Spiking neural P systems with structural plasticity
Spiking neural P systems with structural plasticity
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DOI：
10.1007/s00521-015-1857-4
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发表时间：
2015-11-01
影响因子：
6
通讯作者：
Song, Tao
中科院分区：
文献类型：
--
作者：
Cabarle, Francis George C.;Adorna, Henry N.;Song, Tao
尖峰神经P（SNP）系统是一类并行的，分布式的，非确定性计算模型的灵感来自于生物神经元的尖峰。在这项工作中，被称为结构可塑性的生物学特征被引入到SNP系统的框架中。结构可塑性是指突触的产生和删除，从而改变突触图。因此，具有结构可塑性的SNP系统（SNPSP系统）的“编程”是基于神经元如何相互连接。SNPSP系统也是对SNP系统中一个开放问题的部分回答，该问题仅针对突触具有动态性。对于接受模式和生成模式，我们证明了SNPSP系统是通用的。通过修改SNPSP系统的语义，引入了尖峰节省模式，并证明了该模式的通用性。然而，在保存模式下，可能会出现死锁状态，我们证明，达到这样的状态是不可判定的。最后，我们提供了一种技术，以使用结构塑性解决一个困难的问题：一个常数时间，不确定性，和半一致的解决方案的NP完全问题子集和。
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.