Pattern Recognition With Spiking Neural Networks and Dynamic Synapses

Belatreche, Ammar; Maguire, Liam; McGinnity, Martin

doi:10.1142/9789812702661_0040

Cited by 6 publications

(5 citation statements)

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“…2a, b). These dynamics underlie the behaviour of a single synaptic connection between two neurons or between the input spike train sources and an input neuron [22]. The synaptic current (I syn ) is fed to an IAF neuron, whose state is represented by a voltage across its cell membrane and a threshold.…”

Section: Dynamic Synapses Based Architecturementioning

confidence: 99%

Advances in Design and Application of Spiking Neural Networks

2006

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This paper presents new findings in the design and application of biologically plausible neural networks based on spiking neuron models, which represent a more plausible model of real biological neurons where time is considered as an important feature for information encoding and processing in the brain. The design approach consists of an evolutionary strategy based supervised training algorithm, newly developed by the authors, and the use of different biologically plausible neuronal models. A dynamic synapse (DS) based neuron model, a biologically more detailed model, and the spike response model (SRM) are investigated in order to demonstrate the efficacy of the proposed approach and to further our understanding of the computing capabilities of the nervous system. Unlike the conventional synapse, represented as a static entity with a fixed weight, employed in conventional and SRM-based neural networks, a DS is weightless and its strength changes upon the arrival of incoming input spikes. Therefore its efficacy depends on the temporal structure of the impinging spike trains. In the proposed approach, the training of the network free parameters is achieved using an evolutionary strategy where, instead of binary encoding, real values are used to encode the static and DS parameters which underlie the learning process. The results show that spiking neural networks based on both types of synapse are capable of learning non-linearly separable data by means of spatio-temporal encoding. Furthermore, a comparison of the obtained performance with classical neural networks (multi-layer perceptrons) is presented.

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Section: Dynamic Synapses Based Architecturementioning

confidence: 99%

Advances in Design and Application of Spiking Neural Networks

2006

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“…Pattern recognition will be carried out with the procedure described in [9][10][11][12][13][14][15][16]. The classification is made by counting the Table 2 Spike quantity for all the inputs and the parameter combination in Section 2.3.2 for T ¼100 ms. number of spikes generated, by each of the input patterns, when processed by the neuron.…”

Section: Pattern Recognition Methodsmentioning

confidence: 99%

“…was optimized, one can see that the initial error is normally smaller, this is because the fixed values for the parameters were chosen from already known values which worked well for pattern recognition [9][10][11][12][13][14][15][16].…”

Section: In a Second Combination Wmentioning

confidence: 99%

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

Cachon¹,

Vázquez²

2015

Neurocomputing

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“…However, their application in computer vision and pattern recognition has not been widely explored. Although there are some works related to image segmentation [ 36 – 38 ] and pattern recognition [ 39 – 43 ], there still are several issues to research related to the learning process, design, and implementation.…”

Section: Introductionmentioning

confidence: 99%

Training Spiking Neural Models Using Artificial Bee Colony

Vázquez

Garro

2015

Computational Intelligence and Neuroscience

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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, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy.

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Pattern Recognition With Spiking Neural Networks and Dynamic Synapses

Cited by 6 publications

References 0 publications

Advances in Design and Application of Spiking Neural Networks

Advances in Design and Application of Spiking Neural Networks

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

Training Spiking Neural Models Using Artificial Bee Colony

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