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Spiking neural P systems with structural plasticity

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Abstract

Spiking neural P (SNP) systems are a class of parallel, distributed, and nondeterministic computing models inspired by the spiking of biological neurons. In this work, the biological feature known as structural plasticity is introduced in the framework of SNP systems. Structural plasticity refers to synapse creation and deletion, thus changing the synapse graph. The “programming” therefore of a brain-like model, the SNP system with structural plasticity (SNPSP system), is based on how neurons connect to each other. SNPSP systems are also a partial answer to an open question on SNP systems with dynamism only for synapses. For both the accepting and generative modes, we prove that SNPSP systems are universal. Modifying SNPSP systems semantics, we introduce the spike saving mode and prove that universality is maintained. In saving mode, however, a deadlock state can arise, and we prove that reaching such a state is undecidable. Lastly, we provide one technique in order to use structural plasticity to solve a hard problem: a constant time, nondeterministic, and semi-uniform solution to the NP-complete problem Subset Sum.

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Notes

  1. This is inspired by synaptic homeostasis in biological neurons, where total synapse number in the system is left unchanged [2].

  2. http://ppage.psystems.eu/.

  3. We refer to this later as synapse level nondeterminism.

  4. Or in the case of neurons, the quanta of energy which is the spike.

  5. ADD, SUB, and FIN module neurons in the saving and generative case, since the accepting case requires a lesser number of neurons with plasticity rules.

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Acknowledgments

F. G. C. Cabarle is supported by a scholarship from the DOST-ERDT Philippines. T. Song is supported by the China Postdoctoral Science Foundation Project (No. 2014M550389). H. N. Adorna is funded by a DOST-ERDT research grant and the Semirara Mining Corporation professorial chair of the College of Engineering, UP Diliman. M. J. Pérez-Jiménez acknowledges the support of the Project TIN2012-37434 of the “Ministerio de Economía y Competitividad” of Spain, co-financed by FEDER funds. Miguel Ángel Martínez-del Amor are also acknowledged. The authors are also grateful to three anonymous referees for their useful comments.

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Authors and Affiliations

  1. Algorithms and Complexity Lab, Department of Computer Science, University of the Philippines Diliman, 1101, Quezon City, Philippines

    Francis George C. Cabarle & Henry N. Adorna

  2. Department of Computer Science and AI, University of Sevilla, Avda. Reina Mercedes s/n, 41012, Sevilla, Spain

    Mario J. Pérez-Jiménez

  3. Key Laboratory of Image Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Tao Song

Authors
  1. Francis George C. Cabarle
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  2. Henry N. Adorna
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  3. Mario J. Pérez-Jiménez
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  4. Tao Song
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Corresponding author

Correspondence to Francis George C. Cabarle.

Additional information

Extended and improved version of the submission from ACMC 2013 [4].

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Cabarle, F.G.C., Adorna, H.N., Pérez-Jiménez, M.J. et al. Spiking neural P systems with structural plasticity. Neural Comput & Applic 26, 1905–1917 (2015). https://doi.org/10.1007/s00521-015-1857-4

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  • DOI: https://doi.org/10.1007/s00521-015-1857-4

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