Artículo
Dynamic threshold neural P systems
Autor/es | Peng, Hong
Wang, Jun Pérez Jiménez, Mario de Jesús Riscos Núñez, Agustín |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2019 |
Fecha de depósito | 2019-03-29 |
Publicado en |
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Resumen | Pulse coupled neural networks (PCNN, for short) are models abstracting the synchronization behavior
observed experimentally for the cortical neurons in the visual cortex of a cat’s brain, and the intersecting
cortical ... Pulse coupled neural networks (PCNN, for short) are models abstracting the synchronization behavior observed experimentally for the cortical neurons in the visual cortex of a cat’s brain, and the intersecting cortical model is a simplified version of the PCNN model. Membrane computing (MC) is a kind computation paradigm abstracted from the structure and functioning of biological cells that provide models working in cell-like mode, neural-like mode and tissue-like mode. Inspired from intersecting cortical model, this paper proposes a new kind of neural-like P systems, called dynamic threshold neural P systems (for short, DTNP systems). DTNP systems can be represented as a directed graph, where nodes are dynamic threshold neurons while arcs denote synaptic connections of these neurons. DTNP systems provide a kind of parallel computing models, they have two data units (feeding input unit and dynamic threshold unit) and the neuron firing mechanism is implemented by using a dynamic threshold mechanism. The Turing universality of DTNP systems as number accepting/generating devices is established. In addition, an universal DTNP system having 109 neurons for computing functions is constructed. |
Identificador del proyecto | 61472328
2018JY0083 Z2016143 Z2016148 17TD0034 |
Cita | Peng, H., Wang, J., Pérez Jiménez, M.d.J. y Riscos Núñez, A. (2019). Dynamic threshold neural P systems. Knowledge-Based Systems, 163 (january 2019), 875-884. |
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