Normal forms for spiking neural P systems

Ibarra, Óscar H.; Pun, Andrei; Pun, Gheorghe; Rodríguez‐Patón, Alfonso; Sosík, Petr; Woodworth, Sara

doi:10.1016/j.tcs.2006.11.025

Cited by 68 publications

(33 citation statements)

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“…Together with SUB modules, this suffices in the case when the number to accept is introduced as the number of spikes initially present in neuron σ  . If this number is introduced in the system as the distance between the first two spikes which enters the input neuron, then a input module is necessary, as used, for instance, in [3]. Note that the module INPUT from [3] uses only pure rules (involving only spikes, not also anti-spikes), hence we get a theorem like Theorem 1 also for the accepting case, for both ways of providing the input number.…”

Section: Universality Resultsmentioning

confidence: 99%

Spiking Neural P Systems with Anti-Spikes

Pan

Păun

2009

INT J COMPUT COMMUN

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177

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Besides usual spikes employed in spiking neural P systems, we consider "anti-spikes", which participate in spiking and forgetting rules, but also annihilate spikes when meeting in the same neuron. This simple extension of spiking neural P systems is shown to considerably simplify the universality proofs in this area: all rules become of the form b c → b ′ or b c → λ , where b, b ′ are spikes or anti-spikes. Therefore, the regular expressions which control the spiking are the simplest possible, identifying only a singleton. A possible variation is not to produce anti-spikes in neurons, but to consider some "inhibitory synapses", which transform the spikes which pass along them into anti-spikes. Also in this case, universality is rather easy to obtain, with rules of the above simple forms.

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Section: Universality Resultsmentioning

confidence: 99%

Spiking Neural P Systems with Anti-Spikes

Pan

Păun

2009

INT J COMPUT COMMUN

Self Cite

177

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“…The models of P system are mainly divided into three types, namely, the cell-like P system [2], the tissue-like P system [3] and the neural-like P system [4]. They have been applied to solve the problems such as NP problems [5]- [8], image processing [9], [10], arithmetic operations [11]- [14] and so on.…”

Section: Introductionmentioning

confidence: 99%

The P System Design Method based on the P Module

Guo¹,

Peng²,

Lian³

2018

ijacsa

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Abstract-Membrane computing is a kind of biocomputing model. At present, the main research areas of membrane computing are computational models and P system design. With the expansion of the P system scale, how to rapidly construct the P system has become a prominent issue. Designing P system based on P module is a P system design method proposed in recent years. This method provides information hiding and can build P system through recursive combination. However, the current P module design lacks a unified design method and lacks the standard process of building P system from P module. This paper studies the structural characteristics of cell-like P systems, and proposes an improved P module design method and a process for assembling P systems through P modules. In order to fully expound the design method of P module, the P system for the square root of the large number was analyzed and designed. And the correctness of the P system based on the P module design method was verified by an instance.

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“…(The "code" of the particular partial recursive function and the argument of the function are introduced in registers 1 and 2 of the universal machine and the value of the function for that argument, if defined, is found in register 0 when/if the machine halts.) Following then the construction of an SN P system simulating a register machine from [3], with some improvements inspired from [2] and some additional "code optimization", as well as a suitable input module, we get an SN P system with 84 neurons simulating the register machine from [4].…”

Section: Introductionmentioning

confidence: 99%

Small universal spiking neural P systems

Păun¹,

Păun²

2007

Biosystems

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195

104

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Summary. In search for small universal computing devices of various types, we consider here the case of spiking neural P systems (SN P systems), in two versions: as devices computing functions and as devices generating sets of numbers. We start with the first case and we produce a universal spiking neural P system with 84 neurons. If a slight generalization of the used rules is adopted, namely, we allow rules for producing simultaneously several spikes, then a considerable improvement, to 49 neurons, is obtained. For SN P systems used as generators of sets of numbers, we find a universal system with restricted rules having 76 neurons, and one with extended rules having 50 neurons.

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Normal forms for spiking neural P systems

Cited by 68 publications

References 1 publication

Spiking Neural P Systems with Anti-Spikes

Spiking Neural P Systems with Anti-Spikes

The P System Design Method based on the P Module

Small universal spiking neural P systems

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