A review of learning in biologically plausible spiking neural networks

• Published in: Neural networks Vol. 122; pp. 253 - 272
• Main Authors: Taherkhani, Aboozar, Belatreche, Ammar, Li, Yuhua, Cosma, Georgina, Maguire, Liam P., McGinnity, T.M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2020
• Subjects: Action Potentials - physiology; Algorithms; Brain - physiology; Humans; Learning; Learning - physiology; Models, Neurological; Neural Networks, Computer; Neuronal Plasticity - physiology; Neurons - physiology; Spiking neural network (SNN); Synaptic plasticity; Learning; Spiking neural network (SNN); Synaptic plasticity
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2019.09.036

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.