Tuning the parameters of an integrate and fire neuron via a genetic algorithm for solving pattern recognition problems

Cachon, Aleister; Vázquez, Roberto A.

doi:10.1016/j.neucom.2012.11.059

Cited by 24 publications

(10 citation statements)

References 24 publications

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“…Cachón and Vázquez proposed a genetic algorithm (GA) to automatically adjust parameters, removing the need of manual tuning and increasing efficiency for solving pattern recognition problems [12].…”

Section: Related Workmentioning

confidence: 99%

Predicting Behaviors Of Insurance Costumers By Using The Genetic Algorithm

Bagheri

Ziaratban

Tarokh

2015

J. Math. Computer Sci.

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Improving customers' satisfaction, retaining loyal customers, and acquiring new customers are the most important goals of companies. Because of existing competition conditions among insurance industries, especially in optional insurances such as vehicle body insurance, implementing policies which have benefits for both customers and insurance companies can increase the corporation profitability and customers' satisfaction. In this paper, general policies of vehicle body insurance in Iran are investigated. In this way, a non-linear function (Cj) is proposed to separate profitable and harmful customers. This function is based on some insurance features of customers such as age of a vehicle, vehicle price, vehicle type, and number of years without accident. Customers are categorized based on the calculated values of Cj in which all parameters are optimally set. The parameters have to be set in the training phase to calculate reliable Cj values for customers. Parameters of Cj are set so that in a subset of the customers of the training set for which the value of Cj is greater than a threshold (Th), the number of profitable customers and disadvantageous customers should be maximized and minimized, respectively. A customer with positive M (monetory) value is a profitable customer. To optimize this multivariable problem with large number of parameters, the genetic algorithm is used. After optimizing the parameters in the training phase by using GA, the behaviors of the customers in the test set are predicted. In the prediction phase, it was estimated that a costumer is profitable or not, even he had accidents and received recompenses from the company. If a customer is predicted as profitable, the company allows him to enjoy some discounts. In the current Iranian insurance policies, all discounts are canceled for next insurance year if a customer has accidents and gives recompense from the company. By implementing the proposed policy, the customers' satisfactions are increased and also more new customers are acquired from other insurance companies.

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Section: Related Workmentioning

confidence: 99%

Predicting Behaviors Of Insurance Costumers By Using The Genetic Algorithm

Bagheri

Ziaratban

Tarokh

2015

J. Math. Computer Sci.

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“…They play a crucial role in determining the algorithm performance. Being inspired and motivated by [15], [16], the GA is used to tune the parameters of the BCO model for each benchmark problem. In the computational experiments, the GA is executed first and the resulting parameter setting will be passed to the BCO model to solve each of the benchmark instances.…”

Section: Genetic Algorithm For Parameter Tuningmentioning

confidence: 99%

A bee colony optimization with automated parameter tuning for sequential ordering problem

Wun

Wong

Khader

et al. 2014

2014 4th World Congress on Information and Communication Technologies (WICT 2014)

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Sequential Ordering Problem (SOP) is a type of Combinatorial Optimization Problem (COP). Solving SOP requires finding a feasible Hamiltonian path with minimum cost without violating the precedence constraints. SOP models myriad of real world industrial applications, particularly in the fields of transportation, vehicle routing and production planning. The main objective of this research is to propose an idea of solving SOP using the Bee Colony Optimization (BCO) algorithm. The underlying mechanism of the BCO algorithm is the bee foraging behavior in a typical bee colony. Throughout the research, the SOP benchmark problems from TSPLIB will be chosen as the testbed to evaluate the performance of the BCO algorithm in terms of the solution cost and the computational time needed to obtain an optimum solution. Moreover, efforts are taken to investigate the feasibility of using the Genetic Algorithm to optimally tune the parameters equipped in the existing BCO model. On average, over the selected 40 benchmark problems, the proposed method has successfully solved 9 (22.5%) benchmark problems to optimum, 17 (42.5%) benchmark problems ≤ 1% of deviation from the known optimum, and 37 (85%) benchmark problems ≤ 5% of deviation from the known optimum. Overall, the 40 benchmark problems are solved to 2.19% from the known optimum on average.

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“…Although there are more developed neuron models and Lapique neuron model is being the simplest neuron model of them all, even today, Lapicque's neuron model is still one of the most used neuron models in neuroscience. It has found application areas in neural and cellular networks studies and also in computational neurology due to its simplicity [2,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Synchronization of two cardiomyocyte with Integrate and fire neuron model model is examined in [16].…”

Section: Introductionmentioning

confidence: 99%

“…Effects of passive dendritic tree properties on the firing dynamics of a leaky integrate-and-fire neuron are inspected in [27]. The parameters of an integrate and fire neuron is tuned via a genetic algorithm for solving pattern recognition problems in [28]. Sensitivity of the coefficient of variation of interspike intervals on the lower boundary of membrane potential for the leaky integrate-and-fire neuron model is studied EEE 101-2 in [29].…”

Section: Introductionmentioning

confidence: 99%

A Simple Lapicque Neuron Emulator

Tulumbaci¹,

Eryildiz²,

Mutlu³

2020

World Congress on Electrical Engineering and Computer Systems and Science

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Neuron models such as Leaky Integrate and Fire (Lapicque Neuron) model, Hodgkin Huxley Model, Fitzhugh-Nagumo Model, and Izhikevich neuron model are commonly used in neuronal studies. Nobel laureate Hudgkin-Hugsley neuron model is quite complex despite of being accurate. That's why simplified neuron models such as Fitzhugh-Nagumo neuron model is commonly used in studies. Lapicque Neuron is the first neuron model suggested in Literature. It had been reported by Lapicque in 1907, it is easy to understand, and it is still commonly used for neural studies for its simplicity. Neuron emulator circuits are used for education and research purposes. They can be made using individual electronics components or VLSI circuits. In this study, a Lapicque Neuron emulator circuit topology is introduced. It is simple enough to be made by students and it can be made using cheap off-the shelves components in a short time. Its circuit analysis is also given. It is experimentally shown that the emulator is able to mimic activation potentials well. It is suggested that the neuron emulator can be used for not only educational purposes in Biomedical Engineering Courses but also neuronal studies to show and prove concepts.

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Tuning the parameters of an integrate and fire neuron via a genetic algorithm for solving pattern recognition problems

Cited by 24 publications

References 24 publications

Predicting Behaviors Of Insurance Costumers By Using The Genetic Algorithm

Predicting Behaviors Of Insurance Costumers By Using The Genetic Algorithm

A bee colony optimization with automated parameter tuning for sequential ordering problem

A Simple Lapicque Neuron Emulator

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