-022-25863-2
Fernandez-Leon, J.A.; Acosta, G. (2022) Uncovering the Secrets of the Concept of Place in Cognitive Maps Aided by Artificial Intelligence. Cognitive Computation. doi: 10.1007/s12559-022-10064-w
Fernandez-Leon, J.A.; Acosta, G. (2021) Challenges for neuroscience-based computational intelligence. International Journal of Computational Intelligence Studies. International Journal of Computational Intelligence Studies 10(4), pp.232-238. doi: 10.1504/IJCISTUDIES.2021.1004448
Fernandez-Leon, J. A. & Sarramone, L. Grid cell modules coordination overcomes self-localization deviations. Cognit. Comput. Submitted (2022)
Fernandez-Leon, J. A., Sarramone, L. & Todorovich, E. Intrinsic Coordination of Grid Cell Modules for Position Estimation. Submitted ALIFE 2023 (2023).
Sarramone, L., Todorovich, E. & Fernandez-Leon, J. A. Control neuro-inspirado para navegación espacial entre neurociencia e inteligencia computacional. in 2021 CONAIISI (ed. Mendoza, U. T. N.-F. R.) (2021).
Robust yet Adaptable Behaviours in Cognitive Neuro-Robotics
Robust yet Adaptable Behaviours in Cognitive Neuro-Robotics
Fernandez-Leon, J.A.; Acosta, G; Rozenfeld, A. (2014) How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune system fields. BioSystems 124: 7–20. doi: 10.1016/j.biosystems.2014.08.003
Fernandez-Leon, J.A.; Acosta, G.; Mayosky, M.; Calvo Ibáñez, O. (2009). A Biologically Inspired Control based on Behavioural Coordination in Evolutionary Robotics, Chapter VII in Advancing Artificial Intelligence through Biological Process Applications, pp. 107-129, IGI-Global (Idea Group Inc.), USA-UK. Editors: Dr. A. B. Porto, Dr. A. Pazos, and Dr. W. Buño, 2009. (ISBN: 978-1- 59904-996-0).
Fernandez-Leon, J.A.; Acosta, G.; Mayosky, M. (2009). Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation. Robotics & Autonomous Systems 57(4): 411-419. doi: 10.1016/j.robot.2008.06.012
Principles of Neural Coding in Computational Neuroscience
Principles of Neural Coding in Computational Neuroscience
Fernandez-Leon, J.A. (2022) Does the free energy principle sleep on it?: Comment on "How particular is the physics of the free energy principle?" by Miguel Aguilera et al. Phys Life Rev. 43:1-3. doi: 10.1016/j.plrev.2022.07.004.
Fernandez-Leon, J.A.; Acosta, G. (2021) A heuristic perspective on non-variational free energy modulation at the sleep-like edge. BioSystems 208, 104466. doi: 10.1016/j.biosystems.2021.104466
Fernandez-Leon, J.A. (2014) Robustness as a non-localizable relational phenomenon. Biological Reviews 89(3): 552–567. doi: 10.1111/brv.12067
Algorithms of the Brain in Systems Neuroscience
Algorithms of the Brain in Systems Neuroscience
Ash, RT; Palagina, G.*; Fernandez-Leon, J.A.*; Park, J*; Seilheimer, R; Lee, S; Sabharwal, J; Reyes, F; Wang, J; Lu, D; Sarfraz, M; Froudarakis, E; Tolias, AS; Wu, SM; Smirnakis, SM. (2022) Increased Reliability of Visually-Evoked Activity in Area V1 of the MECP2-Duplication Mouse Model of Autism. J Neurosci. 42 (33) 6469-6482. doi: 10.1523/JNEUROSCI.0654-22.2022. *denotes equal contribution.
Fernandez-Leon, J.A.*; Engelke, DS.*; Aquino-Miranda, G*; Goodson, A; Do Monte, FH. (2021) Neural correlates and determinants of approach-avoidance conflict in the prelimbic prefrontal cortex. eLife 10. doi: http://dx.doi.org/10.7554/eLife.74950 (available in bioRxivs 2021.05.27.445881). *denotes equal contribution.
Engelke, D.; Zhang, X.; O’Malley, J.; Fernandez-Leon, J.A.; Li, S.; Kirouac, G.; Beierlein, M.; Do-Monte, F. (2021) A Hypothalamic-thalamostriatal circuit that controls approach-avoidance conflict in rats. Nature Communications-Nature. 12(1):2517. doi: 0.1038/s41467-021-22730-y
Fernandez-Leon, J.A.; Hansen, B.; Dragoi, V. (2018) Representation of Rapid Image Sequences in V4 Networks. Cerebral Cortex, 28(8): 2675–2684. doi: 10.1093/cercor/bhx146
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