Dendrite P Systems Toolbox: Representation, Algorithms and Simulators.

Dendrite P systems (DeP systems) are a recently introduced neural-like model of computation. They provide an alternative to the more classical spiking neural (SN) P systems. In this paper, we present the first software simulator for DeP systems, and we investigate the key features of the representation of the syntax and semantics of such systems. First, the conceptual design of a simulation algorithm is discussed. This is helpful in order to shade a light on the differences with simulators for SN P systems, and also to identify potential parallelizable parts. Second, a novel simulator implemented within the P-Lingua simulation framework is presented. Moreover, MeCoSim, a GUI tool for abstract representation of problems based on P system models has been extended to support this model. An experimental validation of this simulator is also covered.

[1]  Henry N. Adorna,et al.  Spiking Neural P System Simulations on a High Performance GPU Platform , 2011, ICA3PP.

[2]  Hong Peng,et al.  Spiking neural P systems with multiple channels , 2017, Neural Networks.

[3]  Linqiang Pan,et al.  Cell-like spiking neural P systems , 2016, Theor. Comput. Sci..

[4]  Henry N. Adorna,et al.  Handling Non-determinism in Spiking Neural P Systems: Algorithms and Simulations , 2019, Fundam. Informaticae.

[5]  Xu Zhang,et al.  A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks , 2016, Int. J. Neural Syst..

[6]  Jun Wang,et al.  Nonlinear Spiking Neural P Systems , 2020, Int. J. Neural Syst..

[7]  Ferrante Neri,et al.  A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning , 2020, Integr. Comput. Aided Eng..

[8]  Ferrante Neri,et al.  An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems , 2014, Int. J. Neural Syst..

[9]  Miguel A. Martínez-del-Amor,et al.  P-Lingua 2.0: A software framework for cell-like P systems , 2009, Int. J. Comput. Commun. Control.

[10]  Pierluigi Frisco,et al.  Applications of Membrane Computing in Systems and Synthetic Biology , 2014 .

[11]  Fan Yang,et al.  Spiking neural P systems with multiple channels and anti-spikes , 2018, Biosyst..

[12]  Pan Linqiang,et al.  Spiking neural P systems with neuron division and budding , 2011 .

[13]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[14]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[15]  Jin He,et al.  Monitor-Based Spiking Recurrent Network for the Representation of Complex Dynamic Patterns , 2019, Int. J. Neural Syst..

[16]  Linqiang Pan,et al.  Spiking Neural P Systems With Communication on Request and Mute Rules , 2017, IEEE Transactions on Parallel and Distributed Systems.

[17]  Andrei Paun,et al.  Simplified and Yet Turing Universal Spiking Neural P Systems with Communication on Request , 2018, Int. J. Neural Syst..

[18]  Gheorghe Paun,et al.  Membrane Computing, 10th International Workshop, WMC 2009, Curtea de Arges, Romania, August 24-27, 2009. Revised Selected and Invited Papers , 2010, Workshop on Membrane Computing.

[19]  Henry N. Adorna,et al.  Simulating Spiking Neural P Systems Without Delays Using GPUs , 2011, Int. J. Nat. Comput. Res..

[20]  John A. Freeman,et al.  Dendritic Spikes and Their Inhibition in Alligator Purkinje Cells , 1968, Science.

[21]  Xiangxiang Zeng,et al.  Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity , 2019, J. Membr. Comput..

[22]  Alfonso Rodríguez-Patón,et al.  A New Class of Symbolic Abstract Neural Nets: Tissue P Systems , 2002, COCOON.

[23]  Xiangxiang Zeng,et al.  Matrix Representation of Spiking Neural P Systems , 2010, Int. Conf. on Membrane Computing.

[24]  Jun Wang,et al.  A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies , 2020, Eng. Appl. Artif. Intell..

[25]  Linqiang Pan,et al.  Spiking Neural P Systems with Astrocytes , 2012, Neural Computation.

[26]  A. Rodríguez-Patón,et al.  Spiking Neural P Systems with Several Types of Spikes , 2011, Int. J. Comput. Commun. Control.

[27]  Mario J. Pérez-Jiménez,et al.  Reaching efficiency through collaboration in membrane systems: Dissolution, polarization and cooperation , 2017, Theor. Comput. Sci..

[28]  Xiaohui Luo,et al.  Dendrite P systems , 2020, Neural Networks.

[29]  Hong Peng,et al.  Fuzzy reasoning spiking neural P system for fault diagnosis , 2013, Inf. Sci..

[30]  U. Rajendra Acharya,et al.  Automated EEG-based screening of depression using deep convolutional neural network , 2018, Comput. Methods Programs Biomed..

[31]  Josep L. Rosselló,et al.  Compact Hardware Synthesis of Stochastic Spiking Neural Networks , 2019, Int. J. Neural Syst..

[32]  Mario J. Pérez-Jiménez,et al.  Population Dynamics P System (PDP) Models: A Standardized Protocol for Describing and Applying Novel Bio-Inspired Computing Tools , 2013, PloS one.

[33]  Henry N. Adorna,et al.  Spiking neural P systems with structural plasticity , 2015, Neural Computing and Applications.

[34]  Linqiang Pan,et al.  Spiking Neural P Systems: Theoretical Results and Applications , 2018, Enjoying Natural Computing.

[35]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[36]  Gheorghe Paun,et al.  Spiking Neural dP Systems , 2011, Fundam. Informaticae.

[37]  Mario J. Pérez-Jiménez,et al.  A P-Lingua Based Simulator for Spiking Neural P Systems , 2011, Int. Conf. on Membrane Computing.

[38]  Gheorghe Paun,et al.  Computing with Membranes , 2000, J. Comput. Syst. Sci..

[39]  Mario J. Pérez-Jiménez,et al.  An interactive timeline of simulators in membrane computing , 2019, Journal of Membrane Computing.