A novel parallel multiplier using spiking neural P systems with dendritic delays

Diaz, Carlos; Frias, Thania; Sánchez, Giovanny; Pérez, Héctor; Sánchez-Pérez, Gabriel; Duchen, Gonzalo

doi:10.1016/j.neucom.2017.02.009

Cited by 22 publications

(34 citation statements)

References 12 publications

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“…In step t + 2 , neurons l (2) i and l (3) i become active after accumulating two spikes. Therefore, according to the maxsequentiality strategy, a randomly selected neuron will be executed.…”

Section: The Universality Of Psn P Systems With Delay Using the Max-sequentiality Strategymentioning

confidence: 99%

“…In terms of theoretical research, the computational performance of various extended models is discussed and analyzed, such as: universal or small universal computing systems [6, 14, 21-26, 33-35, 41, 47, 51], language production ability [1,15,46,48,52], design optimization algorithm [50,54], etc. In terms of practical applications [5,49], the SN P system and its variants have also been widely used, such as: pattern recognition [4,36,37], fuzzy SN P systems and fault diagnosis of power systems [31,32,[38][39][40], computational biology [2], solving computational hard problems [12,16,53], performing arithmetic and logical operations, and hardware implementation [3,8,17], etc.…”

Section: Introductionmentioning

confidence: 99%

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Universality of spiking neural P systems with polarizations working in sequential mode induced by maximum spike number

Liu

Jiang

2021

J Membr Comput

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Based on the inspiration that the communication signal between neurons in biology is composed of short electrical pulses, we investigate a new variant of spiking neural P systems, i.e., spiking neural P systems with polarizations (PSN P systems), which have a rule-triggering condition associated with polarization. In this work, we focus on the computational power of sequential PSN P systems with delay based on the maximum number of spikes, i.e., the ability to preferentially fire the neuron with the maximum number of spikes among the active neurons at each step (except for the neurons in the refractory period) of the computation. Thus, two strategies are considered, i.e., the max-sequentiality strategy and the max-pseudo-sequentiality strategy, and we prove that PSN P systems with delay adopting the max-sequentiality strategy or the max-pseudo-sequentiality strategy are Turing universal as number generating devices. The results give positive answers to the open problem formulated in [Tingfang Wu et al. (2020), Neurocomputing, 401, 392-404].

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Section: The Universality Of Psn P Systems With Delay Using the Max-sequentiality Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Universality of spiking neural P systems with polarizations working in sequential mode induced by maximum spike number

Liu

Jiang

2021

J Membr Comput

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“…Additionally, simulations of 3-8 decoders on the MeCosim platform were observed and the results infer that the decoders based on the SNPS are efficient. In 2017, a parallel multiplier was constructed using the SNPS with dendritic delays by Díaz, et al [41]. It is important to note that the models discussed above have one common drawback-i.e., the processing of the inputs is sequential, which increases the processing time.…”

Section: Performing Arithmetic and Logical Operations And Hardware Immentioning

confidence: 99%

“…It is important to note that the models discussed above have one common drawback-i.e., the processing of the inputs is sequential, which increases the processing time. The parallel multiplier discussed in [41] can multiply any two natural numbers with long digits in parallel. Moreover, the parallel multiplier has optimized sequential processors which can process multiple units in parallel and then the implementation of the parallel multiplier circuit in FPGA devices, such as spiking neural-network architecture for versatile applications (SNAVA) and field programmable gate array prototypes, also were investigated.…”

Section: Performing Arithmetic and Logical Operations And Hardware Immentioning

confidence: 99%

“…Many variants of SNPS have been introduced by incorporating features from the biological neurons such as asynchronous systems [8], astrocytes [9], rule on synapses [10], communication on request [11,12], synapses with schedules [13], structural plasticity [14], weighted synapses [15], inhibitory synapses [16], anti-spikes [17], etc. These models have also been used in solving problems related to real-life applications, such as fault diagnosis of power systems [18][19][20][21][22][23][24][25][26][27][28][29][30][31], pattern recognition [32][33][34], computational biology [35], performing arithmetic and logical operations and hardware implementation [36][37][38][39][40][41][42][43][44][45][46][47], solving computational hard problems [18,[48][49][50][51][52]…”

Section: Introductionmentioning

confidence: 99%

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On Applications of Spiking Neural P Systems

Fan

Paul

et al. 2020

Applied Sciences

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Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations.

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