Brief description

Cognition, as a subjective understanding of the external world, is the primary functional ability defining the brain. To act in complex situations, humans use mental models. These models are built over an Internal Representation (IR) of the environment and the body. Biophysical principles and mechanisms of IR are mostly unknown. Our team has proposed an approach based on the hypothesis that the IR emerges from the nonlinear dynamics of a system composed of a neural network that predicts changes in the environment and a reaction-diffusion lattice modeling all actions available to the subject. This system “compacts” the time dimension of situations. Then the decision-making process is reduced to a movement to an attractor in the phase space. Within the proposed Research Program, we aim at the following goals. 1) Develop mathematical models describing the interaction of cognitive agents. 2) Test the models on robots in human environments. 3) Study the functional mechanisms of behavioral memory. The development of the program will lead to a qualitative leap in understanding cognition and bring us closer to the development of a new generation of robots.

 

Researchers

• Valeriy Makarov Slizneva (Profesional, Facultad de Matemáticas, UCM)
  
• Carlos Calvo Tapia (Profesional, Facultad de Matemáticas, UCM)

 

External Collaborators

• Abel Sánchez Jiménez (Profesional, Facultad de Biología, UCM)
• Sergio Díez Hermano (Profesional, Facultad de Biología, UCM)
• Sergey Lobov.  Nizhny Novgorod State University (Russia) 

 

Publications

• O. Herreras, D. Torres, V. A. Makarov, J. Makarova. Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent. Frontiers in Cellular Neuroscience. 2023, 17. https://doi.org/10.3389/fncel.2023.1129097
• A. Lobov, A. Mikhaylov, E. S. Berdnikova, V. Makarov, V. B. Kazantsev. Spatial Computing in Modular Spiking Neural Networks with a Robotic Embodiment. Mathematics,  2023, 234, 11(1). https://doi.org/10.3390/math11010234
• V. A. Makarov, S. A. Lobov, S. Shchanikov, A. Mikhaylov, V. B. Kazantsev. Toward Reflective Spiking Neural Networks Exploiting Memristive Devices. Frontiers in Computational Neuroscience, 2022, 16, 859874. https://doi.org/10.3389/fncom.2022.859874
• O. Herreras, D. Torres, G. Martín-Vázquez, S. Hernández-Recio, V. J. López-Madrona, N. Benito, V. A Makarov, J. Makarova. Site-dependent shaping of field potential waveforms, Cerebral Cortex, 2022. https://doi.org/10.1093/cercor/bhac297
• S. A. Lobov, N. P. Krilova, V. A. Makarov, D. P. Kurganov, J. Makarova. Arcade game testing of generalized cognitive maps in humans. 2021 Third International Conference Neurotechnologies and Neurointerfaces (CNN). 2021, 61-63. https://doi.org/10.1109/CNN53494.2021.9580220
• S. Shchanikov, I. Bordanov, A. Belov, D. Korolev, M. Shamshin, E. Gryaznov, V. Kazantsev, A. Mikhaylov, V. A. Makarov. Memristive Concept of a High-Dimensional Neuron. 2021 Third International Conference Neurotechnologies and Neurointerfaces (CNN). 2021, 96-99. https://doi.org/10.1109/CNN53494.2021.9580310
• A. E. Hramov, A. A. Koronovskii, V. A. Makarov, V. A. Maksimenko, A. N. Pavlov, E. Sitnikova. Wavelets in Neuroscience. Springer Series in Synergetics. 2021. https://doi.org/10.1007/978-3-030-75992-6. Hardcover ISBN: 978-3-030-75991-9, Softcover ISBN: 978-3-030-75994-0, eBook ISBN: 978-3-030-75992-6.
  
• S. A. Lobov, A. I. Zharinov, V. A. Makarov, V. B. Kazantsev. Spatial memory in a spiking neural network with robot embodiment. Sensors. 2021, 21, 8, Article number 2678. https://doi.org/10.3390/s21082678
• J. A. Villacorta-Atienza, C. Calvo Tapia, S. Díez-Hermano, A. Sánchez-Jiménez, S. Lobov, N. Krilova, A. Murciano, G. E. López-Tolsa, R. Pellón, V. A. Makarov. Static internal representation of dynamic situations reveals time compaction in human cognition. Journal of Advanced Research. 2021, 28, 111-125. https://doi.org/10.1016/j.jare.2020.08.008
• A. I. Zharinov, V. A. Makarov, V. B. Kazantsev, S. A. Lobov. Spatial memory based on an STDP-driven neural network. 2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR). 2020, 269-271. https://doi.org/10.1109/DCNAIR50402.2020.9216804
• C. Calvo-Tapia, V. A. Makarov, C. Van Leeuwen. Basic principles drive self-organization of brain-like connectivity structure. Communications in Nonlinear Science and Numerical Simulation. 2020, 82, 105065. https://doi.org/10.1016/j.cnsns.2019.105065 
• A. N. Gorban, V. A. Makarov, I. Y. Tyukin. High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality. Entropy. 2020, 22, 82. https://www.mdpi.com/1099-4300/22/1/82
• C. Calvo-Tapia, J. A. Villacorta-Atienza, S. Díez-Hermano, M. Khoruzhko, S. Lobov, I. Potapov, A. Sánchez-Jiménez, V. A. Makarov. Semantic knowledge representation for strategic interactions in dynamic situations. Frontiers in Neurorobotics. 2020, 4, 4. https://doi.org/10.3389/fnbot.2020.00004 
• S. A. Lobov, A. N. Mikhaylov, M. Shamshin, V. A. Makarov, V. B. Kazantsev. Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot. Frontiers in Neuroscience. 2020, 14, 88. https://doi.org/10.3389/fnins.2020.00088
• C. Calvo Tapia, I. Tyukin, V. A. Makarov. Universal principles justify the existence of concept cells. Sci Rep. 2020, 10, 7889. https://doi.org/10.1038/s41598-020-64466-7

News

• 29 de junio de 2022. Aprobados dos nuevos proyecto de investigación adscritos al IMI en la resolución provisional de concesión de ayudas 2021 a «PROYECTOS DE GENERACIÓN DE CONOCIMIENTO» en el marco del Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia, del Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023. Enhorabuena a Enrique Arrondo y Marina Logares (IPs de uno de los proyectos concedidos) y a Valeri Makarov (IP del otro proyecto).