Persona:
Paz López, Félix de la

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0000-0002-1530-159X
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Paz López
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Félix de la
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2018 - 20192
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Fecha de finalización: 2019 × Fecha de inicio: 2018 ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Frequency variation analysis in neuronal cultures for stimulus response characterization
    (Springer, 2019-01-03) Val Calvo, Mikel; Alegre Cortés, Javier; Ferrández Vicente, José Manuel; Fernández Jover, Eduardo; Val Calvo, Inhar; Álvarez Sánchez, José Ramón; Paz López, Félix de la
    In vitro neuronal cultures embodied in a closed-loop control system have been used recently to study neuronal dynamics. This allows the development of neurons in a controlled environment with the purpose of exploring the computational capabilities of such biological neural networks. Due to the intrinsic properties of in vitro neuronal cultures and how the neuronal tissue grows in them, the ways in which signals are transmitted and generated within and throughout the culture can be difficult to characterize. The neural code is formed by patterns of spikes whose properties are in essence nonlinear and non-stationary. The usual approach for this characterization has been the use of the post-stimulus time histogram (PSTH). PSTH is calculated by counting the spikes detected in each neuronal culture electrode during some time windows after a stimulus in one of the electrodes. The objective is to find pairs of electrodes where stimulation in one of the pairs produces a response in the other but not in the rest of the electrodes in other pairs. The aim of this work is to explore possible ways of extracting relevant information from the global response to culture stimulus by studying the patterns of variation over time for the firing rate, estimated from inverse inter-spike intervals, in each electrode. Machine learning methods can then be applied to distinguish the electrode being stimulated from the whole culture response, in order to obtain a better characterization of the culture and its computational capabilities so it can be useful for robotic applications
  • Miniatura
    Publicación
    Speech gestural interpretation by applying word representations in robotics
    (IOS Press, 2018-12-03) Almagro Cádiz, Mario; Paz López, Félix de la; Fresno Fernández, Víctor Diego
    Human-Robot Interaction (HRI) is a growing area of interest in Artificial Intelligence that aims to make interaction with robots more natural. In this sense, numerous research studies on verbal and visual interactions with robots have appeared. The present paper will focus on non-verbal communication and, more specifically, gestures related to speech, which is an open question. With the aim of developing this part of Human-Robot Interaction or HRI, a new architecture is proposed for the assignment of gestures to speech based on the analysis of semantic similarities. In this way, gestures will be intelligently selected using Natural Language Processing (NLP) techniques. The conditions for gesture selection will be determined from an assessment of the effectiveness of different language models in a lexical substitution task applied to gesture annotation. On the basis of this analysis, the aim is to compare models based on expert knowledge and statistical models generated from lexical learning.