José Luis Carrillo-Medina

Learn More
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)
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)
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)
We study the emerging collective dynamics of a neural network model that emits and recognizes neural signatures with different network topolo-gies in order to assess the capacity of a neural network to implement a signature-based information processing strategy. Complex collective dynamics emerge in the proposed model in the presence of stimuli, i.e.(More)
  • 1