Evolutionary Indirect Design of Feed-Forward Spiking Neural Networks

The present paper proposes the automatic design of Feed-Forward Spiking Neural Networks by representing several inherent aspects of the neural architecture in a proposed Context-Free Grammar; which is evolved through an Evolutionary Strategy. In the indirect design, the power of the design and the capabilities of the designed neural network are strongly related with the complexity of the grammars. The neural networks designed with the proposed grammar are tested with two well-known benchmark datasets of pattern recognition. Finally, neural networks derived from the proposed grammar are compared with other generated by similar grammars which were designed for the same purposed, the neural network design.

[1]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[2]  Anthony Brabazon,et al.  Foundations in Grammatical Evolution for Dynamic Environments , 2009, Studies in Computational Intelligence.

[3]  W. Gerstner,et al.  Time structure of the activity in neural network models. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[4]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Wolfgang Maass,et al.  Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons , 1996, NIPS.

[7]  Hui Li,et al.  Evolutionary artificial neural networks: a review , 2011, Artificial Intelligence Review.

[8]  Wang Qian,et al.  A cooperative method for supervised learning in Spiking neural networks , 2010, The 2010 14th International Conference on Computer Supported Cooperative Work in Design.

[9]  Luis Fernando de Mingo López,et al.  The optimal combination: Grammatical swarm, particle swarm optimization and neural networks , 2012, J. Comput. Sci..

[10]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[11]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

[12]  Sander M. Bohte,et al.  Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks , 2002, IEEE Trans. Neural Networks.

[13]  Cameron Johnson,et al.  A reversibility analysis of encoding methods for spiking neural networks , 2011, The 2011 International Joint Conference on Neural Networks.

[14]  J. Stephen Judd,et al.  Neural network design and the complexity of learning , 1990, Neural network modeling and connectionism.

[15]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[16]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[17]  Ammar Belatreche.  Biologically Inspired Neural Networks: Models, Learning, and Applications , 2010 .

[18]  Anthony Brabazon,et al.  Grammatical Differential Evolution , 2006, IC-AI.

[19]  Juan Martín Carpio Valadez,et al.  Developing Architectures of Spiking Neural Networks by Using Grammatical Evolution Based on Evolutionary Strategy , 2014, MCPR.

[20]  Michael O'Neill,et al.  Grammatical Evolution: Evolving Programs for an Arbitrary Language , 1998, EuroGP.

[21]  Sander M. Bohte,et al.  Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.