Reference time in SpikeProp

Sporea, Ioana; Grüning, André

doi:10.1109/ijcnn.2011.6033344

“…Without start cue, the firing delay of each occurring spike cannot be accurately determined. The significance of the start cue has been discussed at length in literatures [11,28]. The time of start cue is kept when the first presynaptic spike arrives at CD neuron.…”

Section: Output Of Coincidence Detection Networkmentioning

confidence: 99%

Coincidence Detection Using Spiking Neurons with Application to Face Recognition

Kamaruzaman

¹

,

Shafie

²

,

Mustafah

³

2015

Journal of Applied Mathematics

3

0

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We elucidate the practical implementation of Spiking Neural Network (SNN) as local ensembles of classifiers. Synaptic time constantτsis used as learning parameter in representing the variations learned from a set of training data at classifier level. This classifier uses coincidence detection (CD) strategy trained in supervised manner using a novel supervised learning method calledτsPrediction which adjusts the precise timing of output spikes towards the desired spike timing through iterative adaptation ofτs. This paper also discusses the approximation of spike timing in Spike Response Model (SRM) for the purpose of coincidence detection. This process significantly speeds up the whole process of learning and classification. Performance evaluations with face datasets such as AR, FERET, JAFFE, and CK+ datasets show that the proposed method delivers better face classification performance than the network trained with Supervised Synaptic-Time Dependent Plasticity (STDP). We also found that the proposed method delivers better classification accuracy thanknearest neighbor, ensembles ofkNN, and Support Vector Machines. Evaluation on several types of spike codings also reveals that latency coding delivers the best result for face classification as well as for classification of other multivariate datasets.

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“…In comparable settings, so far only classification tasks or simple mapping tasks have been considered [9,11,14,21], either with only a single neuron or in much larger Liquid State Machines (LSMs) [15], but no computational tasks. Or computational tasks like the Exclusive-Or problem have been considered in layered networks, but only with single-spike latency-encoded outputs [3,4,29,30]. In contrast, it is our aim to demonstrate that layered networks can learn to perform simple, but non-trivial computations in a supervised framework and make use of multiple timed spikes for input and output patterns.…”

Section: Introductionmentioning

confidence: 99%

Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

Grüning

¹

,

Sporea

²

2012

Neural Process Lett

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Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can be successfully applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons.We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons.While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understanding of how computations can be done with spike trains.

show abstract

“…In comparable settings, so far only classification tasks or simple mapping tasks have been considered [9,11,14,21], either with only a single neuron or in much larger Liquid State Machines (LSMs) [15], but no computational tasks. Or computational tasks like the Exclusive-Or problem have been considered in layered networks, but only with single-spike latency-encoded outputs [3,4,29,30]. In contrast, it is our aim to demonstrate that layered networks can learn to perform simple, but non-trivial computations in a supervised framework and make use of multiple timed spikes for input and output patterns.…”

Section: Introductionmentioning

confidence: 99%

Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

Grüning,

Sporea

2011

Preprint

Self Cite

0

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Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can be successfully applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons.We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons.While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understanding of how computations can be done with spike trains.

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Reference time in SpikeProp

Cited by 7 publications

References 10 publications

Coincidence Detection Using Spiking Neurons with Application to Face Recognition

Coincidence Detection Using Spiking Neurons with Application to Face Recognition

Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

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