2015
DOI: 10.1186/s13640-015-0059-4
|View full text |Cite
|
Sign up to set email alerts
|

Simplified spiking neural network architecture and STDP learning algorithm applied to image classification

Abstract: Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease of use in neural interface circuits and reduced computation complexity. SNN have been successfully used for image classification. They provide a model for the mammalian visual cortex, image segmentation and pattern recognition. Different spiking neuron mathematical models exist, but their computational complexity makes them ill-suited f… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
38
0

Year Published

2017
2017
2023
2023

Publication Types

Select...
4
2
2
2

Relationship

0
10

Authors

Journals

citations
Cited by 98 publications
(38 citation statements)
references
References 20 publications
0
38
0
Order By: Relevance
“…In the future, we will incorporate a detailed biophysical model of SNc along with dopamine synthesis pathway (Tello-Bravo, 2012), apoptosis pathway (Hong et al, 2012) and neural energy supply-consumption properties to the current model. The synaptic weights in the proposed model are not dynamic, we would like to include some type of learning principle by incorporating STDP type of learning rule in STN population which can show the long-term effect of DBS treatment (Ebert et al, 2014;Iakymchuk et al, 2015).…”
Section: Limitations and Future Workmentioning
confidence: 99%
“…In the future, we will incorporate a detailed biophysical model of SNc along with dopamine synthesis pathway (Tello-Bravo, 2012), apoptosis pathway (Hong et al, 2012) and neural energy supply-consumption properties to the current model. The synaptic weights in the proposed model are not dynamic, we would like to include some type of learning principle by incorporating STDP type of learning rule in STN population which can show the long-term effect of DBS treatment (Ebert et al, 2014;Iakymchuk et al, 2015).…”
Section: Limitations and Future Workmentioning
confidence: 99%
“…In the current form, the ensemble has a capacity to generalise and a limited capacity to process noisy, incomplete data, which has been previously observed in some SNN [29], [30]. However, in our experiments we used random noise and complete silencing of a proportion of input neurons, which are extreme cases of data corruption as there is no link between the corrupted values and the original data.…”
Section: Resultsmentioning
confidence: 99%
“…In order to do so, all neuronal types should be modeled as conductance-based models where calcium dynamics should be considered. The synaptic weights in the proposed model are not dynamic; we would like to incorporate learning principles such as spike time-dependent plasticity in the STN population, which can show the long-term effect of DBS treatment (Ebert et al, 2014;Iakymchuk et al, 2015). A more ambitious goal is to incorporate the present model into a large-scale model of basal ganglia (Muralidharan et al, 2018) in understanding the effect of therapeutics in terms of the behavioral response (Erdi et al, 2006).…”
Section: Limitations and Future Directionsmentioning
confidence: 99%