Person:
Sánchez Jiménez, Abel

Profile Picture
First Name
Abel
Last Name
Sánchez Jiménez
URI
https://hdl.handle.net/20.500.14352/74359
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Biológicas
Department
Biodiversidad, Ecología y Evolución
Area
Matemática Aplicada
Identifiers
UCM identifierORCIDScopus Author IDWeb of Science ResearcherIDDialnet IDGoogle Scholar ID

Filters

2001 - 200912010 - 20201
Reset filters

Settings

Author: Villacorta Atienza, José Antonio × Subject: 51:57 ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Necessary conditions for signal processing by resonant neurons
    (2001) Villacorta Atienza, José Antonio; Sánchez Jiménez, Abel; Panetsos Petrova, Fivos
    We 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.
  • Thumbnail Image
    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, Valeriy
    Evolved 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.