Spiking Neural P Systems: Theoretical Results and Applications

Rong, Haina; Wu, Tingfang; Pan, Linqiang; Zhang, Gexiang

doi:10.1007/978-3-030-00265-7_20

Cited by 18 publications

(7 citation statements)

References 48 publications

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“…The computational power of SN P systems was well studied. Several variants of these systems are known to have universal computational capability, being equivalent to Turing machines [44]. Meanwhile, as early as in 1943, McCulloch and Pitts [33] asserted that neural networks are computationally universal.…”

Section: Conclusion and Open Problemsmentioning

confidence: 99%

“…There are many possibilities for SN P systems for applications like optimization and classification with learning ability [44]. However, up to this moment, we know no attempt to bring problems and techniques from the neural computing area to the SN P systems area [54].…”

Section: Conclusion and Open Problemsmentioning

confidence: 99%

“…Many variants of SN P systems have been proposed [44]: with anti-spikes, with weights, with thresholds, with rules on synapses, with structural plasticity, with learning functions, with polarization, with white hole neurons, with astrocytes, etc. For a given problem, which of these models is the most efficient?…”

Section: Introductionmentioning

confidence: 99%

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Hyperparameter optimization in learning systems

Andonie

2019

J Membr Comput

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While the training parameters of machine learning models are adapted during the training phase, the values of the hyperparameters (or meta-parameters) have to be specified before the learning phase. The goal is to find a set of hyperparameter values which gives us the best model for our data in a reasonable amount of time. We present an integrated view of methods used in hyperparameter optimization of learning systems, with an emphasis on computational complexity aspects. Our thesis is that we should solve a hyperparameter optimization problem using a combination of techniques for: optimization, search space and training time reduction. Case studies from real-world applications illustrate the practical aspects. We create the framework for a future separation between parameters and hyperparameters in adaptive P systems.

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Section: Conclusion and Open Problemsmentioning

confidence: 99%

Section: Conclusion and Open Problemsmentioning

confidence: 99%

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Hyperparameter optimization in learning systems

Andonie

2019

J Membr Comput

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“…There are also works that focus on simulating SN P systems, as they are parallel in nature, in GPUs such as [9,10] and more recently in [11][12][13]. Much theoretical work has been done on SN P systems, e.g., their normal forms [14][15][16], formal representations [17][18][19], and their relations to classical models of computation [20][21][22][23][24][25] with a short and recent survey in [26]. After much theoretical work, more recently the work to apply SN P systems to real-world problems becomes even more active, with some early works on image processing e.g., [27] and more recently in [28], use for cryptography [29][30][31], use of evolutionary algorithms to design SN P systems [32][33][34], in pattern recognition [35,36], computational biology [37], with a recent survey in [38].…”

Section: Introductionmentioning

confidence: 99%

Snapse: A Visual Tool for Spiking Neural P Systems

et al. 2020

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Spiking neural P (SN P) systems are models of computation inspired by spiking neurons and part of the third generation of neuron models. SN P systems are equivalent to Turing machines and are able to solve computationally hard problems using a space-time trade-off. Research in SN P systems theory is especially active, more so in recent years as more efforts are directed towards their real-world applications. Usually, SN P systems are represented visually as a directed graph and simulated through mainly text-based simulations or tables. Thus, there is a need for tools that can simulate and create SN P Systems in a visual and easy-to-use manner. Snapse is such a tool which aims to hasten the speed and ease at which researchers may create and experiment with SN P systems. Furthermore, visual tools such as Snapse can help further bring SN P systems outside of theoretical computer science.

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“…A rather extensive bibliography on spiking neural P systems was published in the first volume of the Bulletin of the International Membrane Computing Society 2016, see [22]. A recent survey can be found in [27].…”

Section: Introductionmentioning

confidence: 99%

A formal framework for spiking neural P systems

et al. 2020

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Spiking neural P systems are a class of distributed parallel computing models, inspired by the way in which neurons process information and communicate with each other by means of spikes. In 2007, Freund and Verlan developed a formal framework for P systems to capture most of the essential features of P systems and to define their functioning in a formal way. In this work, we present an extension of the formal framework related to spiking neural P systems by considering the applicability of each rule to be controlled by specific conditions on the current contents of the cells. The main objective of this extension is to also capture spiking neural P systems in the formal framework. Another goal of our extension is to incorporate the notions of input and output. Finally, we also show that in the case of spiking neural P systems, the rules have a rather simple form and in that way spiking neural P systems correspond to vector addition systems where the application of rules is controlled by semi-linear sets.

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Spiking Neural P Systems: Theoretical Results and Applications

Cited by 18 publications

References 48 publications

Hyperparameter optimization in learning systems

Hyperparameter optimization in learning systems

Snapse: A Visual Tool for Spiking Neural P Systems

A formal framework for spiking neural P systems

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