EditorialComputational intelligence and bioinspired systems
Section snippets
Neurocomputational formulations
In the paper by Angelo di Garbo et al., the functional role of electrical synapses in a network of inhibitory interneurons is investigated by using a single compartment biophysical model of a Fast Spiking cell. In particular, the parameter values, which lead to the emergence of synchronous regimes in a network of Fast Spiking interneurons coupled by chemical and electrical synapses in the weak coupling limit, were determined theoretically.
The paper by E.E. Claverol-Tinturé et al. describes the
Single and hybrid conventional models
Enrique Romero and René Alquézar obtain heuristically a suboptimal solution for the selection of weights of the new hidden units in sequential feed-forward neural networks. This process, that usually involves a non-linear optimization problem, cannot be solved analytically in the general case. The obtained results indicate that the orthogonalization of the output vectors of the hidden units outperforms the usual strategy of matching the residue, both for approximation and generalization
Neuroingeneering and hardware implementations
The paper by Christian Morillas and co-workers shows the development of a set of software and hardware tools to interface with neural tissue, in order to transmit visual information encoded into a bioinspired neural-like form. The set is composed of a retina-like encoder, implemented both in software and on FPGA chips, and a platform to optimize the electrical stimulation parameters for a multielectrode implant. The main objective is to progress towards a functional visual prosthesis for the
Applications
Contrary to the conventional point of view based on the modeling of non-linear systems, the paper by K. Madani and L. Thiaw proposes to deem the multi-modeling identification concept as building a modular architecture, inspired from ANN operation mode, where each neuron (module), represented by one of the local models, realizes some higher level transfer function in the non-linear system's behavior identification and prediction context.
Alberto Guillén et al. present a new algorithm that applies
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2017, NeurocomputingA Bio-inspired knowledge system for improving combined cycle plant control tuning
2014, NeurocomputingCitation Excerpt :describes a robust and general rule-based approach to manage situation awareness. Bio-inspired models are inspired by nature [45], which can be applied in many systems ranging from such diverse fields as industry, network security and healthcare [45,53,55]. These models provide a deeper insight of biological phenomena.
Advances in artificial neural networks and machine learning
2013, Neurocomputing