Person: Sánchez Jiménez, Abel
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First Name
Abel
Last Name
Sánchez Jiménez
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Biológicas
Department
Biodiversidad, Ecología y Evolución
Area
Matemática Aplicada
Identifiers
3 results
Search Results
Now showing 1 - 3 of 3
Item Static internal representation of dynamic situations reveals time compaction in human cognition(Journal of Advanced Research, 2020) Villacorta-Atienza, José Antonio; Calvo Tapia, Carlos; Díez-Hermano, Sergio; Sánchez Jiménez, Abel; Lobov, Sergei; Krilova, Nadia; Murciano Cespedosa, Antonio; López-Tolsa, Gabriela E.; Pellón, Ricardo; Makarov Slizneva, ValeriyIntroduction: The human brain has evolved under the constraint of survival in complex dynamic situations. It makes fast and reliable decisions based on internal representations of the environment. Whereas neural mechanisms involved in the internal representation of space are becoming known, entire spatiotemporal cognition remains a challenge. Growing experimental evidence suggests that brain mechanisms devoted to spatial cognition may also participate in spatiotemporal information processing. Objectives: The time compaction hypothesis postulates that the brain represents both static and dynamic situations as purely static maps. Such an internal reduction of the external complexity allows humans to process time-changing situations in real-time efficiently. According to time compaction, there may be a deep inner similarity between the representation of conventional static and dynamic visual stimuli. Here, we test the hypothesis and report the first experimental evidence of time compaction in humans. Methods: We engaged human subjects in a discrimination-learning task consisting in the classification of static and dynamic visual stimuli. When there was a hidden correspondence between static and dynamic stimuli due to time compaction, the learning performance was expected to be modulated. We studied such a modulation experimentally and by a computational model. Results: The collected data validated the predicted learning modulation and confirmed that time compaction is a salient cognitive strategy adopted by the human brain to process time-changing situations. Mathematical modelling supported the finding. We also revealed that men are more prone to exploit time compaction in accordance with the context of the hypothesis as a cognitive basis for survival. Conclusions: The static internal representation of dynamic situations is a human cognitive mechanism involved in decision-making and strategy planning to cope with time-changing environments. The finding opens a new venue to understand how humans efficiently interact with our dynamic world and thrive in nature.Item Computational study of resonant neural behaviour in the presence of continuous signals(2001) Sánchez Jiménez, Abel; Villacorta Atienza, José Antonio; Panetsos Petrova, Fivos; Pérez de Vargas, Antonio; Rattay, FrankThe resonant behaviour and the response preference to input signals of specific frequencies are well known properties of many neurons of the Central Nervous System. In the present communication we computationally evaluate a theoretical model of oscillating neurons and we prove that ensembles of neurons with a reduced variety of channels could make use of the fluctuations of their membrane potential to perform signal analysis.Item Benchmarking of tools for axon length measurement in individually-labeled projection neurons(PLoS Computational Biology, 2021) Rubio Teves, Mario; Díez Hermano, Sergio; Porrero, César; Sánchez Jiménez, Abel; Prensa Sepúlveda, Lucía; Clascá, Francisco; García Amado, María; Villacorta Atienza, José AntonioProjection neurons are the commonest neuronal type in the mammalian forebrain and their individual characterization is a crucial step to understand how neural circuitry operates. These cells have an axon whose arborizations extend over long distances, branching in complex patterns and/or in multiple brain regions. Axon length is a principal estimate of the functional impact of the neuron, as it directly correlates with the number of synapses formed by the axon in its target regions; however, its measurement by direct 3D axonal tracing is a slow and labor-intensive method. On the contrary, axon length estimations have been recently proposed as an effective and accessible alternative, allowing a fast approach to the functional significance of the single neuron. Here, we analyze the accuracy and efficiency of the most used length estimation tools—design-based stereology by virtual planes or spheres, and mathematical correction of the 2D projected-axon length—in contrast with direct measurement, to quantify individual axon length. To this end, we computationally simulated each tool, applied them over a dataset of 951 3D-reconstructed axons (from NeuroMorpho.org), and compared the generated length values with their 3D reconstruction counterparts. The evaluated reliability of each axon length estimation method was then balanced with the required human effort, experience and know-how, and economic affordability. Subsequently, computational results were contrasted with measurements performed on actual brain tissue sections. We show that the plane-based stereological method balances acceptable errors (~5%) with robustness to biases, whereas the projection-based method, despite its accuracy, is prone to inherent biases when implemented in the laboratory. This work, therefore, aims to provide a constructive benchmark to help guide the selection of the most efficient method for measuring specific axonal morphologies according to the particular circumstances of the conducted research.