Method for Training a Spiking Neuron to Associate Input-Output Spike Trains

Mohemmed, Ammar; Schliebs, Stefan; Matsuda, Satoshi; Kasabov, Nikola

doi:10.1007/978-3-642-23957-1_25

Cited by 25 publications

(40 citation statements)

References 14 publications

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“…In this paper we have demonstrated the application of SNN trained with SPAN [10][11][12] on learning and classifying images of handwritten digits. One crucial factor in using SNN for real-world computer application is properly encoding the information into spike patterns.…”

Section: Discussionmentioning

confidence: 99%

“…More details can be found in previous publications [10][11][12]. SPAN rule is a supervised learning method to associate input spike pattern to a target spike train by adjusting the weights of the input synapses according to the following formula:…”

Section: Span Learning Methods and Network Topologymentioning

confidence: 99%

“…According to previous studies such as in [6] and recent one [14], temporal coding whereby information is encoded into precise time of the spikes, plays a significant role in the neural code of the brain especially in the visual system. SPAN [12,11], is a learning algorithm for spiking neural networks which is based on encoding input information as precise time of spikes (Temporal coding). This is opposite to rate coding where information is coded in the mean firing rate of the neurons.…”

Section: Introductionmentioning

confidence: 99%

“…

Abstract: In a previous work [12,11], the authors proposed SPAN: a learning algorithm based on temporal coding for Spiking Neural Network (SNN). The algorithm trains a neuron to associate target spike patterns to input spatio-temporal spike patterns.

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confidence: 99%

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Evaluating SPAN Incremental Learning for Handwritten Digit Recognition

Mohemmed

Kasabov

2012

Neural Information Processing

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In a previous work [12,11], the authors proposed SPAN: a learning algorithm based on temporal coding for Spiking Neural Network (SNN). The algorithm trains a neuron to associate target spike patterns to input spatio-temporal spike patterns. In this paper we present the details of experiment to evaluate the feasibility of SPAN learning on a real-world dataset: classifying images of handwritten digits. As spike encoding is an important issue in using SNN for practical applications, we discuss few methods for image conversion to spike patterns. The experiment yields encouraging results to consider the SPAN learning for practical temporal pattern recognition applications. Abstract. In a previous work [12,11], the authors proposed SPAN: a learning algorithm based on temporal coding for Spiking Neural Network (SNN). The algorithm trains a neuron to associate target spike patterns to input spatio-temporal spike patterns. In this paper we present the details of experiment to evaluate the feasibility of SPAN learning on a real-world dataset: classifying images of handwritten digits. As spike encoding is an important issue in using SNN for practical applications, we discuss few methods for image conversion to spike patterns. The experiment yields encouraging results to consider the SPAN learning for practical temporal pattern recognition applications.

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Section: Discussionmentioning

confidence: 99%

Section: Span Learning Methods and Network Topologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Evaluating SPAN Incremental Learning for Handwritten Digit Recognition

Mohemmed

Kasabov

2012

Neural Information Processing

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“…The synaptic weights of the network in supervised training algorithms are updated iteratively such that the desired input/output mapping to the SNN is obtained [11]. The present work adopts the Remote Supervised Method (ReSuMe) rule for updating the weight of a synapse i.…”

Section: Learning Methodsmentioning

confidence: 99%

Recognition of English Characters Using Spiking Neural Networks

Humaidi¹,

Kadhim²

2017

IJET

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Abstract-Text Recognition is one of the active and challenging areas of research in the pattern recognition field. It covers many applications like automatic number plate recognition, bank cheques, aid of reading for blind, and hand-written document conversion into form of structural text. In the present work, the spiking neural network (SNN) model is utilized for the identification of characters in a character set. The structure of considered neural network consists of two layers with Izhekevich neurons. Remote Supervised Method (ReSuMe) has been used as a learning rule for training. Also, the recognition of English characters has been considered based on spike Neural Networks. The network could successfully recognize a set of 48 characters. Keyword-Spiking Neural Network (SNN), Remote Supervised Method (ReSuMe), Artificial Neural Networks(ANN) I. INTRODUCTIONSpiking neuron models lead to the rise of third generation of artificial neural networks(ANN) [1]. Such models add more realistic sense in neural simulation and invocation of the time concept. It has been shown that a considerable enhancement in image recognition speed can be reached by using pulse coding (versus rate coding) in biological neurons of the cortex [2]. Generally, a longer processing time is required by rate coding as the neuron could be fired after waiting for several spikes to come. Also, in case of pulse coding, the information based on timing order of incoming spikes can be encoded at faster rate.The researchers in [3] and [4] have used spiking neural networks based on pulse coding for image recognition application. The model has been developed by Gupta could recognize a set of 48 characters with 3×5 pixel size. The model of integrate and fire spiking neuron network has been utilized for recognition and the spike timing based plasticity (STDP) has been used for training [5]. Thorpe used the same neuron model to examine the recognition, but with enlarged size of images [6]. Buonomano et al. have proposed a spiking neural model which can code the intensity of the input using relative firing time for the application of position invariant character recognition [7]. Presently, researchers in the field of character recognition systems showed a strong interest to find models which mimicking the biological nature neural networks. In other words, it would be useful to inspect for such systems (image recognition) the use of neuron models which are more identical to biological nature than integrate-and-fire model. Izhikevich stated that the integrate and fire model is far from mimicking the true biological nature of spiking neuron models. He notified that integrate-and-fire model cannot satisfy most essential features of cortical spiking neurons and thereby this model has to be avoided [8]. Izhikevich has set up a comparison analysis between 11 spiking neuron models in the aspects of biological accuracy and computational load [8]. He sorted the models according to their biological accuracy and showed that the first five most biologically accurate mod...

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