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
6 results
Search Results
Now showing 1 - 6 of 6
Item Necessary conditions for signal processing by resonant neurons(2001) Villacorta Atienza, José Antonio; Sánchez Jiménez, Abel; Panetsos Petrova, FivosWe study a mathematical model for information processing and coding by means of groups of resonant neurons. We conclude that incoming signals can be expressed by means of their Fourier series which coefficients are represented by the value of the membrane potential of the resonant neurons.- Project number: 63
Item Diseño e implementación de la metodología Flipped Classroom en la asignatura de “Estadística aplicada a la Biología”. Uso de dispositivos móviles para la evaluación del alumnado(2020) Juan Llamas, María del Carmen; Murciano Cespedosa, Antonio; Sánchez Jiménez, Abel; Villacorta Atienza, José Antonio 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 Cognitive Neural Network Driving DoF-Scalable Limbs in Time-Evolving Situations(2018) Calvo Tapia, Carlos; Villacorta Atienza, José Antonio; Kastalskiy, Innokentiy; Díez Hermano, Sergio; Sánchez Jiménez, Abel; Makarov Slizneva, ValeriyObject handling and manipulation are vital skills for humans and autonomous humanoid robots. The fundamental bases of how our brain solves such tasks remain largely unknown. Here we develop a novel approach that addresses the problem of limb movements in time-evolving situations at an abstract cognitive level. We exploit the concept of generalized cognitive maps constructed in the so-called handspace by a neural network simulating a wave simultaneously exploring different subject actions independently on the number of objects in the workspace. We show that the approach is scalable to limbs with minimalistic and redundant numbers of degrees of freedom (DoF). It also allows biasing the effort of reaching a target among different DoF.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.Item Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations(Frontiers in Neurorobotics, 2020) Calvo Tapia, Carlos; Villacorta Atienza, José Antonio; Díez Hermano, Sergio; Khoruzkho, Maxim; Lobov, Sergey; Potapov, Ivan; Sánchez Jiménez, Abel; Makarov Slizneva, ValeriyEvolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.