José Luis Carrillo-Medina

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Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g., individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or(More)
Experimental evidence has revealed that different living neural systems can “sign” their output signals with some specific neural signature. Although experimental and modeling results suggest that these neural signatures can have significant implications for the activity of the neural circuits where they are present, the functional meaning of(More)
We study the emerging collective dynamics of a neural network model that emits and recognizes neural signatures with different network topologies in order to assess the capacity of a neural network to implement a signaturebased information processing strategy. Complex collective dynamics emerge in the proposed model in the presence of stimuli, i.e. specific(More)
This article focuses on the design and development of a control system for a robotic arm designed at the Universidad de las Fuerzas Armadas, Latacunga extension, by using Dynamixel servomotors. The use of Python software, with advantages and features of being a free programming language, provides the project with reliability and ease of communication with a(More)
Robotic arms have been proposed since the 50's and they have evolved to play a key role in a number of applications, such as food, auto motion, and entertainment industry. Two of the paramount parameters in the design of arm robots are their weight and their strength efficiency. Our aim was to improve these two key parameters by using ABS plastic in the(More)
The existence of neural fingerprints associated to specific cell types or to different processing states has been reported in widely different neural systems (e.g. see [1-3]). Does the nervous system have the ability to process information using these neural fingerprints? Model simulations suggest that neural signatures characterizing the origin of specific(More)
Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple(More)
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