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دسته بندی:
شبکه های نورونی - neuron-networks
سال انتشار:
2020
عنوان انگلیسی مقاله:
A review of learning in biologically plausible spiking neural networks
ترجمه فارسی عنوان مقاله:
مروری بر یادگیری در شبکه های عصبی اسپایک بیولوژیکی قابل قبول
منبع:
Sciencedirect - Elsevier - Neural Networks, 122 (2020) 253-272. doi:10.1016/j.neunet.2019.09.036
نویسنده:
Aboozar Taherkhani a,∗, Ammar Belatreche b, Yuhua Li c, Georgina Cosma d, Liam P. Maguire e, T.M. McGinnity e,f
چکیده انگلیسی:
Artificial neural networks have been used as a powerful processing tool in various areas such as
pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged
researchers to improve artificial neural networks by investigating the biological brain. Neurological
research has significantly progressed in recent years and continues to reveal new characteristics of
biological neurons. New technologies can now capture temporal changes in the internal activity of the
brain in more detail and help clarify the relationship between brain activity and the perception of a
given stimulus. This new knowledge has led to a new type of artificial neural network, the Spiking
Neural Network (SNN), that draws more faithfully on biological properties to provide higher processing
abilities. A review of recent developments in learning of spiking neurons is presented in this paper.
First the biological background of SNN learning algorithms is reviewed. The important elements of
a learning algorithm such as the neuron model, synaptic plasticity, information encoding and SNN
topologies are then presented. Then, a critical review of the state-of-the-art learning algorithms for
SNNs using single and multiple spikes is presented. Additionally, deep spiking neural networks are
reviewed, and challenges and opportunities in the SNN field are discussed.
Keywords: Spiking neural network (SNN) | Learning | Synaptic plasticity
قیمت: رایگان
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