2015
DOI: 10.1155/2015/947098
|View full text |Cite
|
Sign up to set email alerts
|

Training Spiking Neural Models Using Artificial Bee Colony

Abstract: Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, incl… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
16
0

Year Published

2016
2016
2021
2021

Publication Types

Select...
5
3

Relationship

0
8

Authors

Journals

citations
Cited by 27 publications
(16 citation statements)
references
References 64 publications
(81 reference statements)
0
16
0
Order By: Relevance
“…We chose six experimental evolutionary algorithms, DE [13], CS [14], PSO [12], HS [19], ABC [18], and CRO [21], to compare with the proposed algorithm to evaluate its performance.…”
Section: ) Experimental Conditionsmentioning
confidence: 99%
See 2 more Smart Citations
“…We chose six experimental evolutionary algorithms, DE [13], CS [14], PSO [12], HS [19], ABC [18], and CRO [21], to compare with the proposed algorithm to evaluate its performance.…”
Section: ) Experimental Conditionsmentioning
confidence: 99%
“…The authors argued that the results obtained with the spiking neuron model trained with the cuckoo search algorithm were good. Vazquez et al proposed a learning strategy based on artificial bee colonies to train a spiking neuron aiming to solve pattern recognition problems [18]. Saleh et al proposed a hybrid harmony search algorithm with evolving SNNs for classification problems [19].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Therefore, in multi-layer networks, the input values to the inputs of the first layer, allow the signals to propagate through the network, and read the output values where output of the th node can be described by the function in Eq. 4.1 below [25], [28].…”
Section: Rnn Training Using Abc Algorithmmentioning
confidence: 99%
“…They can combine diversification and intensification in their search for finding optimal solutions, jointly with an objective function. A representative case is given in [5], where the classic ABC algorithm takes the part of one supervised training process. On the other hand, advanced optimisation for designing SNNs based on evolutionary metaheuristics has also been proposed [6].…”
mentioning
confidence: 99%